Decentralized computation of the conditional posterior Cramér-Rao lower bound: Application to adaptive sensor selection

Motivated by the problem of adaptive resource management in decentralized sensor networks, the paper derives an algorithm for the distributed computation of the conditional posterior Cramér-Rao lower bound (PCRLB) for nonlinear tracking applications as an alternative to the non-conditional (conventional) PCRLB. Using the proposed conditional bound, a decentralized adaptive sensor-selection algorithm is then developed with the objective of dynamically activating a subset of observation nodes to optimize the network´s performance. Our Monte Carlo simulations verify the superiority of the proposed decentralized PCRLB based sensor selection approach in bearing only tracking applications over its conventional counterparts.