In recent years there has been growing interest in Ad-hoc and Wireless Sensor Networks (WSNs) for a variety of indoor applications. Thus, recent developments in communications and RF technology have enabled system concept formulations and designs for low-cost radar systems using state-of-the-art software radio modules. Position-Adaptive radar concepts have been formulated and investigated at the Air Force Research Laboratory (AFRL) within the past few years. Adopting a position-adaptive approach to the design of distributed radar systems shows potential for the development of future radar systems that function under new and challenging environments that contain large clutter discretes and require co-functionality within multi-signal RF environments. In this paper, we present the simulation performance analysis on the application aspect. We apply Extremum Seeking Control (ESC) schemes by using the swarm seeking problem, where the goal is to design a control law for each individual sensor that can minimize the error metric by adapting the sensor positions in real-time based on cross-path loss exponents estimates between sensors, thereby minimizing the unknown estimation error. As a result we achieved source seeking and collision avoidance of the entire group of the sensor positions.

Optical communications between a dismounted warfighter and his weapon-mounted scope increases warfighting capability. Since optical communication requires line-of-sight, typical movements of a dismounted warfighter during military missions and their effect on line-of-sight were investigated. Video analysis demonstrates technological transition feasibility for this application and provides the asis for future testing standards.

This paper focuses on the performance of cochannel interference (CCI) which is the primary factor to limit the capacity of wireless communication systems. Several cellular network architectures have been proposed in the literature to reduce the cochannel interference, but none of them appears to effectively tackle this problem. Microzoning is the technique, where the cells are further divided into smaller zones. The advantage of this technique is that the cochannel interference in the cellular system is reduced because the cell maintains a particular coverage radius. The objective of this paper is to analyze the performance of cochannel interference on the forward channels of the proposed microzone based CDMA cellular systems operating with perfect power control in an effort to reduce the cochannel interference. Simulation results showed that the proposed technique can effectively minimize cochannel interference and the proposed architecture can be used for practical applications.

This paper presents a novel wireless sensor networking technique using ultrasonic signal as the carrier wave for binary data exchange. Using the properties of lamb wave propagation through metal substrates, the proposed network structure can be used for runtime transport of structural fault information to ultrasound access points. Primary applications of the proposed sensor networking technique will include conveying fault information on an aircraft wing or on a bridge to an ultrasonic access point using ultrasonic wave through the structure itself (i.e. wing or bridge). Once a fault event has been detected, a mechanical pulse is forwarded to the access node using shortest path multi-hop ultrasonic pulse routing. The advantages of mechanical waves over traditional radio transmission using pulses are the following: First, unlike radio frequency, surface acoustic waves are not detectable outside the medium, which increases the inherent security for sensitive environments in respect to tapping. Second, event detection can be represented by the injection of a single mechanical pulse at a specific temporal position, whereas radio messages usually take several bits. The contributions of this paper are: 1) Development of a transceiver for transmitting/receiving ultrasound pulses with a pulse loss rate below 2·10^-5 and false positive rate with an upper bound of 2·10^-4. 2) A novel one-hop distance estimation based on the properties of lamb wave propagation with an accuracy of above 80%. 3) Implementation of a wireless sensor network using mechanical wave propagation for event detection on a 2024 aluminum alloy commonly used for aircraft skin construction.

Typically maritime and military users in the High Frequency (HF) band have been allocated channels with a bandwidth of 3 kHz. This narrow bandwidth has had the undesired effect of limiting the maximum data rates achievable over HF. The need for higher data rates has motivated research into waveform design using wider bandwidth waveforms. Two approaches have emerged: wider contiguous bandwidth waveforms and multiple non-contiguous 3 kHz waveforms. This paper will explore the crest factor and power amplifier implications of both approaches.

Symbol spread OFDM technique has been introduced to improve diversity performance of the conventional OFDM system in the frequency selective fading channel, where, in this technique, every data symbol is mod- ulated using all OFDM subcarriers. Linear Minimum Mean Square Error (MMSE) equalizer is widely used in spread OFDM signal detection because of its low complexity compared to optimal equalizers such as Maximum Likelihood (ML).1 In this paper we introduce turbo equalization based receiver for detecting symbol spread OFDM signal in which MMSE equalizer and channel decoder exchange soft information in an iterative fashion. Bit Error Rate (BER) performance is investigated with both full and partial spread scenarios and also with and without channel decoding. Simulation results show improved performance especially at low SNR regime and when partially spread OFDM scenario is used.

In some scenarios of wireless communications, due to the fast change of channel information, it is very difficult to estimate the channel parameters in real time. This difficulty can be overcame by noncoherent communication techniques. In this paper, we propose a new class of unitary space-time codes for non-coherent wireless MIMO communications, aimed at improving the bit error rate performance and data speed of communication systems. This class of unitary space-time codes can be efficiently decoded using sphere decoder algorithms. A numerical approach is proposed for the optimization of signal constellation. Such coding techniques can be applied to the data transmission of wireless sensor systems.

Continuous Phase Modulation (CPM) schemes are advantageous for low-power radios. The constant envelope transmit signal is more efficient for both linear and non-linear amplifier architectures. A standard, coherent CPM receiver can take advantage of modulation memory and is more complex than a coherent Phase Shift Keyed receiver. But the CPM signal can be demodulated non-coherently and still take advantage of the trellis structure inherent in the modulation. With this complexity reduction, the CPM receiver is comparable in performance to a Phase Shift Keyed radio with the power utilization of a Frequency Shift-Keyed design. In this paper, we discuss two methods for increasing the modulation memory of the CPM signal. In the first method, the distribution of the transmitted symbol across multiple phase pulses is investigated and the bit error rate analyzed. In the next method we address the addition of convolutioncodes. In both cases the effects of the CPM memory to quasi-coherent demodulation is analyzed and discussed. The differences in complexity will be analyzed and the overall performance enhancements of several different modulation schemes will be illustrated. 1

In this paper a soft-input soft-output (SISO) QAM demodulator with robust performance on imperfect channel information are proposed for bit-interleaved OFDM systems. A full Bayesian approach is proposed to the channel estimation and demodulation problem. The frequency-selective fading channel impulse response and AWGN variance encountered in OFDM systems are jointly modeled as complex Gaussian-gamma random variables. The uncertainty of the channels are naturally encoded in the posterior distribution. Robust demodulators for known and unknown AWGN variance are derived basing on Bayesian posterior predictive distribution. It's performance combined with the bit-interleaved coded modulation (BICM) is demonstrated. And schemes with reduced complexity are also discussed. Simulation results show an improved BER (more than 0:5dB in most cases) comparing to that of the conventional demodulators ignorant of the channel estimation errors.

This paper compares linear and non-linear modulation schemes for both bandwidth-limited and power-limited scenarios. In particular, we discuss the various trade-offs in terms of spectral efficiency, signal-to-noise ratio requirements and transmission range when selecting a candidate modulation scheme. Examples of the trade-offs are shown for maximising transmission range in frequency non-selective fading channels.

Recent research has shown that acoustic waves can be used to transmit data and power through metallic barriers. In this paper, we extend this work to consider the case where the channel consists of multiple layers of different materials. In particular, a steel-water-steel type of interface i.e., a layer of water sandwiched between two steel walls, is investigated. A pair of 1 MHz resonant (25.4 mm diameter) piezoelectric transducers are co-axially aligned and mounted on the dry side of each steel wall to form the channel. This channel is acoustic-electric in nature and is modeled as cascade of layers and interfaces in MATLAB. Each layer (single material) and interface is interpreted as transmission line in the acoustic domain. Experimental channels are implemented and the measured channel characteristics are compared to those obtained using the model. The power transfer efficiency and channel capacity are determined using the measured channel data. To maximize the capacity and reduce interference, it is assumed that data transmission is performed using orthogonal frequency division multiplexing (OFDM). The width of water column is varied and its effect on the power transfer efficiency and data capacity are shown. Results indicate that a channel formed by two steel walls of 15.97 mm and 10.92 mm thickness separated by 88.3 mm water column is capable of supporting data rates of several megabits/sec and of transferring power with more than 30 percent efficiency.

A suit of low complexity signal processing algorithms are identified for the directional spectrum sensing and two-dimensional (2-D) spatio-temporal white space detection in cognitive radio systems. The concept of spectral white spaces in 2-D spatio-temporal frequency space is reviewed based on the specific spectral properties of 2-D spatio-temporal array signals. The proposed system contains an array processing stage, magnitude-fast-Fourier-transform (FFT) stage followed by an energy detection stage. The use of 2-D infinite impulse response (IIR) filters having beam-shaped passbands in the 2-D frequency space is identi_ed as a low complexity solution for the array processing stage for the directional enhancement of radio signals. A low complexity algorithm that delivers the magnitude FFT is described for the 16-point case and computational complexity is expressed in closed-form.

Radio frequency identification (RFID) is a rapidly developing wireless communication technology for electronically identifying, locating, and tracking products, assets, and personnel. RFID has become one of the most important means to construct real-time locating systems (RTLS) that track and identify the location of objects in real time using simple, inexpensive tags and readers. The applicability and usefulness of RTLS techniques depend on their achievable accuracy. In particular, when multilateration-based localization techniques are exploited, the achievable accuracy primarily relies on the precision of the range estimates between a reader and the tags. Such range information can be obtained by using the received signal strength indicator (RSSI) and/or the phase difference of arrival (PDOA). In both cases, however, the accuracy is significantly compromised when the operation environment is impaired. In particular, multipath propagation significantly affects the measurement accuracy of both RSSI and phase information. In addition, because RFID systems are typically operated in short distances, RSSI and phase measurements are also coupled with the reader and tag antenna patterns, making accurate RFID localization very complicated and challenging. In this paper, we develop new methods to localize RFID tags or readers by exploiting sparse signal recovery techniques. The proposed method allows the channel environment and antenna patterns to be taken into account and be properly compensated at a low computational cost. As such, the proposed technique yields superior performance in challenging operation environments with the above-mentioned impairments.

This paper proposes efficient target localization methods for a passive radar system using bistatic time-of-arrival (TOA) information measured at multiple synthetic array locations, where the position of these synthetic array locations is subject to random errors. Since maximum likelihood (ML) formulation of this target localization problem is a non-convex optimization problem, semi-definite relaxation (SDR)-based optimization methods in general do not provide satisfactory performance. As a result, approximated ML optimization problems are proposed and solved with SDR plus bisection methods. For the case without position errors, it is shown that the relaxation guarantees a rank-one solution. The optimization problem for the case with position errors involves only a relaxation of a scalar quadratic term. Simulation results show that the proposed algorithms outperform existing methods and provide mean square position error performance very close to the Cramer-Rao lower bound even for larger values of noise and position estimation errors.

A Ziva team has recently demonstrated a novel technique called Collaborative Beamfocusing Radios (COBRA) which enables an ad-hoc collection of distributed commercial off-the-shelf software defined radios to coherently align and beamform to a remote radio. COBRA promises to operate even in high multipath and non-line-of-sight environments as well as mobile applications without resorting to computationally expensive closed loop techniques that are currently unable to operate with significant movement. COBRA exploits two key technologies to achieve coherent beamforming. The first is Time Reversal (TR) which compensates for multipath and automatically discovers the optimal spatio-temporal matched filter to enable peak signal gains (up to 20 dB) and diffraction-limited focusing at the intended receiver in NLOS and severe multipath environments. The second is time-aligned buffering which enables TR to synchronize distributed transmitters into a collaborative array. This time alignment algorithm avoids causality violations through the use of reciprocal buffering. Preserving spatio-temporal reciprocity through the TR capture and retransmission process achieves coherent alignment across multiple radios at ~GHz carriers using only standard quartz-oscillators. COBRA has been demonstrated in the lab, aligning two off-the-shelf software defined radios over-the-air to an accuracy of better than 2 degrees of carrier alignment at 450 MHz. The COBRA algorithms are lightweight, with computation in 5 ms on a smartphone class microprocessor. COBRA also has low start-up latency, achieving high accuracy from a cold-start in 30 ms. The COBRA technique opens up a large number of new capabilities in communications, and electronic warfare including selective spatial jamming, geolocation and anti-geolocation.

In this study, the wireless communication data obtained after the earthquake are introduced to rapidly assess the earthquake disaster. Firstly, the wireless communication data including the real-time signaling data and the base station data are used to analyze the activities and the relationship of the mobile phones and the base stations. Based on the analysis results, five signaling parameters are selected and the Apriori algorithm is used to judge the damaged status of the stations. All the base stations within the affected area of the earthquake are divided into several categories according to the damage levels. Each category will produce a range of earthquake damage in the spatial domain within the affected area. Finally, when the earthquake disaster-stricken areas are located, the extent of the damage will be estimated. The Wenchuan earthquake, happened on May 12, 2008 in Sichuan Province of China reflects that the method discussed in the paper is feasible. The Wenchuan EQ also shows that the wireless communication data is very useful when we assess the damage soon after the disaster occurred, especially when there is no other way to get the field disaster information.

This paper presents a novel energy-efficient distributed self-organized pulse switching architecture with a cell based event localization for wireless sensor and actuator network applications. The key idea of this pulse switching architecture is to abstract a single pulse, as opposed to multi-bit packets, as the information exchange mechanism. Unlike multi-bit packet communication, the proposed pulse switching architecture is based on pulse communications where a node either transmits a pulse or keeps silent at every time unit. Specifically, an event can be coded as a single pulse in a specific time unit with respect to the global clock. Then the pulse is transported multi-hop while preserving the event’s localization information in the form of temporal pulse position representing its originating cell, destination cell and next-hop cell. The proposed distributed pulse switching is shown to be energy-efficient compared to traditional packet switching especially for binary event sensing and actuation applications. Binary event sensing and actuation with conventional packet transport can be prohibitively energy-inefficient due to the communication, processing, and buffering overheads of the large number of bits within a packet’s data, header, and preambles. This paper presents a joint MAC and Routing architecture for self-organized distributed pulse switching. Through simulation experiments, it is shown that pulse switching can be an effective distributed means for event based networking in wireless sensor and actuator networks, which can potentially replace the packet transport when the information to be transported is binary in nature.

The fast Fourier sampling (FFS) method is related to the new sampling paradigm, compressive sampling (CS). This paper explores the application of the FFS method in an ultra-wide band digital receiver. The aim of the study is to quickly detect sparsely distributed carrier frequencies in an ultra-wide frequency band using fewer digital sampled data when compared to ubiquitous methods, such as the fast Fourier transform (FFT). Study shows that the FFS method can be applied to ultra-wide band sparse radar signal detection using randomly selected data from conventional analog-todigital converter and has the added advantage that it can be implemented on DSP hardware using a short-length of FFT.

Single Input Multiple Output (SIMO) acoustic communication system using an adaptive spatial diversity combined with parallel Decision Feedback Equalizer (DFE) is presented in this document. The SIMO system operates at high frequencies with high data rate over a limited range (less than 200 m) in very shallow waters. The SIMO system consists of a single source transmitting Phase Shift Keying (PSK) messages modulated at 300 kHz and received by multiple receivers. In a first configuration, the symbols collected at each receiver are equalized using a decision feedback equalizer and combined using Maximum Ratio Combining (MRC). In a second configuration, the MRC outputs are used as decision symbols in the DFE. This second configuration is a form of turbo equalization: the process can be repeated over and over, leading to a better estimate of the received message as the number of iterations increases. The adaptive process of diversity is repeated until the best possible result is achieved or a predefined error criterion is met. Bit Error Rate (BER) and Signal-to-Noise-and-Interference Ratio (SNIR) are used as performance metrics of the acoustic channel. Experimental results using SIMO system with three, four or five receivers and pre-processed real recorded data demonstrate ability to improve the performance of the acoustic channel in challenging environments. Using received messages with non-zero BER, adaptive spatial diversity can achieve BER of 0% and increased SNIR of 3 dB with number of iterations depending on the number of receivers used.

This paper demonstrates methods to select and apply regularization to the linear least-squares model formulation of the radio tomographic imaging (RTI) problem. Typically, the RTI inverse problem of image reconstruction is ill-conditioned due to the extremely small singular values of the weight matrix which relates the link signal strengths to the voxel locations of the obstruction. Regularization is included to offset the non-invertible nature of the weight matrix by adding a regularization term such as the matrix approximation of derivatives in each dimension based on the difference operator. This operation yields a smooth least-squares solution for the measured data by suppressing the high energy or noise terms in the derivative of the image. Traditionally, a scalar weighting factor of the regularization matrix is identified by trial and error (adhoc) to yield the best fit of the solution to the data without either excessive smoothing or ringing oscillations at the boundaries of the obstruction. This paper proposes new scalar and vector regularization methods that are automatically computed based on the weight matrix. Evidence of the effectiveness of these methods compared to the preset scalar regularization method is presented for stationary and moving obstructions in an RTI wireless sensor network. The variation of the mean square reconstruction error as a function of the scalar regularization is calculated for known obstructions in the network. The vector regularization procedure based on selective updates to the singular values of the weight matrix attains the lowest mean square error.