Global warming has caused serious damage to our environment in recent years. Accelerated loss of ice from Greenland and Antarctica has been observed in recent decades. The melting of polar ice sheets and mountain glaciers has a considerable influence on sea level rise and altering ocean currents, potentially leading to the flooding of the coastal regions and putting millions of people around the world at risk. Synthetic aperture radar (SAR) systems are able to provide relevant information about subsurface structure of polar ice sheets. Manual layer identification is prohibitively tedious and expensive and is not practical for regular, longterm ice-sheet monitoring. Automatic layer finding in noisy radar images is quite challenging due to huge amount of noise, limited resolution and variations in ice layers and bedrock. Here we propose an approach which automatically detects ice surface and bedrock boundaries using distance regularized level set evolution. In this approach the complex topology of ice and bedrock boundary layers can be detected simultaneously by evolving an initial curve in radar imagery. Using a distance regularized term, the regularity of the level set function is intrinsically maintained that solves the reinitialization issues arising from conventional level set approaches. The results are evaluated on a large dataset of airborne radar imagery collected during IceBridge mission over Antarctica and Greenland and show promising results in respect to hand-labeled ground truth.

It has been demonstrated that the instantaneous acceleration associated with vibrating objects that are directly imaged by synthetic aperture radar (SAR) can be estimated through the application of the discrete fractional Fourier transform (DFrFT) using the information contained in the complex SAR image. In general, vibration signatures may include, for example, the number of chirped sinusoids as well as their respective base frequencies and chirp rates. By further processing the DFrFT-processed data for clutter-noise rejection by means of pseudo- subspace methods, has been shown that the SAR-vibrometry method can be reliable as long as the signal-to-noise ratio (SNR) and the signal-to-clutter ratio (SCR) of the slow-time SAR signal at the range-line of interest exceeds 15dB. Meanwhile, the Nyquist theorem dictates that the maximum measurable vibration frequency is limited by half of the pulse-repetition frequency. This paper focuses on the detection and estimation of vibrations generated by machinery concealed within buildings and other structures. This is a challenging task in general because the vibration signatures of the source are typically altered by their housing structure; moreover, the SNR at the surface of the housing structure tends to be reduced. Here, experimental results for three different vibrating targets, including one concealed target, are reported using complex SAR images acquired by the General Atomics Lynx radar at resolutions of 1-ft and 4-in. The concealed vibrating target is actuated by a gear motor with an off-balance weight attached to it, which is enclosed by a wooden housing. The vibrations of the motor are transmitted to a chimney that extends above the housing structure. Using the SAR vibrometry approach, it is shown that it is possible to distinguish among the three vibrating objects based upon their vibration signatures.

Passive Coherent Location (PCL) is a developing radar field, in which the system processes reflections from opportunistic illumination sources in the environment for detection and tracking. Many developments and improvements of PCL implement pseudo-monostatic and bistatic radar configurations; however, with the proliferation of commercial communication systems, the spectrally dense environment proves to be in favor of a multistatic PCL system. In the multistatic case, it can be shown that geolocation of a target is a unique extension of multilateration algorithms. In multilateration, the receiver location is unknown and calculated by exploiting the intersections of distances from receiver to transmitters; in multistatic PCL, an object location is unknown and the intersection of transmitter distance to receiver and reflection distance to receiver are utilized. Multilateration is a well-studied topic where there are many developed techniques that can be applied after the reflection distance is derived with ray tracing principles. The framework for a time-of-arrival based multistatic PCL is provided using time-difference-of-arrival (TDOA) as a measure of propagation time and path length. A nonlinear multilateration solver using a least means square algorithm calculates the geolocation of the object within a tolerable error. Using a frequency modulated (FM) test signal, simulated results show comparable results to a bistatic PCL system using angular measurements for geolocation. Furthermore, a multistatic multilateration system is more tolerant to timing errors compared to a bistatic system with angular inaccuracy. The developed multistatic PCL system is realized and verified using an FM signal.

Over the past decade, the radio frequency (RF) spectrum in which radar, telecommunication, navigational, and other systems operate has become increasingly crowded. The Army Research Laboratory has developed a Stepped-Frequency Radar (SFR) which can avoid areas of the RF spectrum in which systems are already operating. This allows for mitigation of RF interference (RFI) on the radar, as well as reduction in the interference from the radar that might affect other systems. This paper addresses the impact of frequency notching on GPR imagery, and methods of mitigating negative effects. The SFR operates over 300–2000 MHz, with a minimum frequency step size of 1 MHz. The radar transmits and receives in pulsed intervals, allowing for receiver blanking of close-in targets. The SFR is vehicle mounted and utilizes two dualpolarized transmit (Tx) horn antennas on either side of the receiver (Rx) antenna aperture, which consists of 16 Vivaldi notch antennas (which can be rotated to either H or V polarization). The radar is capable of completing a sweep over its entire operating band in <250 μsec. Furthermore, the radar is capable of coherently averaging directly on the field programmable gate array (FPGA) in which it digitizes the Rx channels, albeit at the cost of increased data collection time.

The evolution of exquisitely sensitive Synthetic Aperture Radar (SAR) systems is positioning this technology for use in time-critical environments, such as search-and-rescue missions and improvised explosive device (IED) detection. SAR systems should be playing a keystone role in the United States’ Intelligence, Surveillance, and Reconnaissance activities. Yet many in the SAR community see missed opportunities for incorporating SAR into existing remote sensing data collection and analysis challenges. Drawing on several years' of field research with SAR engineering and operational teams, this paper examines the human and organizational factors that mitigate against the adoption and use of SAR for tactical ISR and operational support. We suggest that SAR has a design problem, and that context-sensitive, human and organizational design frameworks are required if the community is to realize SAR's tactical potential.

The second generation AVTIS ground-based millimeter wave instruments designed for monitoring topography of volcanic lava domes are solid state 94 GHz FMCW rastered, real beam radars operating at ranges of up to ~7 km with a range resolution of ~2.5 m. Operating ten times faster than the prototype with reduced power consumption suitable for battery powered portable use as well as installation at a telemetered site under solar power, we examine their performance as tools for monitoring topography over time and report on the operational statistics both as a radar sensor and as a means of generating digital elevation maps.

We present the design and characterization of a 94 GHz pulsed coherent radar to be used for the evaluation and demonstration of novel wideband, high power vacuum tube amplifier technology. The radar is designed to be fully coherent and exploits a low phase noise architecture to maximize Doppler performance. We selected to use horn-fed Fresnel zone plate lens antennas (FZPs) with 4-level phase quantization as a low cost method of realizing large aperture (0.5 m) antennas. The measured performance of these FZPs agrees closely with the design predictions and exceeds that obtainable with a Cassegrain of an equivalent size.

The single aperture implementation of laser radars in combination with beam scanning solutions enables low cost, compact and efficient laser systems for 3D acquisition. The design benefits include the lack of dead zones, improved stability and compact footprint for the system implementation. In our presentation we focus on the scanning solution development for 3D laser radars that is based on all solid state magneto-optic design. The novel solid-state scanner implementation results are presented.

The FlexSAR radar system was designed to be a high quality, low-cost, flexible research prototype instrument. Radar researchers and practitioners often desire the ability to prototype new or advanced configurations, yet the ability to enhance or upgrade existing radar systems can be cost prohibitive. FlexSAR answers the need for a flexible radar system that can be extended easily, with minimal cost and time expenditures. The design approach focuses on reducing the resources required for developing and validating new advanced radar modalities. Such an approach fosters innovation and provides risk reduction since actual radar data can be collected in the appropriate mode, processed, and analyzed early in the development process. This allows for an accurate, detailed understanding of the corresponding trade space. This paper is a follow-on to last years paper and discusses the advancements that have been made to the FlexSAR system. The overall system architecture is discussed and presented along with several examples illustrating the system utility.

The U.S. Army Research Laboratory (ARL) has investigated the ultra-wideband (UWB) radar technology for detection of landmines, improvised explosive devices and unexploded ordnance, for over two decades. This paper presents a phenomenological study of the radar signature of buried landmines in realistic environments and the performance of airborne synthetic aperture radar (SAR) in detecting these targets as a function of multiple parameters: polarization, depression angle, soil type and burial depth. The investigation is based on advanced computer models developed at ARL. The analysis includes both the signature of the targets of interest and the clutter produced by rough surface ground. Based on our numerical simulations, we conclude that low depression angles and H-H polarization offer the highest target-to-clutter ratio in the SAR images and therefore the best radar performance of all the scenarios investigated.

We report results from the segmenting and study of terrain surface signatures of fully polarimetric multilook L-band and C-band SIR-C data. Entropy/alpha/anisotropy decomposition features are available from single multilook pixel data. This eliminates the need to average data from several pixels. Entropy and alpha are utilized in the segmentation along with features we have developed primarily from the eigenanalysis of the Kennaugh matrices of multilook data. We have previously reported on our algorithm for segmenting fully polarimetric single look TerraSAR-X, multilook SIR-C and 7 band Landsat 5 data featuring the iterative application of a feedforward neural network with one hidden layer. A comparison of signatures from simultaneously recorded data at L and C bands is presented. The terrain surfaces surveyed include the ocean, lakes, lake ice, bare ground, desert salt flats, lava beds, vegetation, sand dunes, rough desert surfaces, agricultural and urban areas.

This paper demonstrates a non-invasive method to determine the hydration level of human skin by measuring the reflectance of W-band (75-110 GHz) and Ka-band (26-40 GHz) radiation. Ka-band provides higher hydration accuracy (<1%) and greater depth of penetration (> 1 mm), thereby allowing access to the important dermis layer of skin. W-band provides less depth of penetration but finer spatial resolution (~2 mm). Both the hydration sensing concept and experimental results are presented here. The goal is to make a human hydration sensor that is 1% accurate or better, operable by mechanically scanning, and fast enough to measure large areas of the human body in seconds.

This paper addresses the detection and imaging of a small tumor underneath the inner surface of the human intestine. The proposed system consists of an around-body antenna array cooperating with a capsule carrying a radio frequency (RF) transmitter located within the human body. This paper presents a modified Levenberg-Marquardt algorithm to reconstruct the dielectric profile with this new system architecture. Each antenna around the body acts both as a transmitter and a receiver for the remaining array elements. In addition, each antenna also acts as a receiver for the capsule transmitter inside the body to collect additional data which cannot be obtained from the conventional system. In this paper, the synthetic data are collected from biological objects, which are simulated for the circular phantoms using CST studio software. For the imaging part, the Levenberg-Marquardt algorithm, which is a kind of Newton inversion method, is chosen to reconstruct the dielectric profile of the objects. The imaging process involves a two-part innovation. The first part is the use of a dual mesh method which builds a dense mesh grid around in the region around the transmitter and a coarse mesh for the remaining area. The second part is the modification of the Levenberg-Marquardt method to use the additional data collected from the inside transmitter. The results show that the new system with the new imaging algorithm can obtain high resolution images even for small tumors.

Noise radars have many advantages over conventional radars and receive great attentions recently. The performance of a noise radar is determined by its waveforms. Investigating characteristics of noise radar waveforms has significant value for evaluating noise radar performance. In this paper, we use binomial distribution theory to analyze general characteristics of binary phase coded (BPC) noise waveforms. Focusing on aperiodic autocorrelation function, we demonstrate that the probability distributions of sidelobes for a BPC noise waveform depend on the distances of these sidelobes to the mainlobe. The closer a sidelobe to the mainlobe, the higher the probability for this sidelobe to be a maximum sidelobe. We also develop Monte Carlo framework to explore the characteristics that are difficult to investigate analytically. Through Monte Carlo experiments, we reveal the Fractal relationship between the code length and the maximum sidelobe value for BPC waveforms, and propose using fractal dimension to measure noise waveform performance.

This work focuses on implementing a class of exactly solvable chaotic oscillators at speeds that allow real world radar applications. The implementation of a chaotic radar using a solvable system has many advantages due to the generation of aperiodic, random-like waveforms with an analytic representation. These advantages include high range resolution, no range ambiguity, and spread spectrum characteristics. These systems allow for optimal detection of a noise-like signal by the means of a linear matched filter using simple and inexpensive methods. This paper outlines the use of exactly solvable chaos in ranging systems, while addressing electronic design issues related to the frequency dependence of the system's stretching function introduced by the use of negative impedance converters (NICs).

The performance of different random array geometries is analyzed and compared. Three phased array geometries are considered: linear arrays with non-uniform randomized spacing between elements, circular arrays with non-uniform element radii, and ad hoc sensor networks with elements located randomly within a circular area. For each of these array geometries, computer simulations modeled the transmission, reflection from an arbitrary target, and reception of signals. The effectiveness of each array’s beamforming techniques was measured by taking the peak cross-correlation between the received signal and a time-delayed replica of the original transmitted signal. For each array type, the correlation performance was obtained for transmission and reception of both chirp waveforms and ultra-wideband noise signals. It was found that the non-uniform linear array generally produced the highest correlation between transmitted and reflected signals. The non-uniform circular and ad hoc arrays demonstrated the most consistent performance with respect to noise signal bandwidth. The effect of scan angle was found to have a significant impact on the correlation performance of the linear arrays, where the correlation performance declines as the scan angle moves away from broadside to the array.

The use of noise waveforms for radar has been popular for many years; however, not much work has been done to extend their use to long range applications. To understand the practicality of using noise for this work, the correlation values between transmitted and received signals were investigated as well as the ratio of reflected to transmitted power. This was done for both ground clutter and simple shapes representing targets of interest. Reflections from these different surfaces are dependent on the frequency of operation, polarization, angle of incidence, and target material. To act as a direct comparison to the noise waveform, a chirp signal was also reflected from these surfaces and correlated with the originally transmitted signal. For terrain, it was found that the noise offers similar correlation patterns as the chirp waveform but slightly larger reflected power for certain cases. Additionally, noise waveforms have decreased correlation values compared to chirp waveforms at low angles. For the simple shaped targets, the noise and chirp signals had similar correlation patterns, values, and power ratios.

Recently, dual-function radar-communications (DFRC) has been proposed as means to mitigate the spectrum congestion problem. Existing amplitude-shift keying (ASK) methods for information embedding do not take full advantage of the highest permissable sidelobe level. In this paper, a new ASK-based signaling strategy for enhancing the signal-to-noise ratio (SNR) at the communication receiver is proposed. The proposed method employs one reference waveform and simultaneously transmits a number of orthogonal waveforms equals to the number of 1's in the binary sequence being embedded. 3 dB SNR gain is achieved using the proposed method as compared to existing sidelobe ASK methods. The effectiveness of the proposed information embedding strategy is verified using simulations examples.

The essence of amplitude-modulation based dual-function radar-communications is to modulate the sidelobe of the transmit beampattern while keeping the main beam, where the radar function takes place, unchanged during the entire processing interval. The number of distinct sidelobe levels (SLL) required for information embedding grows exponentially with the number of bits being embedded. We propose a simple and computationally cheap method for transmit beampattern synthesis which requires designing and storing only two beamforming weight vectors. The proposed method first designs a principal transmit beamforming weight vector based on the requirements dictated by the radar function of the DFRC system. Then, a second weight vectors is obtained by enforcing a deep null towards the intended communication directions. Additional SLLs can be realized by simply taking weighted linear combinations of the two available weight vectors. The effectiveness of the proposed method for beampattern synthesis is verified using simulations examples.

The phase responses of nonlinear-radar targets illuminated by stepped frequencies are studied. Data is presented for an experimental radar and two commercial electronic targets at short standoff ranges. The amplitudes and phases of harmonics generated by each target at each frequency are captured over a 100-MHz-wide transmit band. As in the authors’ prior work, target detection is demonstrated by receiving at least one harmonic of at least one transmit frequency. In the present work, experiments confirm that the phase of a harmonic reflected from a radio-frequency electronic target at a standoff distance is linear versus frequency. Similar to traditional wideband radar, the change of the reflected phase with respect to frequency indicates the range to the nonlinear target.

The radar range equation for detecting targets using linear radar has been defined and derived many times for many different applications. The nonlinear radar range equation has been defined in the literature but a step by step derivation is lacking and no experimental validation has been shown. This paper starts with a nonlinear system model and provides simulated and experimental validation for the model. Once the model is validated, the model is used to derive the nonlinear radar range equation for nonlinear radar. Key differences between the linear and nonlinear radar range equation will be emphasized.

In many applications of radar systems, detection of targets in environments with heavy clutter and interference can be difficult. It is desired that a radar system should detect targets at a further range as well as be able to detect these targets with very few false positive or negative readings. In a cognitive radar system, there are ways that these negative effects can be mitigated and target detection can be significantly improved. An important metric to focus on for increasing target detectability is the signal-to-clutter ratio (SCR). Cognitive radar offers solutions to issues such as this with the use of a priori knowledge of targets and environments as well as real time adaptations. A feature of cognitive radar that is of interest is the ability to adapt and optimize transmitted waveforms to a given situation. A database is used to hold a priori and dynamic knowledge of the operational environment and targets to be detected, such as clutter characteristics and target radar cross-section (RCS) estimations. Assuming this knowledge is available or can be estimated in real-time, the transmitted waveform can be tailored using methods such as transmission of a spectrum corresponding to the target-to-clutter ratio (TCR). These methods provide significant improvement in distinguishing targets from clutter or interference.

The Spectral Analysis Solution (SAS), under development, is a multichannel superheterodyne signal analyzer with the intended applications of radio frequency (RF) research, radar verification, and general purpose spectrum sensing, primarily in the ultra-wideband (UWB) range from ultra high frequency (UHF) to the S-band. The SAS features a wideband channel operating from 100 kHz to 1.8 GHz and eight narrowband channels having adjustable instantaneous bandwidths ranging from 1 MHz to 100 MHz. The wideband channel provides a large picture of the RF spectrum while the narrowband channels allow for high resolution, low noise floor, and high spurious free dynamic range (SFDR) capabilities. An adaptive graphic user interface (GUI) has been implemented for the system that actively pulls and processes the system data in real time. This paper outlines the motivation and theory behind the system along with system validation and implementation results.

Since the invention of Synthetic Aperture Radar imaging in the 1950’s, users or potential users have sought to exploit SAR imagery for a variety of applications including the earth sciences and defense. At Sandia Laboratories, SAR Research and Development and associated defense applications grew out of the nuclear weapons program in the 1980’s and over the years has become a highly viable ISR sensor for a variety of tactical applications. Sandia SAR systems excel where real-­‐time, high-­‐resolution, all-­‐weather, day or night surveillance is required for developing situational awareness. This presentation will discuss the various aspects of Sandia’s airborne ISR capability with respect to issues related to current operational success as well as the future direction of the capability as Sandia seeks to improve the SAR capability it delivers into multiple mission scenarios. Issues discussed include fundamental radar capabilities, advanced exploitation techniques and human-­‐computer interface (HMI) challenges that are part of the advances required to maintain Sandia’s ability to continue to support ever changing and demanding mission challenges.

Today’s military radars are being challenged to satisfy multiple mission requirements and operate in complex, dynamic electromagnetic (EM) environments. They are simultaneously constrained by practical considerations like cost, size, weight and power (SWaP), and lifecycle requirements. Tomorrow’s radars need to be resilient to changing operating environments and capable of doing more with fewer resources. Radar research supports this shift toward more agile and efficient radar systems, and current trends include modular hardware and software development for multi-purpose, scalable radio frequency (RF) solutions. Software-defined radios (SDRs) and other commercial-off-the-shelf (COTS) technology are being used for flexible waveform generation, signal processing, and nontraditional radar applications. Adaptive RF technology, including apertures and other front-end components, are being developed for multi-purpose functionality and resiliency. Together, these research trends will result in a technology framework for more robust future systems that are capable of implementing cognitive processing techniques and adapting their behavior to meet the demands of a congested and contested EM environment.

The Pacific Northwest National Laboratory has developed three-dimensional holographic (synthetic aperture) radar imaging techniques and systems for a wide variety of near-field applications. These applications include radar crosssection (RCS) imaging, personnel screening, standoff concealed weapon detection, concealed threat detection, throughbarrier imaging, ground penetrating radar (GPR), and non-destructive evaluation (NDE). Sequentially-switched linear arrays are used for many of these systems to enable high-speed data acquisition and 3-D imaging. In this paper, the techniques and systems will be described along with imaging results that demonstrate the utility of near-field 3-D radar imaging for these compelling applications.

This paper analyzes the spectral features from human beings and indoor clutter for building and tuning Support Vector Machines (SVMs) classifiers for the purpose of classifying stationary human targets. The spectral characteristics were obtained through simulations using Finite Difference Time Domain (FDTD) techniques where the radar cross section (RCS) of humans and indoor clutter objects were captured over a wide range of frequencies, polarizations, aspect angles, and materials. Additionally, experimental data was obtained using a vector network analyzer. Two different feature sets for class discrimination are used from the acquired target and clutter RCS spectral data sets. The first feature vectors consist of the raw spectral characteristics, while the second set of feature vectors are statistical features extracted over a set frequency interval. Utilizing variables of frequency and polarization, a SVM classifier can be trained to classify unknown targets as a human or clutter. Classification accuracy over 80% can be effectively achieved given appropriate features.

MIMO radar provides improvement over traditional phased array radars for through wall imaging. By transmitting independent waveforms from a transmit array to a receive array an effective virtual array is created. This array has improved degrees of freedom over phased arrays and mono-static MIMO systems. This virtual array allows us to achieve the same effective aperture length as a phased array with less elements because the virtual array can be described as the convolution of transmit and receive array positions. In addition, data from multiple walls of the same room can be used to collect target information. If two walls are perpendicular to each other and the geometry of transmit and receive arrays is known, then data can be processed independently of each other. Since the geometry of the arrays is known, a target scene can be created where the two data sets overlap. The overlapped scene can then be processed so that image artifacts that do not correlate between the data sets can be excised. The result gives improved target detection, reduction in false alarms, robustness to noise, and robustness against errors such as improperly aligned antennas.

Synthetic aperture radar (SAR) images contain a grainy pattern, called speckle, that is a consequence of a coherent imaging system. For fine resolution SAR images speckle can obscure subtle features and reduce visual appeal. Many speckle reduction methods result in a loss of image resolution and reduce visual appeal which can obscure subtle features. Another approach to maintain resolution while reducing speckle is to register and combine multiple images. For persistent surveillance applications it is more efficient for an airborne platform to fly circles around the particular area of interest. In these cases, it would be beneficial to combine multiple circle mode SAR images, however the image registration process is not so straightforward because the layover angle changes in each image. This paper develops a SAR image registration process for combining multiple circle mode SAR images to reduce speckle while preserving resolution. The registration first uses a feature matching algorithm for a coarse rotation and alignment, and then uses a fine registration and warp. Ku band SAR data from a circle mode SAR collection is used to show the effectiveness of the registration and enhanced visual appeal from multi-looking.

A novel photonic beam-former concept is discussed within the context of traditional Radar design considerations. The beam-former concept has been proven for passive imaging applications¹; however, the work presented herein discusses extension of the theory to an active sensing problem space. The paper concentrates on discussion of photonic beamspace beam-forming and its relation to existing Beam-Space array theory and closes with several observations of the application of the photonic beam-space receiver to a simulated radar scenario. The initial results presented highlight the photonic beam-former’s ability to generate a full beam space for an arbitrary array size in real-time.

Matched Filter sidelobes from diversified LPI waveform design and sensor resolution are two important considerations in radars and active sensors in general. Matched Filter sidelobes can potentially mask weaker targets, and low sensor resolution not only causes a high margin of error but also limits sensing in target-rich environment/ sector. The improvement in those factors, in part, concern with the transmitted waveform and consequently pulse compression techniques. An adaptive pulse compression algorithm is hence desired that can mitigate the aforementioned limitations. A new Matched Filter based Iterative Adaptive Approach, MF-IAA, as an extension to traditional Iterative Adaptive Approach, IAA, has been developed. MF-IAA takes its input as the Matched Filter output. The motivation here is to facilitate implementation of Iterative Adaptive Approach without disrupting the processing chain of traditional Matched Filter. Similar to IAA, MF-IAA is a user parameter free, iterative, weighted least square based spectral identification algorithm. This work focuses on the implementation of MF-IAA. The feasibility of MF-IAA is studied using a realistic airborne radar simulator as well as actual measured airborne radar data. The performance of MF-IAA is measured with different test waveforms, and different Signal-to-Noise (SNR) levels. In addition, Range-Doppler super-resolution using MF-IAA is investigated. Sidelobe reduction as well as super-resolution enhancement is validated. The robustness of MF-IAA with respect to different LPI waveforms and SNR levels is also demonstrated.

This paper investigates the feasibility of real-time, multiple channel processing of a digital phased array system backend design, with focus on high-performance embedded computing (HPEC) platforms constructed based on general purpose digital signal processor (DSP). Serial RapidIO (SRIO) is used as inter-chip connection backend protocol to support the inter-core communications and parallelisms. Performance benchmark was obtained based on a SRIO system chassis and emulated configuration similar to a field scale demonstrator of Multi-functional Phased Array Radar (MPAR). An interesting aspect of this work is comparison between “raw and low-level” DSP processing and emerging tools that systematically take advantages of the parallelism and multi-core capability, such as OpenCL and OpenMP. Comparisons with other backend HPEC solutions, such as FPGA and GPU, are also provided through analysis and experiments.

This study introduces a practical approach to develop real-time signal processing chain for general phased array radar on NVIDIA GPUs(Graphical Processing Units) using CUDA (Compute Unified Device Architecture) libraries such as cuBlas and cuFFT, which are adopted from open source libraries and optimized for the NVIDIA GPUs. The processed results are rigorously verified against those from the CPUs. Performance benchmarked in computation time with various input data cube sizes are compared across GPUs and CPUs. Through the analysis, it will be demonstrated that GPGPUs (General Purpose GPU) real-time processing of the array radar data is possible with relatively low-cost commercial GPUs.

Carbon fiber composite materials have many useful structural material properties. The electromagnetic perfor- mance of these materials is of great interest for future applications. The work presented in this paper deals with the construction of Salisbury screen microwave absorbers made from unidirectional carbon fiber composite sand- wich structures. Specifically, absorbers centered at 7.25 GHz and 12.56 GHz are investigated. Circuit models are created to match the measured performance of the carbon fiber Salisbury screens using a genetic algorithm to extract lumped element circuit values. The screens presented in this paper utilize unidirectional carbon fiber sheets in place of the resistive sheet utilized in the classic Salisbury screen. The theory, models, prototypes, and measurements of these absorbers are discussed.

Since the inception of coherent waveforms, it has been realized that the effect of non-uniform motion of a non-point like object can induce structure in the return spectrum of the waveform that can be exploited. The non-uniform Doppler spectrum has useful information that can be found in the spreading of the Doppler spectrum for the motion models: acceleration, jerk, quadric, and exponential slowdown as examples well as a characteristic of periodic motion. We illustrate this with examples relevant to automotive radar, tracking meteors with ambient sources, characterizing moving sources and other relevant examples.

Detecting humans and classifying their activities on the water has significant applications for surveillance, border patrols, and rescue operations. When humans are illuminated by radar signal, they produce micro-Doppler signatures due to moving limbs. There has been a number of research into recognizing humans on land by their unique micro-Doppler signatures, but there is scant research into detecting humans on water. In this study, we investigate the micro-Doppler signatures of humans on water, including a swimming person, a swimming person pulling a floating object, and a rowing person in a small boat. The measured swimming styles were free stroke, backstroke, and breaststroke. Each activity was observed to have a unique micro-Doppler signature. Human activities were classified based on their micro-Doppler signatures. For the classification, we propose to apply deep convolutional neural networks (DCNN), a powerful deep learning technique. Rather than using conventional supervised learning that relies on handcrafted features, we present an alternative deep learning approach. We apply the DCNN, one of the most successful deep learning algorithms for image recognition, directly to a raw micro-Doppler spectrogram of humans on the water. Without extracting any explicit features from the micro-Dopplers, the DCNN can learn the necessary features and build classification boundaries using the training data. We show that the DCNN can achieve accuracy of more than 87.8% for activity classification using 5- fold cross validation.

Falls are a major cause of accidents in elderly people. Even simple falls can lead to severe injuries, and sometimes result in death. Doppler fall detection has drawn much attention in recent years. Micro-Doppler signatures play an important role for the Doppler-based radar systems. Numerous studies have demonstrated the offerings of micro-Doppler characteristics for fall detection. In this respect, a plethora of micro-Doppler signature features have been proposed, including those stemming from speech recognition and wavelet decomposition. In this work, we consider four different sets of features for fall detection. These can be categorized as spectrogram based features, wavelet based features, mel-frequency cepstrum coefficients, and power burst curve features. Support vector machine is employed as the classifier. Performance of the respective fall detectors is investigated using real data obtained with the same radar operating resources and under identical sensing conditions. For the considered data, the spectrogram based feature set is shown to provide superior fall detection performance.

Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

This paper presents the results of our experimental investigation into how different environments impact the classification of human motion using radar micro-Doppler (MD) signatures. The environments studied include free space, through-thewall, leaf tree foliage, and needle tree foliage. Results on presented on classification of the following three motions: crawling, walking, and jogging. The classification task was designed how to best separate these movements. The human motion data were acquired using a monostatic coherent Doppler radar operating in the C-band at 6.5 GHz from a total of six human subjects. The received signals were analyzed in the time-frequency domain using the Short-time Fourier Transform (STFT) which was used for feature extraction. Classification was performed using a Support Vector Machine (SVM) using a Radial Basis Function (RBF). Classification accuracies in the range 80-90% were achieved to separate the three movements mentioned.

We provide a common framework for the measurement problem for radar, sonar, and quantum mechanics by casting them in the common language of quantum mechanics as a Rigged Hilbert Space. This language reveals a more detailed understanding of the underlying interactions of a return signal that are not usually brought out by standard signal processing design techniques. It also provides a means to "post-select" the return signal so the receiver design for radars can be optimized for either a single or multiple operators. Thus, detector design can be optimized for signal interaction with objects, so the algorithm provides a solution to receiver design for general types of interactions.

Recent research suggests that quantum radar offers several potential advantages over classical sensing technologies. At present, the primary practical challenge is the fast and efficient generation of entangled microwave photons. To mitigate this limitation we propose and briefly examine a distributed architecture to synthetically increase the number of effectively-distinguishable modes.

In the context of traditional radar systems, the Doppler effect is crucial to detect and track moving targets in the presence of clutter. In the quantum radar context, however, most theoretical performance analyses to date have assumed static targets. In this paper we consider the Doppler effect at the single photon level. In particular, we describe how the Doppler effect produced by clutter and moving targets modifies the quantum distinguishability and the quantum radar error detection probability equations. Furthermore, we show that Doppler-based delayline cancelers can reduce the effects of clutter in the context of quantum radar, but only in the low-brightness regime. Thus, quantum radar may prove to be an important technology if the electronic battlefield requires stealthy tracking and detection of moving targets in the presence of clutter.

We present analysis of a radar detection technique using a Photon Addition Homodyne Receiver (PAHR) that improves SNR of the interferometer fringes and reduces uncertainty of the phase measurement. This system uses the concept of Photon Addition (PA) in which the coherent photon distribution is altered. We discuss this process first as a purely mathematical concept to introduce PA and illustrate its effect on coherent photon distribution. We then present a notional proof-of-concept experiment involving a parametric down converter (PDC) and probabilistic post-selection of the results. We end with presentation of a more deterministic PAHR concept that is more suitable for development into a working system. Coherent light illuminates a target and the return signal interferes with the local oscillator reference photons to create the desired fringes. The PAHR alters the photon probability distribution of the returned light via interaction between the return photons and atoms. We refer to this technique as “Atom Interaction” or AI. The returning photons are focused at the properly prepared atomic system. The injected atoms into this region are prepared in the desired quantum state. During the interaction time, the initial quantum state evolves in such a way that the photon distribution function changes resulting in higher photon count, lower phase noise and an increase in fringe SNR. The result is a 3-5X increase of fringe SNR. This method is best suited for low light intensity (low photon count, 0.1-5) applications. The detection protocol could extend the range of existing systems without loss of accuracy, or conversely enhance a system’s accuracy for given range. We present quantum mathematical analysis of the method to illustrate how both range and angular resolution improve in comparison with standard measurement techniques. We also suggest an experimental path to validate the method which also will lead toward deployment in the field.

A major scientific thrust from recent years has been to try to harness quantum phenomena to increase the performance of a wide variety of information processing devices. In particular, quantum radar has emerged as an intriguing theoretical concept that could revolutionize electromagnetic standoff sensing. In this paper we will discuss how the techniques developed for quantum radar could also be used towards the design of novel seismographs able to detect small ground vibrations., We use a hypothetical earthquake warning system in order to compare quantum seismography with traditional seismographic techniques.

It has been found that the quantum radar cross section (QRCS) equation can be written in terms of the Fourier transform of the surface atom distribution of the object. This paper uses this form to provide an analytical formulation of the quantum radar cross section by deriving closed form expressions for various geometries. These expressions are compared to the classical radar cross section (RCS) expressions and the quantum advantages are discerned from the differences in the equations. Multiphoton illumination is also briefly discussed.

The Radar Cross Section (RCS) is a crucial element for assessing target visibility and target characterization, and it depends not only on the target’s geometry but also on its composition. However, the calculation of the RCS is a challenging task due to the mathematical description of electromagnetic phenomena as well as the computational resources needed. In this paper, we will introduce two ideas for the use of quantum information processing techniques to calculate the RCS of dielectric targets. The first is to use toolboxes of quantum functions to determine the geometric component of the RCS. The second idea is to use quantum walks, expressed in terms of scattering processes, to model radar absorbing materials.

In this paper, we designed a new two-stage algorithm to formulate accurately the focusing image with the highly squinted synthetic aperture radar. First of all is the general model of the squinted synthetic aperture radar (SAR) focusing a fixed area. After the echo data collection, the first stage of the algorithm is to establish the coarse image by performing the conventional range cell migration correction and azimuth matched filtering. In the second stage, the azimuth-variant was corrected and the motion compensation was carried to achieve a better image. A simulation experiment was given in the end.

Antenna apertures are often parsed into subapertures for Direction of Arrival (DOA) measurements. However, when the overall aperture is tapered for sidelobe control, the locations of phase centers for the individual subapertures are shifted due to the local taper of individual subapertures. Furthermore, individual subaperture gains are also affected. These non-uniform perturbations complicate DOA calculations. Techniques are presented to calculate subaperture phase center locations, and algorithms are given for equalizing subapertures’ gains.

Synthetic Aperture Radar (SAR) images are often complex-valued to facilitate specific exploitation modes. Furthermore, these pixel values are typically represented with either real/imaginary (also known as I/Q) values, or as Magnitude/Phase values, with constituent components comprised of integers with limited number of bits. For clutter energy well below full-scale, Magnitude/Phase offers lower quantization noise than I/Q representation. Further improvement can be had with companding of the Magnitude value.

A sensor/payload operator for modern multi-sensor multi-mode Intelligence, Surveillance, and Reconnaissance (ISR) platforms is often confronted with a plethora of options in sensors and sensor modes. This often leads an over-worked operator to down-select to favorite sensors and modes; for example a justifiably favorite Full Motion Video (FMV) sensor at the expense of radar modes, even if radar modes can offer unique and advantageous information. At best, sensors might be used in a serial monogamous fashion with some cross-cueing. The challenge is then to increase the utilization of the radar modes in a manner attractive to the sensor/payload operator. We propose that this is best accomplished by combining sensor modes and displays into ‘super-modes’.

Coherent change detection (CCD) provides a way for analysts and detectors to find ephemeral features that would otherwise be invisible in traditional synthetic aperture radar (SAR) imagery. However, CCD can produce false alarms in regions of the image that have low SNR and high vegetation areas. The method proposed looks to eliminate these false alarm regions by creating a mask which can then be applied to change products. This is done by utilizing both the magnitude and coherence feature statistics of a scene. For each feature, the image is segmented into groups of similar pixels called superpixels. The method then utilizes a training phase to model each terrain that the user deems as capable of supporting change and statistically comparing superpixels in the image to the modeled terrain types. Finally, the method combines the features using probabilistic fusion to create a mask that a user can threshold and apply to a change product for human analysis or automatic feature detectors.

Last year, we presented the theory behind “instantaneous stepped-frequency, non-linear radar”. We demonstrated through simulation that certain devices (when interrogated by a multi-tone transmit signal) could be expected to produce a multi-tone output signal near harmonics of the transmitted tones. This hypothesized non-linear (multitone) response was then shown to be suitable for pulse compression via standard stepped-frequency processing techniques. At that time, however, we did not have measured data to support the theoretical and simulated results. We now present laboratory measurements confirming our initial hypotheses. We begin with a brief description of the experimental system, and then describe the data collection exercise. Finally, we present measured data demonstrating the accurate ranging of a non-linear target.

The results of model-based target decomposition are the main features used to discriminate urban and non-urban area in polarimetric synthetic aperture radar (PolSAR) application. Traditional urban-area extraction methods based on modelbased target decomposition usually misclassified ground-trunk structure as urban-area or misclassified rotated urbanarea as forest. This paper introduces another feature named orientation angle to improve urban-area extraction scheme for the accurate mapping in urban by PolSAR image. The proposed method takes randomness of orientation angle into account for restriction of urban area first and, subsequently, implements rotation angle to improve results that oriented urban areas are recognized as double-bounce objects from volume scattering. ESAR L-band PolSAR data of the Oberpfaffenhofen Test Site Area was used to validate the proposed algorithm.

The U.S. Army Research Laboratory (ARL) has recently upgraded the indoor, rail-mounted synthetic aperture radar (SAR) system, RailSAR, to enable collection of large amounts of low-frequency, ultrawideband (UWB) data. Our intent is to provide a research tool that is capable of emulating airborne SAR configuration and associated data collection geometries against surrogate explosive hazard threat deployments. By having such a capability, ARL’s facility will afford a more rapid response to the ever changing improvised characteristics associated with explosive hazards today and in the future. Therefore, upgrades to this RailSAR tool to improve functionality and performance are needed to meet the potential rapid response assessments to be carried out. The new, lighter RailSAR cart puts less strain on the radar positioning hardware and allows the system to move smoothly along a specified portion of the rail. In previous papers, we have presented co-polarized SAR data collected using the ARL RailSAR. Recently, however, researchers at ARL have leveraged this asset to collect polarimetric data against multiple targets. This paper presents the SAR imagery resulting from these experiments and documents characteristics of certain target signatures that should be of interest to developers of automatic target detection (ATD) algorithms.

In this letter, we develop and demonstrate a coherent distributed transmission system based on high-accuracy microwave ranging systems to perform distributed transmit beamforming for mobile platforms. The output of the high-accuracy microwave ranging systems is used to adjust the phase of a transmitter to maintain phase coherence when platforms are moved. Results from laboratory and outdoor experiments demonstrating successful performance are provided.