Robust Relative Fingerprinting-Based Passive Source Localization via Data Cleansing

Recently, source localization is becoming a major research focus. The majority of the existing studies focus on the design of received signal strength (RSS)-based localization methods. However, when in the face of complicated environments with severe fading, RSS-based localization methods achieve relatively inferior accuracy performance, compared with fingerprinting-based localization methods. Nevertheless, traditional fingerprinting-based localization methods are subject to the condition that the source transmit power is known, which cannot be directly used in passive localization cases where the sensing nodes do not have the prior information on the source. In addition, the received sensing data may contain errors and then affect the location precision due to various abnormal conditions, such as device failure and malicious cases. In this paper, we propose a novel robust relative fingerprinting-based passive localization algorithm via a data cleansing approach. First, we figure out the fingerprint correlations property and introduce a new relative fingerprint framework. The key idea is that by exploring the correlations between the source fingerprint and the reference fingerprint database, the correction factors can be achieved to apply the fingerprint idea into the passive localization case. Second, we formulate a generalized modeling of the abnormal data in localization problem and propose a data cleansing approach which utilizes the sparse property of the abnormal data. Based on this, the negative influence of abnormal data can be further eliminated. Third, considering the sparse property of the source position, we use the sparse Bayesian learning in the matching process for the purpose of achieving more precise estimated source position. Simulation results demonstrate that the proposed algorithm achieves higher accuracy performance in passive source localization in terms of eliminating the abnormal data impairment.