The use of Bayesian location estimation methods (a subset of consensus average tracking methods) is one of the most commonly used tracking methods in Wireless Sensor Networks (WSN) due to high error tolerance, tracking accuracy and scalability. In these networks, due to Quasi-spherical of the transmitting radiation pattern (sensing), it is only possible to estimate the target distance from the received signal level. In this paper, a hybrid method called DCACT based on dynamic clustering and

more »

... ian estimation methods (IMMKF and MMPF), will be presented that tracks the maneuvering target with very good accuracy. The simulation results show DCACT method based on multi-mode particle filter (DCMMPF), with a 50% reduction in active sensors, track the target well only with increasing the location estimation error and velocity estimation error up to 26 and 16 percent respectively.