Abstract
The post-Doppler adaptive matched filter (PD-AMF) with constant false alarm rate (CFAR) property was developed for adaptive detection of moving targets, which is a standardized version of the post-Doppler space–time adaptive processing (PD-STAP) in practical applications. However, its detection performance is severely constrained by the training data, especially in a dense signal environment. Improper training data and contamination of moving target signals remarkably degrade the performance of disturbance suppression and result in target cancellation by self-whitening. To address these issues, a novel post-Doppler parametric adaptive matched filter (PD-PAMF) detector is proposed in the range-Doppler domain. Specifically, the detector is introduced via the post-Doppler matched filter (PD-MF) and the lower-diagonal-upper (LDU) decomposition of the disturbance covariance matrix, and the disturbance signals of the spatial sequence are modelled as an auto-regressive (AR) process for filtering. The purpose of detecting ground moving targets as well as for estimating their geographical positions and line-of-sight velocities is achieved when the disturbance is suppressed. The PD-PAMF is able to reach higher performances by using only a smaller training data size. More importantly, it is tolerant to moving target signals contained in the training data. The PD-PAMF also has a lower computational complexity. Numerical results are presented to demonstrate the effectiveness of the proposed detector.
Highlights
Multichannel adaptive processing for airborne radar applications offers a powerful approach for signal detection in a background of correlated clutter plus additive white noise
This paper proposes a novel post-Doppler parametric adaptive matched filter (PD-parametric AMF (PAMF)) detector that operates in the range-Doppler domain
Based on the post-Doppler matched filter (PD-MF) and the LDU decomposition of the disturbance covariance matrix, the theory of the PD-PAMF and its mathematical framework are introduced and the versatility of the PD-PAMF is improved by modifying the conventional model of multichannel signal
Summary
Multichannel adaptive processing for airborne radar applications offers a powerful approach for signal detection in a background of correlated clutter plus additive white noise. The parametric STAP detector has been developed by the AR process, such as in parametric AMF (PAMF) [12], parametric Rao detector [13], and parametric generalized likelihood ratio test (PGLRT) [14,15] They all operate in the space–time domain, and their computational burden increases significantly when the radar has a long CPI and a medium or large array. The DCM inversion of most STAP detectors increase the computational burden To address these issues, this paper proposes a novel post-Doppler parametric adaptive matching filter (PD-PAMF) detector for moving targets in the range-Doppler domain.
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