Robust Bearing-Only Localization Using Total Least Absolute Residuals Optimization

Ji-An Luo, Chang-Cheng Xue and Dong-Liang Peng

Complexity, 2020, vol. 2020, 1-13

Abstract:

Robust techniques critically improve bearing-only target localization when the relevant measurements are being corrupted by impulsive noise. Resistance to isolated gross errors refers to the conventional least absolute residual (LAR) method, and its estimate can be determined by linear programming when pseudolinear equations are set. The LAR approach, however, cannot reduce the bias attributed to the correlation between system matrices and noise vectors. In the present study, perturbations are introduced into the elements of the system matrix and the data vector simultaneously, and the total optimization problem is formulated based on least absolute deviations. Subsequently, an equivalent form of total least absolute residuals (TLAR) is obtained, and an algorithm is developed to calculate the robust estimate by dual ascent algorithms. Moreover, the performance of the proposed method is verified through the numerical simulations by using two types of localization geometries, i.e., random and linear. As revealed from the results, the TLAR algorithm is capable of exhibiting significantly higher localization accuracy as compared with the LAR method.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:3456923

DOI: 10.1155/2020/3456923

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