An Improved Sensor Selection for TDOA-Based Localization with Correlated Measurement Noise
This paper focuses on the problem of sensor selection in time-difference-of-arrival (TDOA) localization scenario with correlated measurement noise. The challenge lies in how to select the reference sensor and ordinary sensors simultaneously when the TDOA measurement noises are correlated. Specifically, the optimal sensor subset is found by introducing two independent Boolean selection vectors and formulating a nonconvex optimization problem, which motivates to minimize the localization error in the presence of correlated noise and energy constraints. Upon transforming the original nonconvex problem to the semidefinite program (SDP), the randomization method is leveraged to tackle the problem, and thereby proposing the novel algorithm for sensor selection. Simulations are included to validate the performance of proposed algorithm by comparing with the exhaustive search method.
KeywordsSensor selection Time-difference-of-arrival Source localization Convex optimization
This work was supported by the National Natural Science Foundation of China under Grant 61631015, 61401323, 61471395 and 61501356, by the Key Scientific and Technological Innovation Team Plan (2016KCT-01), the Fundamental Research Funds of the Ministry of Education (7215433803 and XJS16063).
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