Target Tracking With Unknown Maneuvers Using Adaptive Parameter Estimation in Wireless Sensor Networks

Tracking a target which is sensed by a collection of randomly deployed, limited-capacity, and short-ranged sensors is a tricky problem and, yet applicable to the empirical world. in this paper, this challenge has been addressed by introducing a nested algorithm to track a maneuvering target entering the sensor field. in the proposed nested algorithm, different modules are to fulfil different functions, including sensor selection, adaptive maneuver parameter estimation, and target trajectory extraction. to that end, proposed algorithm combines the auxiliary particle filter with the liu and west filter and applies them for the first time in the wireless sensor network. its performance is compared to one of the most common approaches for this kind of problem and the results show the superiority of proposed method in terms of the estimation accuracy. the simulation study also involves evaluating the proposed algorithm based on the scalability criterion and the results are promising since the reduction by 40 percent in the number of active sensors leads to, respectively, 18.2 and 14.3 percent increments in the rmse of position and velocity estimates.