MAP-PF position tracking with a network of sensor arrays

The maximum a posteriori penalty function (MAP-PF) approach is applied to target position tracking with a network of sensor arrays. The track estimation problem is formulated directly from the array data using the MAP estimation criterion. The penalty function method of nonlinear programming is used to obtain a tractable solution. A sequential update procedure is developed in which penalized maximum likelihood estimates of target bearing and power are computed for each array, and then used as synthetic measurements in an extended Kalman filter. The two steps are coupled via the penalty function. The current target state is used to guide the bearing estimation, and estimated signal powers control the influence of the bearing estimates from each array on the final track estimate. The algorithm can be implemented in a decentralized manner where bearing estimation is performed at the arrays, and track estimation is performed at a central processing site.