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Abstract: Sparse linear arrays provide better performance than the filled linear arrays in terms of direction estimation and resolution with reduced size and low cost. However, they are subject to manifold ambiguity. A method based on the Multiple Signal Classification (MUSIC) algorithm to solve the manifold ambiguity of uncorrelated sources for sparse array is proposed in this paper. The method consists of two steps. The first step is to obtain all the directions of arrivals (DOAs), including true and spurious DOAs, using traditional MUSIC. The second step is to estimate the power values of the all DOAs by substituting all the DOAs to a cost function. The well-known Davidson Fletcher Powell (DFP) and Broyden Fletcher Goldfarb Shanno (BFGS) algorithms are used to estimate the power values. The power values of spurious DOAs are very small or tend to zero compared with the values of the true DOAs. The true DOAs are then discriminated easily from the spurious DOAs with the power values. Simulation results demonstrate the effectiveness and the feasibility of the method. [Copyright &y& Elsevier]

Abstract: In this paper, we present an efficient external calibration technique for a typical multi-camera system. The technique is very handy in practice using a simple planar pattern. Based on homography, three different pair-wise estimation algorithms are proposed to recover the rigid rotation and translation between neighboring cameras. By registering the accurate and reliable partial calibrated structures, the complete calibration of a multi-camera system can be accomplished. In order to evaluate and compare the calibration results for different camera setups, a novel accuracy metric is introduced based on the deflection angles of projection rays. Experimental results using both simulated and real data are presented to verify the validity and performance of the proposed method. [Copyright &y& Elsevier]