Maximum Correntropy Criterion With Variable Center for Robust Passive Multistatic Localization

Passive multistatic localization (PML) refers to locating a signal-reflecting/relaying target using the bistatic range and direct range measurements acquired by employing multiple spatially-separated transmitters and receivers, where the transmitter positions are unknown. In real-world applications, one of the major technical challenges faced in PML is the non-line-of-sight (NLOS) propagation of signals, and recent studies have turned to the concept of robust statistics to tackle such an issue. Continuing to delve into this research direction, here we address the discrepancy arising from the fact that the conventional robust statistical PML schemes inherently assume zero-centered error samples, which may not hold true when the positive NLOS biases are present. In contrast to the existing PML solutions, our proposal is based on the maximum correntropy criterion with variable center (MCC-VC), thereby taking into consideration the potential non-zero-centrality of error samples in the estimator derivation. Subsequently, we develop an alternating minimization algorithm to handle the nonconvex MCC-VC optimization problem in a way that can strike a fine balance between accuracy and computational efficiency. The superiority of our PML approach over its competitors is demonstrated via computer simulations.

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