ISAR Imaging Of Ship Target Based On Compressed Sensing

The emergence of compressed sensing provides a new idea for radar imagingtechnology, but also because of the presence of sea clutter, radar data of sparsity isdamaged, which is the greedy iterative algorithm’s requires, and then for compressedsensing reconstruction algorithm, no doubt there is a great impact. This article will useSAMP algorithm to compressed sensing radar imaging. First, the algorithm does notrequire sparsity as an input parameter, which for sparsity affected by sea clutter radarecho is good news. Secondly, SAMP algorithm sets the threshold value for the iterativereconstruction algorithm termination conditions. Based on the situation of sea clutter forthreshold estimates, this article will give a nice result of suppressing sea clutter.Firstly, this thesis analyzes the amplitude characteristics of sea clutter, and the timeand space-related characteristics, uses SIRP algorithms to simulate sea clutter,combined with the simulation results compared to the theoretical analysis of thesimulation of sea clutter temporal and spatial correlation, finally it has proved theeffectiveness of the method. Next, it created a model ship target scatterers, thenintroduced the basic principles of the RD. And base on the RD algorithm, it simulatesthe ISAR imaging of ship model under sea clutter. In the end,it analyzes the effect ofsea clutter for ISAR imaging results base on RD algorithm.Then, this thesis uses compressed sensing to radar imaging in azimuth. First itintroduced the principle of compressed sensing imaging in azimuth, constructed sparsegroups required by the algorithm, reconstruction algorithm is the OMP algorithm andSAMP algorithm of greedy iterative algorithm. This paper analyzes the impact of thesparsity and the measuremet for OMP algorithm, as well as the impact of the step lengthand the threshold and the number of measurements for the SAMP algorithm. Thesimulation results proved the importance of sparsity to the performance of the algorithm,which reflects the Advantage of SAMP algorithm, because SAMP algorithm don’t needthe sparsity as an input parameter. On the other hand, with theoretical and simulationresults, it proved that the method of estimating the magnitude of sea clutter for thresholdin SAMP algorithm is feasible and reasonable, it can significantly suppress sea clutteramplitude.Finally, this thesis uses compressed sensing to radar imaging in distance. It hasused OMP reconstruction algorithm and SAMP reconstruction algorithms, andcompared with the RD algorithm. Then, this paper analyzes the impact of the number ofmeasurements for OMP algorithm and SAMP reconstruction algorithms. In the end, it simulated with OMP algorithm and SAMP algorithms under different average powerSNR.