Passive localization of multiple sources using joint RSS and AOA measurements in spectrum sharing system

In spectrum sharing systems, locating multiple radiation sources can efficiently find out the intruders, which protects the shared spectrum from malicious jamming or other unauthorized usage. Compared to single-source localization, simultaneously locating multiple sources is more challenging in practice since the association between measurement parameters and source nodes are not known. Moreover, the number of possible measurements-source associations increases exponentially with the number of sensor nodes. It is crucial to discriminate which measurements correspond to the same source before localization. In this work, we propose a centralized localization scheme to estimate the positions of multiple sources. Firstly, we develop two computationally light methods to handle the unknown RSS-AOA measurements-source association problem. One method utilizes linear coordinate conversion to compute the minimum spatial Euclidean distance summation of measurements. Another method exploits the long-short-term memory (LSTM) network to classify the measurement sequences. Then, we propose a weighted least squares (WLS) approach to obtain the closed-form estimation of the positions by linearizing the non-convex localization problem. Numerical results demonstrate that the proposed scheme could gain sufficient localization accuracy under adversarial scenarios where the sources are in close proximity and the measurement noise is strong.