Self-Organizing Multilateration of an Unknown Number of Transmitters

Wireless Sensor Networks (WSNs) that classify the source of detected radio signals require mobile transmitters, physical (PHY) and link layer meta data, and packet sniffing capabilities. These signal classifiers are restricted by assumptions that may be difficult to realize in adversarial Signal of Opportunity (SOP) localization settings, and they do not jointly localize transmitters. In this paper, we present a novel framework that self-organizes to classify and jointly localize sets of stationary transmitters emitting SOP. The framework leverages the underlying Gaussian distribution associated with multilateration estimates via the use of Unsupervised Learning (UL) techniques. Inference of spatial multilateration features allows for the joint estimation of classification outcomes with respect to several unknown parameters, including the number of transmitters, source transmitters for each signal, the underlying multilateration distribution, and the transmitter locations. The proposed framework was evaluated in a two-dimensional trilateration experiment. Signals transmitted by vehicular Tire Pressure Monitoring System (TPMS) wireless beacons were observed by a custom-built WSN test bed to produce Received Signal Strength Indicators (RSS) features. We used a trained Convolutional Neural Network (CNN) to make location estimates from the RSS feature data. An Anderson-Darling test showed that these CNN estimates were statistically indistinguishable from those of a normal distribution. The spatial trilateration estimates were clustered to identify six of the eight TPMS transmitters with a 75% cluster detection rate, which was the result of every statistically different spatial and RSS population as determined by a Kruskal-Wallis (KW) test. The source transmitter of every signal was classified with a 76.4% indicator variable accuracy (93.7% when removing statistically identical RSS populations) and the detected source transmitters were localized with an average of 1.72 m variance and 1.19 m bias within a roughly 15 m square whose perimeter is made up of receivers.