- Front Matter: Volume 8709
- Electromagnetic Induction I
- Electromagnetic Induction II
- Electromagnetic Induction III
- Sonar Processing and ATR
- Man Portable Systems
- Explosive Detection I
- Explosive Detection II
- A Melange of Interesting Techniques
- Radar I
- Radar II
- Infrared and Electro-Optics
- Signal Processing: IR
- Signal Processing: EM Sensors
- Signal Processing for GPR I
- Signal Processing for GPR II
The developments of optical methods to characterize soils and various surface contaminants require complete and reliable databases of spectral signatures of various objects, including chemical and representative background surfaces. Ideally, the databases should be acquired in the field to properly consider the chemical mixing and heterogeneity of the surfaces. Spectral characterization instruments are common in the visible and the shortwave infrared but there are few solutions in the midwave and thermal infrared regions.
ABB recently developed a broad band reflectometer based on a small FTIR spectrometer. It is capable of measuring diffuse spectral reflectance from various surfaces in the infrared from 0.7 to 13.5 microns. This sensor has been developed to be operated in the field by one person. It is lightweight (about 12 kg); it is battery powered and ruggedized for operation in harsh environments. Its operation does not require sophisticated training; it has been designed to be operated by a non-specialist. The sensor can be used to generate spectral libraries or to perform material identification if a spectral library already exists.
Examples of measurements in the field will be presented.
Prior to the calculation of target detection features, ground penetrating radar (GPR) data typically requires extensive preprocessing to suppress clutter artifacts and enhance signals corresponding to weaker targets. Optimization of this GPR signal preprocessing pipeline is necessary to provide the best opportunity at visual detection and automatic target recognition. Manual, independent adjustment of the many configuration parameters in the data preprocessing pipeline is inefficient and not guaranteed to find an optimal result. In this paper, the authors present a new metric for GPR processed data quality and demonstrate its utility in an automated parameter sweep optimization of a large set of algorithm configuration parameters. The observed costs and benefits of using automated preprocessing optimization are presented and discussed.
For preprocessing optimization and evaluation, a cost function was desired that is independent of the target detection features – to enable independent evaluation of the various components of the GPR target detection software. The proposed cost function, JSUM, is a signal-to-clutter ratio (SCR) metric, derived from the known KSUM metric. JSUM was developed to be sensitive to a particular type of noise in GPR data not captured by KSUM. The response of JSUM and KSUM to different common types of noise was explored to qualify the usefulness of the metric.
JSUM was used as the cost function for a parameter sweep optimization across a set of preprocessing parameters. The outcomes of this optimization are presented for discussion.