Cancellation of Matched Filter Sidelobes for LFM Radar using Regularized Orthogonal Matching Pursuit

Abstract

Major disadvantage of matched filter is with sidelobes as they might cause false alarms or may mask weak targets of radar echo. In applications like Urban sensing, Through the wall radar, Non destructive testing etc small objects of interest play crucial role which might be masked by sidelobes of matched filter or sometimes sidelobes may be mistreated as objects of interest that lead to false alarms. Many solutions have been proposed in the literature to suppress these sidelobe levels to some extent but not completely cancelled. In this paper, a new approach is utilized to completely cancel the sidelobes of matched filter using a greedy algorithm known as Regularized Orthogonal Matching Pursuit (ROMP). This algorithm is applied to the matched filter output that resulted in complete removal of sidelobes. Also SNR is improved. Even at very low SNRs targets are detected with high probability and hence the proposed approach is more robust to noise.

Design of matched filter for signal‐to‐noise ratio maximization to improve data rate

“…Detection is based on the threshold compared to the power or size of the output of MF. 11 The proposed matched training filter finds many applications in signal detection in white noise and linear channel conditions. In the proposed learning-based matched filter detection, classification, and improvement of the SNR are demonstrated using simulation results.…”

“…Phase or frequency‐modulated signals are required to remove side lobes and filter out certain signals. Detection is based on the threshold compared to the power or size of the output of MF 11 . The proposed matched training filter finds many applications in signal detection in white noise and linear channel conditions.…”