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This paper addresses a target localization problem in 3-D wireless sensor networks using a hybrid system that fuses received signal strength and angle of arrival measurements. First, we formulate the received signal strength and angle of arrival measurement models as the pseudo-linear equations. Then, the bias is derived from the 3-D angle of arrival measurements that take the measurement noise into account to improve the localization performance. Furthermore, a non-convex estimator is derived based on the Least Squares criterion. Finally, semi-definite relaxation and second-order cone relaxation are applied to transform the derived non-convex estimator into a convex one. We propose a semi-definite relaxation and second-order cone relaxation-based estimator which yields the best performance under a large measurement noise or a small measurement noise. The generalization of the proposed method for known transmit power can also be applied to the case when transmit power is not know. Theoretical analysis and computer simulations corroborate the superior performance of the proposed localization methods over the existing ones. INDEX TERMS Target localization, received signal strength, angle of arrival, convex relaxation.

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Semiautomatic 2D-to-3D conversion plays an important role in generating 3D contents for display. However, most existing methods assume that user scribbles are perfectly correct, and only give acceptable results when user provides accurate labels. To address this problem, Welsch M-estimator data fidelity is used to resist erroneous scribbles. The Welsch M-estimator data fidelity which is able to alleviate the influence of inaccurate scribbles has theoretical guarantee by means of its redescending property. First, the Welsch Mestimator is introduced to measure the fidelity between estimated depth and user provided depth; then local smoothness is built by using color weighted Welsch M-estimator to make neighboring pixels with similar colors have similar depth values. Finally, we solve the problem using generalized iteratively reweighted least squares algorithm. Experiments demonstrate that our method obtains competitive performance in the absence of inaccurate scribbles and outperforms the state of the art both visually and quantitatively in the presence of inaccurate scribbles.

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