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RSS FeedsRemote Sensing, Vol. 13, Pages 4212: Multi-Resolution STAP for Enhanced Ultra-Low-Altitude Target Detection (Remote Sensing)


20 october 2021 17:10:34

Remote Sensing, Vol. 13, Pages 4212: Multi-Resolution STAP for Enhanced Ultra-Low-Altitude Target Detection (Remote Sensing)

In this paper, an ultra-low-altitude target (ULAT) detection approach, referred to as the multi-resolution space-time adaptive processing (STAP), is proposed to enhance the target detection performance in a missile-borne radar system. In this respect, the whole base band is divided into a series of equal-width and center-frequency-diverse sub-bands with the frequency diversity technique, which enhances the multipath-target coupled (MTC) effect with the decreased range resolution. Hence, it is feasible to exploit the multipath signal power to improve the output signal-to-clutter-plus-noise ratio (SCNR) performance of sub-band STAP. In this regard, the mechanism of the MTC effect is analyzed numerically for the efficient sub-band STAP. However, such SCNR improvement is achieved at the cost of target tracking performance loss. Hence, the full-band STAP is further applied for multipath-target separation based on the target range-Doppler locations detected by the joint multiple sub-bands ΣΔ-STAP, which also alleviates the dynamic target attenuation and the corresponding target Doppler history corruption within the long coherent processing interval (CPI). On this basis, the SCNR performance is further improved by applying coherent accumulation among sub-CPIs, in which the clutter suppression performance degradation and coherent accumulation loss of STAP are alleviated within the sub-CPIs. Numerical and measured results corroborate the effectiveness of ULAT detection with the considered multi-resolution STAP.

55 viewsCategory: Geology, Physics
Remote Sensing, Vol. 13, Pages 4210: A High-Resolution, Random Forest Approach to Mapping Depth-to-Bedrock across Shallow Overburden and Post-Glacial Terrain (Remote Sensing)
Remote Sensing, Vol. 13, Pages 4213: Identifying Damaged Buildings in Aerial Images Using the Object Detection Method (Remote Sensing)
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