Enhanced Tracking Performance with Signal Amplitude Information of Sensor Networks

Abstract

Automatic multi-target tracking in a binary sensor network needs to solve the simultaneous multiple sources localization problem. The virtual measurement (VM) approach provides a way to solve this problem via the integrated sensing processing (ISP). In the VM approach, a set of activated sensor detections are mapped into a set of virtual measurements as if they were observed by a large sensor. The set of VMs are then used for multi-target tracking. A drawback of this method is that some VMs may have larger variances when sensor nodes are sparsely distributed, which can yield considerably large estimation error. In this paper, we present a method to reduce the uncertainty of VMs using relative signal amplitude information at the cost of communicating more bits from activated sensors to base station. Instead of assigning a VM, we estimate the target source position using relative signal amplitude information which is also known as received signal strength (RSS) and is assumed to be available to the base station. This position is then treated as an alternative VM used by a LMIPDA tracker. Simulation results shown that an improved tracking accuracy can be obtained compared to the case where the standard VMs are used