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.