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Sensing Dictionary Optimization Method for Target Localization in Sensor Networks

Compressed sensing method provides a new idea for target localization in sensor networks. However, in practical applications, due to the influence of sensor deployment and grid size, the sensing matrix may not satisfy restricted isometry property (RIP) in sparse signal reconstruction. To solve this problem, this paper proposes a sensing dictionary optimization method for target localization, which is based on equiangular non-coherent unit norm tight frame sensing matrix. The method uses a random matrix as the initial preprocessing matrix. By relaxing the gram matrix iteratively and with matrix algebraic decomposition, an optimal frobenius norm tight frame is attained as the optimal sensing matrix. By preconditioning processing, the cross-correlation between the columns of the sensing matrix is reduced, which improves the estimation performance of the target localization. Simulation results show that the proposed method has better localization performance than forward-backward pursuit (FBP) method using non-optimized dictionary.