Filtration of UAV Movement Parameters Based on the Received Signal Strength Measurement Sensor Networks in the Presence of Anomalous Measurements of Unknown Power at the Transmitter

Methods based on received signal strength measurements (RSS measurements) are used to determine the unmanned aerial vehicle (UAV) location using a wireless sensor network. The UAV transmitter power is usually unknown. In real conditions, it often becomes necessary to consider existence of anomalous measurement results. The reasons for the violation of the measurement process can be: the influence of interference, errors in the identification of signals during primary processing, failures of the equipment and similar. The optimum and quasi-optimal adaptive algorithms of UAV movement parameters filtration based on the RSS-measurement sensor networks in the presence of anomalous measurements at the unknown power of the transmitter are developed. These algorithms are obtained using Bayes’ theorems and the Markov property of a mixed process, including a vector of target movement parameters and a discrete component characterizing the type of measurement. Analysis of developed algorithm performance was carried out by Monte Carlo method on 2D plane. The quasi-optimal adaptive filtering algorithm detects the appearance of anomalous measurements with probabilities close to unity and allows one to eliminate their influence on the accuracy of UAV movement parameters estimation and also to estimate the UAV unknown transmitter power.