Abstract
The multichannel synthetic aperture radar ground moving target indication (SAR/GMTI) technique is a simplified implementation of space-time adaptive processing (STAP), which has been proved to be feasible in the past decades. However, its detection performance will be degraded in heterogeneous environments due to the rapidly varying clutter characteristics. Knowledge-aided (KA) STAP provides an effective way to deal with the nonstationary problem in real-world clutter environment. Based on the KA STAP methods, this paper proposes a KA algorithm for adaptive SAR/GMTI processing in heterogeneous environments. It reduces sample support by its fast convergence properties and shows robust to non-stationary clutter distribution relative to the traditional adaptive SAR/GMTI scheme. Experimental clutter suppression results are employed to verify the virtue of this algorithm.
Highlights
Synthetic aperture radar (SAR) is an important sensor that can reconstruct the reflectivity image of the ground stationary scene
To identify the precise position of the road where 5 control targets were moving, several corner reflectors had been arranged along the road before flight, which can be found in the central part of the SAR image. 5 control targets (T1∼T5) with varied radial velocities show different azimuth displacements in the image, which are marked by the white arrows
With SAR image generated from each receiving channel, the KA and local training sample matrix inversion (SMI) algorithm were applied for clutter suppression, respectively
Summary
Synthetic aperture radar (SAR) is an important sensor that can reconstruct the reflectivity image of the ground stationary scene. 12 samples 24 samples edge is introduce to construct the weight vectors, and the knowledge-aided adaptive SAR/GMTI algorithm is proposed to enhance the detection performance in heterogeneous environments.
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