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The Steered Response Power with phase transform (SRP-PHAT) is one of the most employed techniques for Direction of Arrival (DOA) estimation with microphone arrays due its robustness against acoustical conditions as reverberation or noise. Among its main drawbacks is the growth of its computational complexity when the search space increases. To solve this issue, we propose the use of Neural Networks (NN) to obtain the DOA as a regression problem from a low resolution SRP-PHAT power map.


In this paper, target tracking constrained to short-term linear trajectories is explored. The problem is viewed as an extension of the matrix decomposition problem into low-rank and sparse components by incorporating an additional line constraint. The Cramer–Rao Bound (CRB) for the trajectory estimation is derived; numerical results show that an alternating algorithm which estimates the various components of the trajectory image is near optimal due to proximity to the computed CRB.


Radar waveforms often need to be optimized to achieve minimal sidelobes in the ambiguity function. In this paper, it is shown that sampling rate can affect the optimality of the sidelobe level, so the sampling rate should be considered already at the optimization phase. A theorem for the cross-ambiguity function peak values of the oversampled waveforms is developed.