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This paper proposes an effective anomaly detection algorithm for a forward-looking ground-penetrating radar(FLGPR). One challenge for threat detection using FLGPR is its high dynamic range in response to different kinds of targets and clutter objects. The application of a fixed threshold for detection in a full-band radar image often yields a large number of false alarms. We propose a method that uses both narrow-band and full-band radar processing, coupled with a classifier that uses complex-valued Gabor filter responses as the features. We then fuse the narrow-band and fullband images into a composite confidence map and detect local maxima in this map to produce candidate alarm locations. Full-band radar images provide a high degree of image resolution, while narrow-band images provide a means to detect targets which have a unique narrow-band signature. Experimental results for our improved detection techniques are demonstrated on data sets collected at a US Army test site.

Buried explosives have proven to be a challenging problem for which ground penetrating radar(GPR) has shown to be effective. This paper discusses an explosive hazard detection algorithm for forward lookingGPR (FLGPR). The proposed algorithm uses the fast Fourier transform(FFT) to obtain spectral features of anomalies in the FLGPR imagery. Results show that the spectral characteristics of explosive hazards differ from that of background clutter and are useful for rejecting false alarms (FAs). A genetic algorithm(GA) is developed in order to select a subset of spectral features to produce a more generalized classifier. Furthermore, a GA-based K-Nearest Neighbor probability density estimator is employed in which targets and false alarms are used as training data to produce a two-class classifier. The experimental results of this paper use data collected by the US Army and show the effectiveness of spectrum based features in the detection of explosive hazards.

Synthetic aperture image reconstruction applied to outdoor acoustic recordings is presented. Acoustic imaging is an alternate method having several military relevant advantages such as being immune to RF jamming, superior spatial resolution, capable of standoff side and forward-looking scanning, and relatively low cost, weight and size when compared to 0.5 - 3 GHz ground penetrating radar technologies. Synthetic aperture acoustic imaging is similar to synthetic aperture radar, but more akin to synthetic aperture sonar technologies owing to the nature of longitudinal or compressive wave propagation in the surrounding acoustic medium. The system's transceiver is a quasi mono-static microphone and audio speaker pair mounted on a rail 5meters in length. Received data sampling rate is 80 kHz with a 2- 15 kHz Linear Frequency Modulated (LFM) chirp, with a pulse repetition frequency (PRF) of 10 Hz and an inter-pulse period (IPP) of 50 milliseconds. Targets are positioned within the acoustic scene at slant range of two to ten meters on grass, dirt or gravel surfaces, and with and without intervening metallic chain link fencing. Acoustic image reconstruction results in means for literal interpretation and quantifiable analyses. A rudimentary technique characterizes acoustic scatter at the ground surfaces. Targets within the acoustic scene are first digitally spotlighted and further processed, providing frequency and aspect angle dependent signature information.