Adaptive filtration of radio source movement parameters with complex use of sensor network data based on TDOA and RSS methods

Abstract

The optimal and quasi-optimal adaptive algorithms for filtration of parameters of radio source movement with different kinds of maneuvers have been synthesized on the basis of mathematical tools of discrete-time mixed Markov processes. These algorithms involve the complex use of sensor network data obtained on the basis of the TDOA and RSS methods. The devices implementing the above algorithms are multichannel and belong to the class of devices with feedbacks between channels. The presence of feedbacks between channels is stipulated by the Markov property of discrete component describing types of radio source movement. In the quasi-optimal adaptive algorithm, the processing of measurement values coming from sensors of the sensor network is performed by using the sequential calculation procedure. At the same time, this algorithm ensures polygaussian approximation of a posteriori probability density of the estimated vector of parameters of radio source movement. The analysis of quasi-optimal algorithm is carried out by employing the computer-aided statistical simulation using an example of estimating the movement parameters of UAV performing different kinds of maneuvers and sending out radio waves.