Sensor Fusion with Asynchronous Decentralized Processing for 3D Target Tracking with a Wireless Camera Network.

We present a method to acquire 3D position measurements for decentralized target tracking with an asynchronous camera network. Cameras with known poses have fields of view with overlapping projections on the ground and 3D volumes above a reference ground plane. The purpose is to track targets in 3D space without constraining motion to a reference ground plane. Cameras exchange line-of-sight vectors and respective time tags asynchronously. From stereoscopy, we obtain the fused 3D measurement at the local frame capture instant. We use local decentralized Kalman information filtering and particle filtering for target state estimation to test our approach with only local estimation. Monte Carlo simulation includes communication losses due to frame processing delays. We measure performance with the average root mean square error of 3D position estimates projected on the image planes of the cameras. We then compare only local estimation to exchanging additional asynchronous communications using the Batch Asynchronous Filter and the Sequential Asynchronous Particle Filter for further fusion of information pairs' estimates and fused 3D position measurements, respectively. Similar performance occurs in spite of the additional communication load relative to our local estimation approach, which exchanges just line-of-sight vectors.

Sum-Rate Channel Capacity for Line-of-Sight Models.

This work considers a base station equipped with an M-antenna uniform linear array and L users under line-of-sight conditions. As a result, one can derive an exact series expansion necessary to calculate the mean sum-rate channel capacity. This scenario leads to a mathematical problem where the joint probability density function (JPDF) of the eigenvalues of a Vandermonde matrix WWH are necessary, where W is the channel matrix. However, differently from the channel Rayleigh distributed, this joint PDF is not known in the literature. To circumvent this problem, we employ Taylor's series expansion and present a result where the moments of mn are computed. To calculate this quantity, we resort to the integer partition theory and present an exact expression for mn. Furthermore, we also find an upper bound for the mean sum-rate capacity through Jensen's inequality. All the results were validated by Monte Carlo numerical simulation.