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In this paper, we propose adaptive algorithms for system identification of sparse systems. We introduce a L1norm penalty to improve the performance of affine projection algorithms. This strategy results in two new algorithms, the zeroattracting APA (ZA-APA) and the reweighted zero-attracting AP (RZA-APA). The ZA-APA is derived via the combination of a L1-norm penalty on the coefficients into a standard APA cost function, which generates a zero attractor in the update function. The zero attractor promotes sparsity in the filter coefficients during the update process, and therefore accelerates convergence when identifying sparse systems. We show that the ZA-APA can achieve a lower mean square error than the standard LMS and AP algorithms. To further improve the performance, the RZA-APA is developed using a reweighted zero attractor. The performance of the RZA-APA is superior to that of the ZA-APA numerically. Simulation results demonstrate the advantages of the proposed adaptive algorithms in both convergence rate and steady-state behavior under sparsity assumptions on the true coefficient vector. The RZA-APA is also shown to be robust when the number of non-zero taps increases.

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An obstacle perception system for intelligent vehicle is proposed. The proposed system combines the stereo version technique and the deep learning network model, and is applied to obstacle perception tasks in complex environment. In this paper, we provide a complete system design project, which includes the hardware parameters, software framework, algorithm principle, and optimization method. In addition, special experiments are designed to demonstrate that the performance of the proposed system meets the requirements of actual application. The experiment results show that the proposed system is valid to both standard obstacles and non-standard obstacles, and suitable for different weather and lighting conditions in complex environment. It announces that the proposed system is flexible and robust to the intelligent vehicle.

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