Abstract
Knowledge-aided space-time adaptive processing (KASTAP) using multiple coherent processing interval (CPI) radar data is described. The approach is based on forming earth-based clutter reflectivity maps to provide improved knowledge of clutter statistics in nonhomogeneous terrain environments. The maps are utilized to calculate predicted clutter covariance matrices as a function of range. Using a data set provided under the DARPA knowledge-aided sensor signal processing and expert reasoning (KASSPER) Program, predicted distributed clutter statistics are compared to measured statistics to verify the accuracy of the approach. Robust STAP weight vectors are calculated using a technique that combines covariance tapering, adaptive estimation of gain and phase corrections, knowledge-aided prewhitening, and eigenvalue rescaling. Techniques to suppress large discrete returns, expected in urban areas, are also described. Several performance metrics are presented, including signal-to-interference-plus-noise ratio (SINR) loss, target detections and false alarms, receiver operating characteristic (ROC) curves, and tracking performance. The results show more than an order of magnitude reduction in false alarm density when compared to standard STAP processing.
Highlights
The lack of training data in nonhomogeneous clutter environments can cause severe degradation in the performance of space-timeadaptive processing (STAP) algorithms
Note that with knowledge-aided processing the “clutter null” is significantly narrower in certain areas. This is due to improved knowledge of the local clutter statistics that is gained from the clutter reflectivity map
We have described in this paper knowledge-aided STAP processing using multilook ground moving target indication (GMTI) radar data
Summary
The lack of training data in nonhomogeneous clutter environments can cause severe degradation in the performance of space-timeadaptive processing (STAP) algorithms (see [1, 2] and references therein). Covariance estimation for STAP is usually performed by averaging the outer products of these return vectors with themselves over a number of training range gates from a single CPI. Undernulling may occur if the test range gate contains strong clutter due to, say, steeply sloped terrain, while the training window surrounding the test cell contains less severe clutter This may lead to an excessive number of false alarms or, if the threshold is increased to reduce false alarms, loss of target detections. Averaging outer products of complex returns from additional CPI data cubes to augment standard covariance estimation is not effective (and may cause STAP performance to degrade rather than improve).
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