Kernel Design Meets Clutter Cancellation for Irregular Waveforms

TL;DR

This paper introduces a kernel-regularized joint domain filtering method for irregular waveform radar clutter cancellation, improving efficiency and performance over existing NIMPC techniques.

Contribution

It extends the NIMPC filter to irregular waveforms with kernel regularization and develops a fast algorithm for practical implementation.

Findings

Kernelized filtering outperforms NIMPC in clutter suppression.

Regularization addresses ill-conditioning of the matrix inverse.

Efficient algorithm based on FFT and conjugate gradient methods.

Abstract

Efficient clutter filtering for pulsed radar systems remains an open issue when employing pulse-to-pulse modulation and irregular pulse interval waveforms within the coherent processing interval. The range and Doppler domain should be jointly processed for effective filtering leading to a large computational overhead. In this paper, the joint domain filtering is performed by constructing a clutter projection matrix, also known as the projected non-identical multiple pulse compression (NIMPC) method. The paper extends the projected NIMPC filter to irregular pulse interval waveforms. Additionally, a kernel-based regularization will be introduced to tackle the ill-conditioning of the matrix inverse of the NIMPC method. The regularization is based on a model of the second-order statistics of the clutter. Moreover, a computationally efficient algorithm is formulated based on fast Fourier transforms and the projected conjugate gradient method. Through a Monte Carlo study it is demonstrated that the proposed kernelized filtering outperforms the projected NIMPC in clutter filtering.