Abstract
For statistic space-time adaptive processing (STAP), a critical issue is estimating the clutter covariance matrix (CCM). However, sufficient training samples are difficult to obtain that satisfy the independent and identically distributed (IID) condition. It is because of the realistic heterogeneous environment faced by airborne radar. Moreover, one should eliminate contaminated training samples before CCM estimation. Aiming at the problems of the computational complexity and susceptibility to the outlier of the traditional generalized inner product (GIP) method, a clutter subspace-based training sampling selecting method is proposed combined with specific distribution in the space-time plane of clutter spectrum. Theoretical analysis and simulation results verified the proposed method and indicate that the proposed method is easy to construct CCM and has lower computational complexity and sensitivity to outliers.
Highlights
Clutter Subspace Characteristics-Estimating the time-space clutter covariance matrix (ST-CCM) is one of the most critical issues in adaptive space-time statistical processing (STAP) [1,2,3,4]
It will happen that the mismatch of the weight vector calculation, the loss of the output signal to clutter noise ratio (SCNR), and decrease of the detection performance of low speed and weak moving targets
According to the Equations (28) and (29), the expression of the output SCNR loss His: The function of the adaptive model formed by the optimum STAP is F = w opt S
Summary
Estimating the time-space clutter covariance matrix (ST-CCM) is one of the most critical issues in adaptive space-time statistical processing (STAP) [1,2,3,4]. It is easy to lead to inappropriate sample screening computation For this reason, many researchers have investigated and improved the GIP and affect the sample’s selection performance. Such as GIP method was used together with prior knowledge always obtained invert the high-dimension matrix, which requires a large amount of computation For this from actual [43]. This paper aims firstly to solve the shortcomings of the traditional method and model with a side-looking uniform linear array (ULA) for airborne radar This improve paperAs analyses of outliers in the samples on the performance of a result,the thisinfluence paper firstly established thetraining echo model and moving target detection. Method is easy to build the GIP matrix and requires less computing It is theoretical analysis and simulation experiments show that the proposed sensitive toeasy outliers, making possible select training samples efficiently. It is sensitive to outliers, making it possible to select training samples efficiently
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