OPEN ACCESS
This paper proposes an extended version of the Maximum Likelihood Estimation Detector (MLED) that can operate in severe heterogeneous environment for slow moving target detection in ground clutter using space-time adaptive processing (STAP). Unlike the MLED, the extended version called StopP-Band APES does not suffer from the high Doppler resolution properties of the MLED leading to severe extra computational burden.Performances are illustrated on realistic synthetic data.
Extended Abstract
STAP performs two-dimensional space and time adaptive filtering where different space channels are combined at different times (Bidon S., 2011a), (Montécot M., et al., 2011). In context of radar signal processing, the aim of STAP is to remove ground clutter returns, in order to enhance slow moving target detection. Filter’s weights are adaptively estimated from training data in the neighborhood of the range cell of interest, called cell under test (CST). The estimation of these weights is always deducted, more or less directly, from an estimation of the covariance matrix of the received signal, which is the key quantity in the process of adaptation. Any implementation of STAP processing must remain absolutely consistent with the strategy of radar processing which purpose is to obtain a high probability of detection while maintaining a very low probability of false alarm. To achieve this goal, the selected STAP processing therefore ideally seeks to comply with the following steps:
(1) Selection of enough representative training data of the clutter in the CST,
(2) Construction of a STAP filter based on the training data, in order to eliminate the clutter in the CST, while preserving target signal,
(3) Detection by comparing the power level in the CST after filtering to a threshold estimated using secondary data (data after filtering in neighboring cells).
However when facing highly heterogeneous environments, it is very difficult to meet the conditions (1) and (2) above. The problem of selecting representative data can be indeed very complex on highly heterogeneous clutter, and in some configurations this problem may even be insoluble (no representative training data).On high-density target environments, it is the suppression of target signal by the STAP filter that is problematic (targets in the training data at Doppler frequencies close to the Doppler frequency of the CST cause the STAP filter to put a notch on that frequency).
The article presents an alternative approach to previous hard points, based on an extension of the MLED (Maximum Likelihood Estimation Detector), (Aboutanious and Mulgrew, 2005). The MLED itself is closely linked with the APES algorithm (Amplitude and Phase Estimation of a Sinusoid), (Stoica et al., 1999). Indeed, the first step in the construction of the MLED involves the APES processing. MLED is then derived via proper normalization for CFAR properties. That’s the reason for adopting the term Stop-Band APES for the proposed method in this paper.
The main advantage of Stop-Band APES, compared to the MLED, is to prevent high computationnal burden, because it does not suffer from Doppler hyperresolution properties. In the paper we demonstate the effectiveness of Stop-Band APES, in particular with the application on realistic data. The figures below illustrate the behaviour of the proposed method, compared to the MLED and the classical Doppler processing of the sum channel, in a range gate of interest. Data are collected with a side-looking antenna with four subarrays. Clutter is suppressed in the same efficient way with Stop-band APES and the MLED. However, one can note that the sharpness of the target peak is quite the same with classical Doppler processing and Stop-Band APES, while it is very most sharper for the MLED.
RÉSUMÉ
Cet article propose une version étendue du Maximum Likelihood Estimation Detector (MLED) particulièrement bien adaptée à la problématique de la détection des cibles lentes dans des environnements très fortement hétérogènes. Contrairement à la méthode MLED, cette nouvelle méthode appelée Stop-Band APES ne souffre pas de la haute résolution Doppler de la méthode MLED qui implique une forte charge de calcul. Les performances de cette nouvelle méthode sont illustrées sur des données synthétiques réalistes.
STAP, single data set detection algorithm, heterogeneous clutter, APES, MLED
MOTS-CLÉS
STAP, détection sur données primaires seules, fouillis hétérogène, APES, MLED
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