MAP-PF 3D position tracking using multiple sensor array

Abstract

The maximum a posteriori penalty function (MAP-PF) approach is applied to three-dimensional (3D) target position tracking of multiple wideband sources using multiple distributed sensor arrays. The track estimation problem is formulated directly from the array data using the maximum a posteriori (MAP) estimation criterion. The penalty function (PF) 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 directions-of-arrival (DOAs) and spectra are computed at each array and then used as synthetic measurements in a set of extended Kalman filters. The two steps are coupled via the penalty function. The current target states are used to guide the DOA/spectrum estimation, and the estimated signal spectra control the influence of the DOA estimates from each array on the final track estimates. The algorithm can be implemented in a decentralized manner where DOA/spectrum estimation is performed at the arrays, and track estimation is performed at a central processing site.