Most target tracking applications developed for wireless sensor networks thus far employ passive sensors which detect the target's emissions. Their performance is thereby dictated by the magnitude of the target's signal and the receivers' sensitivity. Instead, we propose using a network of low-power Doppler radars that actively measure the target's radial velocity. Nodes combine their measurements to solve a system of nonlinear equations that estimate the target's position and velocity. Because

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... these equations have no closed-form solutions we solve them using numerical methods. These methods however can lead to local minima or even diverge in the presence of even small measurement noise. For this reason we couple the proposed numerical method with an Extended Kalman filter that models the target's movement along a straight line. Clearly the target does not always follow a linear path and so we augment this simple Kalman filter with a method that detects the target's turns and updates the filter's dynamical model accordingly. The combination of the two approaches improve tracking accuracy compared to the numerical solution alone. Results from simulations and a prototype implementation suggest that the combined solution can effectively track a mobile target with average localization error as low as 25 cm in a 10×10 m outdoor field.