Knowledge-based recursive least squares techniques for heterogeneous clutter suppression

Abstract

In this paper we deal with the design of Knowledge-Based adaptive algorithms for the cancellation of heterogeneous clutter. To this end we revisit the application of the Recursive Least Squares (RLS) technique for the rejection of unwanted clutter and devise modified RLS filtering procedure accounting for the spatial variation of the clutter power. Then we introduce the concept of Knowledge-Based RLS and explain how the a-priori knowledge about the radar operating environment can be adopted for improving the system performance. Finally we assess the benefits resulting from the use Knowledge-Based processing both on simulated and on measured clutter data collected by the McMaster IPIX radar in November 1993.

Non‐parametric detector in non‐homogeneous clutter environments with knowledge‐aided permutation test

“…For the detection problem, the KA Bayesian detection used in heterogeneous environments was discussed in [7], and the KA adaptive detection in compound Gaussian noise was studied in [8]. Moreover, several studies in the literatures [9–14] improved some well‐known detection algorithms with different prior information. For example, Conte et al .…”

“…[9] discussed the combination of the priori geographic‐map‐based data and adaptive radar detection for Doppler processing. The GIS was used to improve the detection performance of the cell averaging (CA) CFAR [10] and the clutter suppression performance of the recursive least square method [11] under non‐homogeneous clutter environments. The KA space time adaptive processing was also studied in [12, 13], and a KA Bayesian detector was proposed in [14] based on the generalised likelihood ratio test criterion with a small training set.…”