A Novel Post-Doppler Parametric Adaptive Matched Filter for Airborne Multichannel Radar

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.

Ground Moving Target Imaging via SDAP-ISAR Processing: Review and New Trends

“…On the other hand, in a heterogeneous clutter environment, the clutter statistics are range-dependent and, therefore, the selected training data may have a different characteristic with respect to that of the area under test. Improper training data selection and the presence of non-stationary interference have been addressed, respectively, in [28] and [29], where a post-Doppler parametric adaptive matched filter and STAP based on piecewise sub-apertures have been proposed as solutions. Clutter range dependence, which involves a strong heterogeneity in the training data, is also present in forward-looking airborne SAR.…”

Heterogeneous Clutter Suppression for Airborne Radar STAP Based on Matrix Manifolds

“…Conversely, sub-optimal algorithms that require fewer samples have been devised. For instance, reduced-dimension [16,17] (e.g., loaded sample matrix inversion (LSMI) [6], 3DT [17]) and reduced-rank algorithms [18,19] (e.g., methods exploiting structural information) were proposed to diminish the demand for samples. These algorithms rely on specific assumptions about the data's statistical properties and suffer from performance loss [9].…”