Classical Space-time Adaptive Processing Research Articles

Adaptive Target Detection Techniques for OFDM-Based Passive Radar Exploiting Spatial Diversity

Recently, the benefit of using a channel-based detector (CHAD) with a passive radar exploiting orthogonal frequency division multiplexing waveforms radiated, e.g., by DVB-T transmitters of opportunity has been studied. When multiple antennas are available on receive, we state that CHAD can be seen as space–time array-processing performing on a particular coherent frequency datacube. Building on this new interpretation, we propose an improved version of CHAD in the form of fully dimensional space–time adaptive processing (STAP). Optimization of the signal-to-interference-plus-noise ratio is obtained combining a linearly constrained minimum variance space–time adaptive beamforming and a least squares spatial adaptive filtering. Unlike classical STAP approaches, no training data are used here and only one space–time sample matrix inversion is required. The computational load is then highly reduced allowing a practical deployment. Moreover, since no beamscan in space is performed, the knowledge of array characteristics is not required and the performance shall not be impacted by any calibration errors. Finally, all the target returns being located in a single 2D (range-Doppler) surveillance map, the detection process is then simplified. Results on experimental data show the interests of this new surveillance scheme: No need for a prior rejection of the dominant interference, systematic reduction of secondary lobes, and discrimination of slow moving targets.

Exploiting persymmetry for low-rank Space Time Adaptive Processing

Reducing the number of secondary data used to estimate the Covariance Matrix (CM) for Space Time Adaptive Processing (STAP) techniques is still an active research topic. Within this framework, the Low-Rank (LR) structure of the clutter is well-known and the corresponding LR STAP filters have been shown to exhibit a smaller Signal Interference plus Noise Ratio (SINR) loss than classical STAP filters, only 2r secondary data (where r is the clutter rank) instead of 2m (where m is the data size) are required to reach the classical 3dB SNR loss. By using other features of the radar system, other properties of the CM can be exploited to further reduce the number of secondary data; this is the case for active systems using a symmetrically spaced linear array with constant pulse repetition interval, which results in a persymmetric structure of the noise CM. In this context, we propose to combine this property of the CM and the LR structure of the clutter to perform CM estimation. In this paper, the resulting STAP filter is shown, both theoretically and experimentally, to exhibit good performance with fewer secondary data; 3dB SINR Loss is achieved with only r secondary data.

Deterministic Aided STAP for Target Detection in Heterogeneous Situations

Classical space-time adaptive processing (STAP) detectors are strongly limited when facing highly heterogeneous environments. Indeed, in this case, representative target free data are no longer available. Single dataset algorithms, such as the MLED algorithm, have proved their efficiency in overcoming this problem by only working on primary data. These methods are based on the APES algorithm which removes the useful signal from the covariance matrix. However, a small part of the clutter signal is also removed from the covariance matrix in this operation. Consequently, a degradation of clutter rejection performance is observed. We propose two algorithms that use deterministic aided STAP to overcome this issue of the single dataset APES method. The results on realistic simulated data and real data show that these methods outperform traditional single dataset methods in detection and in clutter rejection.

Impact of diverse polarisations on clutter statistics

The author addresses the impact of diverse polarisations on clutter statistics in the context of waveform diversity for multi-functional operation from a specific platform as well as for multiple sensing from multiple platforms. A key issue in this context is that of clutter mitigation via the use of diverse waveforms. Classical space–time adaptive processing (STAP) methods for radar target detection can be viewed in the context of a whiten and match filter. To this end, efficient waveforms that lend themselves for such processing are sought. The author specifically considers a statistical analysis of experimental data collected at low grazing angles to validate the fact that vertical transmit–vertical receive (VV) polarised data conform to Rayleigh scatter, whereas horizontal transmit–horizontal receive data do not. Consequently, VV data are suitable for whiten and match processing adopted in conventional radar STAP.

Knowledge-based space-time adaptive processing for airborne early warning radar

This paper describes an innovative concept for knowledge-based control of space-time adaptive processing (STAP) for airborne early warning radar. The knowledge-based approach holds potential for significant performance improvements over classical STAP processing in nonhomogeneous environments by taking advantage of a priori knowledge. Under this approach, knowledge-based control is used to direct pre-adaptive filtering, and to carefully select STAP algorithms, parameters, and secondary data cells.