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.
Highlights
In pulse radar, range resolution is inversely proportional to bandwidth
Major disadvantage with Matched Filter (MF) is that correlation function between transmitted and received signals produces very high sidelobes [1]
In a pulse compression radar long pulse will be transmitted via frequency or phase modulation or digitally via Costas, Barker or Frank codes [8] that results in a short pulse at the output of the Matched Filter receiver achieving high range resolution
Summary
Range resolution is inversely proportional to bandwidth. If width of the pulse is reduced bandwidth is increased but average transmission power decreases which limit the maximum unambiguous range of targets and resolution. A reduction of sidelobes can be accomplished by applying a window function This leads to loss in peak value i.e. SNR decreases and main lobe width increases which leads to loss in resolution [2]. As in [3] mismatched filters can be used where standard convex optimization algorithms are applied and Integrated Sidelobe Level is reduced by minimizing the l1-norm of the vector whose elements are the sidelobe energies. This approach is more computationally complex than the Matched Filter because filter weights need to be estimated for each range bin separately.
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