High Speed Dim Air Target Detection Using Airborne Radar under Clutter and Jamming Effects

The challenging potential problems associated with using airborne radar in detection of high Speed Maneuvering Dim Target (HSMDT) are the highly noise, jamming and clutter effects. The problem is not only how to remove clutter and jamming as well as the range migration and Doppler ambiguity estimation problems due to high relative speed between the targets and airborne radar. Some of the recently published works ignored the range migration problems, while the others ignored the Doppler ambiguity estimation. In this paper a new hybrid technique using Optimum Space Time Adaptive Processing (OSTAP), Second Order Keystone Transform (SOKT), and the Improved Fractional Radon Transform (IFrRT) was proposed. The OSTAP was applied as anti-jamming and clutter rejection method, the SOKT corrects the range curvature and part of the range walk, then the IFrRT estimates the target’ radial acceleration and corrects the residual range walk. The simulation demonstrates the validity and effectiveness of the proposed technique, and its advantages over the previous researches by comparing its probability of detection with the traditional methods. The new approach increases the probability of detection, and also overcomes the limitation of Doppler frequency ambiguity

Wavelet-Based Compressive Sensing for Point Scatterers

Compressive Sensing (CS) allows for the sam-pling of signals at well below the Nyquist rate but does so, usually, at the cost of the suppression of lower amplitude sig-nal components. Recent work suggests that important infor-mation essential for recognizing targets in the radar context is contained in the side-lobes as well, which are often sup-pressed by CS. In this paper we extend existing techniques and introduce new techniques both for improving the accu-racy of CS reconstructions and for improving the separa-bility of scenes reconstructed using CS. We investigate the Discrete Wavelet Transform (DWT), and show how the use of the DWT as a representation basis may improve the accu-racy of reconstruction generally. Moreover, we introduce the concept of using multiple wavelet-based reconstructions of a scene, given only a single physical observation, to derive re-constructions that surpass even the best wavelet-based CS reconstructions. Lastly, we specifically consider the effect of the wavelet-based reconstruction on classification. This is done indirectly by comparing outputs of different algo-rithms using a variety of separability measures. We show that various wavelet-based CS reconstructions are substan-tially better than conventional CS approaches at inducing (or preserving) separability, and hence may be more useful in classification applications