Abstract
Space-time adaptive processing (STAP) is an important airborne radar technique used to improve target detection in clutter-limited environments. Effective STAP implementation is dependent on accurate space-time covariance matrix estimation. Heterogeneous clutter, including spiky, spatial clutter variation, violates underlying STAP training assumptions and can significantly degrade corresponding detection performance. This paper develops a spiky, space-time clutter model based on the K-distribution, assesses the resulting impact on STAP performance using traditional methods, and then proposes and evaluates the utility of the knowledge-aided parametric covariance matrix estimation (KAPE) method, a model-based scheme that rapidly converges to better represent spatial variation in clutter properties. Via numerical simulation of an airborne radar scenario operating in a spiky clutter environment, we find substantial improvement in probability of detection ( P D ) for a fixed probability of false alarm ( P F A ) for the KAPE method. For example, in the spiky clutter environment considered herein, results indicate a P D of 32% for traditional STAP and in excess of 90% for KAPE at a P F A of 1E-4, with a corresponding difference of 11.5 dB in threshold observed from exceedance analysis. The proposed K-distributed spiky clutter model, and application and assessment of KAPE as an ameliorating STAP technique, contribute to an improved understanding of radar detection in complex clutter environments.
Highlights
Space-time adaptive processing (STAP) is a technique used in airborne radar to significantly improve detection performance by suppressing strong clutter returns emanating from stationary scatterers on the Earth’s surface [1,2]
STAP dynamically adjusts its response by estimating the null-hypothesis covariance matrix on a coherent processing interval (CPI) basis
We demonstrate the efficacy of knowledge-aided parametric covariance estimation (KAPE) [11,12] in estimating covariance matrices of K-distributed clutter generated with the cell-based clutter model (CCM)
Summary
Space-time adaptive processing (STAP) is a technique used in airborne radar to significantly improve detection performance by suppressing strong clutter returns emanating from stationary scatterers on the Earth’s surface [1,2]. The formulation leading to Equation (1) assumes that each space-time snapshot in the training data set is independent and identically distributed (iid) with respect to the null-hypothesis condition of the cell under test (CUT). Heterogeneous clutter is commonly encountered in airborne radar STAP scenarios, violating the iid condition, leading to covariance matrix estimation error and consequent loss in detection performance [4]. Several detectors have been proposed to combat K-distributed clutter, including general log-likelihood ratio tests [9,10] These algorithms presume that the background clutter is perfectly parameterized by the K-distribution, an impractical assumption for real data.
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