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A direction of arrival (DOA) estimation algorithm is proposed using the concept of sparse representation. In particular, a new sparse signal representation model called the smoothed covariance vector (SCV) is established, which is constructed using the lower left diagonals of the covariance matrix. DOA estimation is then achieved from the SCV by sparse recovering, where two distinguished error limit estimation methods of the constrained optimization are proposed to make the algorithms more robust. The algorithm shows robust performance on DOA estimation in a uniform array, especially for coherent signals. Furthermore, it significantly reduces the computational load compared with those algorithms based on multiple measurement vectors (MMVs). Simulation results validate the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A Model S195 (8.8 kW) single cylinder was used in this study to determine the effect of four operational parameters, i. e. intake valve close angle, exhaust valve open angle, fuel delivery angle and fuel injection pressure on noise. Single factor and multi-factor quadratic regressive orthogonal methods were adopted in the experiments to find the relationship between the four parameters and noise. By means of optimization technique, the optimum operational parameters for two working conditions of the engine were selected and the test results showed that optimum adjustment could reduce noise by 2–4 dB.