Passive Position Estimation of Moving Target Using the Measured Signals of TDOA and FDOA and Intelligent Kalman Filter

In this paper, a moving target tracking algorithm using measured signals of Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) is presented. TDOA algorithm alone is not enough to estimate the position of the target region. That is why in this paper by using both TDOA system and measured FDOA signals, location and speed estimation of target are addressed. Kalman filter has a significant application in estimation of both calculations and location. Nevertheless, when noise and uncertainty exist in the estimation, the estimation error could be great.In this paper, it is intended to present design of a robust estimator as an optimization problem. Basically, a robust system must be able to withstand parametric changes and also has a constant and proper operation in almost all cases. Intelligent methods are used in this paper to determine the optimal values of parameters of robust estimator. Considering continuous nature of studied optimization problem (regarding the real nature of unknown parameters), algorithms should be used that work on continuous spaces, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). In this paper, both PSO and DE algorithms are used for designing the robust estimator and then a two-degree system with one uncertain parameter in state matrix is used in state estimation

Keywords:

state estimation , Kalman filter , PSO algorithm , DE algorithm , TDOA