Proc. SPIE. 5794, Detection and Remediation Technologies for Mines and Minelike Targets X
KEYWORDS: Target detection, Principal component analysis, Detection and tracking algorithms, Sensors, Receivers, Phased arrays, Ground penetrating radar, Land mines, General packet radio service, Data fusion
Adaptive methods are currently in use for GPR to detect shallow-buried targets in the presence of ground-bounce that may be orders of magnitude greater than the target response. One such method that has been used with noteworthy success to reduce the soil background has been called the Principal Components Algorithm (PCA) that applies the Mahalanobis distance measure, a quadratic form, to distinguish the energy in a target from the background energy. We discuss an alternative vector subspace discrimination method borrowed from adaptive spatial filtering that has been used successfully for suppressing Doppler-spread ionospheric clutter in a high frequency radar application [4,5]. This method first estimates the background vector subspace containing the clutter during a training phase and then projects the current response on the orthogonal complement of the estimated subspace to extract the desired signal during the search phase. Discrete clutter in the output response is reduced without attenuating the target signal by applying constraints derived from the estimated target eigenstructure, either measured or modeled. Mathematical details of the algorithm are presented in an appendix. Comparison of results of the PCA and vector subspace projection methods applied to a stepped-frequency GPR have shown the performance of the proposed algorithm to be better in terms of clutter suppression and principal component economy while also retaining the phase and amplitude of the signal samples to facilitate array processing useful for forward looking ground penetrating radar.