Target localization and tracking in a random access sensor network

We consider tracking of multiple objects using a wireless sensor network where distributed nodes transmit to a fusion center using random access. During an initialization phase, targets are identified on a discrete set of locations using a sparse identification method. Tracking then proceeds to update the target locations and amplitudes explicitly, using a gradient algorithm to solve the underlying non-linear optimization problem. Updating continues at the pace dictated by the average sensing/transmission rate, which can be adjusted to suit an expected target velocity. By focusing explicitly on the target locations, as opposed to continuing with sparse identification over a quantized space whose size may be much greater than the number of targets, the goal is to reduce the computational complexity, improve the performance, and eliminate the spatial quantization effects.