A probability density function approach to distributed sensors' path planning

A novel artificial-potential function approach is presented for planning the paths of distributed sensor networks in a complex dynamic environment. The approach implements a novel potential function generated from a probability density function (PDF) parameterized by an adaptive Gaussian mixture that is optimized to meet network-level objectives, such as cooperative track detection. The PDF represents the goal density that would be obtained by sampling a statistically-significant number of sensors from the mixture. However, since a smaller number of sensors may be deployed, and each sensor is represented by a disk, the potential function is generated by multiplying the PDF by a likelihood update model that produces networks with disjoint fields-of-view. The approach is demonstrated through numerical simulations involving ocean sensor networks deployed in a region of interest near the New Jersey coast.