Enhance LFMCW radar detection and complexity using adaptive recovery CAMP algorithm - Hossiny, M. H.; Salem, Sameh G.; Ahmed, Fathy M.; Moustafa, K. H. (2018 First International Workshop on Deep and Representation Learning (IWDRL), 2018)
This paper presents the application of Compressive Sensing (CS) theory in radar signal processing. CS uses the sparsity property to reduce the number of measurements needed for digital acquisition, which causes reduction in the size, weight, power consumption, and the cost of the CS radar receiver. A well-known CAMP algorithm was used to reconstruct the compressed sparse LFMCW radar signal and improves its Signal-to-Noise Ratio. An Adaptive recovery CAMP algorithm, which had been proposed to deal with pulsed radar signal instead of the CAMP algorithm, in order to manage the problems of the limited number reconstructed targets, according to the incoherence property of the traditional CAMP algorithm, as well as reduce the complexity of the reconstruction algorithm. In present work, the Adaptive recovery CAMP algorithm will be applied to the LFMCW radar signal to enhance the detection performance and the number of detected targets in the LFMCW radar. Simulation results are done to evaluate the proposed algorithm compared with the traditional algorithm, for detection performance evaluation between the proposed algorithm and the traditional algorithm using the Receiver Characteristic Curve (ROC), the number of detected targets, the resolution performance, and the complexity evaluation.