International audienceIn an airborne radar context, heterogeneous situations are a serious concern for space-time adaptive processing (STAP), where the required secondary training data have to be target free and homogeneous with the tested data. Consequently, the performance of these detectors is severely impacted when facing a heavily heterogeneous environment. Single data-set algorithms such as the maximum likelihood estimation detector (MLED) algorithm, based on the amplitude and phase estimation (APES) method, have proved their efficiency in overcoming this problem by only working on primary data. However, restricting the estimation domain solely to the primary data often implies an inaccurate estimation of thecovariance matrix. In this paper, we demonstrate that we can use reduced-rank STAP on the single data-set APES method to increase the performance of the STAP processing. We also introduce an algorithm that reduces the computational cost of the standard subspace-based algorithms based on eigenvalue decomposition. The results on realistic data show that reduced-rank methods outperform traditional single data-set methods in detection and in clutter rejection
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.
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