Detection and classification of landmine-like targets in a non-Gaussian noise environment
Many statistical signal processing approaches to target detection and classification assume the measurement is corrupted by independent, identically distributed (i.i.d.) white Gaussian noise. This common assumption often results in simpler, and less computationally intense, mathematical realizations for the processor. However, in many instances it is not clear if this assumptions regarding the statistics of the noise is valid. In this paper, the effects of assuming i.i.d. white Gaussian noise on the performance of likelihood ratio detectors and maximum likelihood classifiers implemented in a non-Gaussian noise environment are discussed. If the assumptions regarding the noise distribution are accurate, the resulting likelihood ratio detector and classifier are optimal. However, if those assumptions are inaccurate, performance may be degraded. We present simulation results illustrating the effects of mismatch between the assumed and actual noise distributions on detection and classification performance for likelihood ratio processors derived under several assumptions regarding the noise distribution. Specifically, target detection and classification utilizing electromagnetic induction (EMI) sensors is considered.
