FILTRATION OF PARAMETERS OF THE UAV MOVEMENT AT COMPLEX USE OF DATA SENSOR NETWORKS, OBTAINED BASED ON THE TDOA AND RSS METHODS
Keywords:UAV, TDOA, RSS, the expanded Kalman filter, wireless sensor network, parameters of movement.
Background. At the present time the development of technologies for construction of unmanned aerial vehicles (UAVs) are used for the solution of a wide range of tasks, such as: emergency rescue operations, autonomous observation and monitoring of industrial processes and environment (fauna monitoring), etc. On the other hand, their availability and massive use for a wide range of problems has led to the emergence of a new class of threats: application in terrorist purposes, photographing of secret objects, receiving unauthorized access to information in WLAN networks, invasion on the forbidden territory, etc. This leads to the need to develop security systems that solve the problem of detection, positioning and movement parameters of the UAV.
Objective. Synthesize algorithm the filtration of parameters of the UAV movement at complex use of data, obtained based on the TDOA and RSS methods.
Methods. Synthesis of algorithm the filtration of parameters of the UAV movement at complex use of data, obtained based on the TDOA and RSS methods, it is executed on the basis of the mathematical device of the extended Kalman filter. Efficiency analysis of a developed algorithm is carried out by means of statistical modeling. For descriptive reasons of the algorithm works the test trajectory of the UAV movement has been created.
Results. As appears from results of modeling, complex use of data allows reducing RMS errors of the position estimation of the UAV more, than by 3 times, in comparison with independent processing on the basis of the data obtained by the TDOA and RSS methods..
Conclusions. The algorithm filtration of parameters of the UAV movement synthesized on the basis of a mathematical apparatus of the expanded Kalman filter is recurrent and implements a sequential procedure for combining data obtained on the basis of the TDOA and RSS methods. The developed algorithm takes into account the dispersion of power measurement errors, received signals by sensors of the sensor network and determines the estimate of the unknown error of measuring the arrival time signal of the reference sensor.
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