In this paper, a new calibration method for gain and phase errors in large uniform rectangle arrays is proposed for underwater 3-D sonar imaging systems. It requires only one calibrator source at an unknown position in the far field. An efficient and speedy three-step-iteration algorithm is performed first to provide a robust direction-of-arrival (DOA) estimator in the presence of gain and phase errors. It then follows with the gain and phase errors estimation using a spatial matched filter. Finally, a maximum a posteriori (MAP) exercise is executed to further adjust the estimated phase parameters and the source direction. The statistical performance of the proposed algorithm will be demonstrated and compared to representative methods. It will be shown that the proposed method is computationally effective and asymptotically efficient in Monte Carlo simulations.Index Terms-Array signal processing, autocalibration, gain and phase error, underwater 3-D sonar imaging, uniform rectangular array (URA).
Hardware implementation of Neural Networks (NNs) provides advantages such as parallelism and real-time capabilities, whereas Probabilistic Neural Networks (PNNs) achieve high accuracy in pattern discrimination. In this paper, a FPGA implementation of a PNN sorting algorithm is proposed to sort spikes. Both Matlab-based and FPGA-based sorting algorithms using a PNN were implemented and evaluated, and results show that FPGA's implementation is about 44.37 times faster than Matlab's realization with the same accuracy. This novel method indicates that the performance of current FPGAs is capable of portable device application.
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