Multidimensional Digital Smoothing Filters for Target Detection
This work addresses target detection in surveillance systems, presenting an incremental improvement in filter design.
The paper tackled the problem of detecting dim targets in surveillance sensors by designing multidimensional digital filters to reduce clutter and interference, achieving verified performance through simulation.
Recursive, causal and non-causal, multidimensional digital filters, with infinite impulse responses and maximally flat magnitude and delay responses in the low-frequency region, are designed to negate correlated clutter and interference in the background and to accumulate power due to dim targets in the foreground of a surveillance sensor. Expressions relating mean impulse-response duration, frequency selectivity and group delay, to low-order linear-difference-equation coefficients are derived using discrete Laguerre polynomials and discounted least-squares regression, then verified through simulation.