Performance analysis and path planning for UAVs swarms based on RSS measurements

In the localization estimation system, it is well known that the sensor-emitter geometry can seriously impact the accuracy of the location estimate. In this paper, we analyze the optimal deployment for received signal strength (RSS) localization with the measurement noise is set to be distance dependent. First, the Cramer-Rao low bound (CRLB) with distance-dependent noise in RSS localization is calculated and chosen to be the optimality criterion. The optimal deployment is analyzed via angle and distance criterion, respectively. Then, the analytic solutions to the optimal deployment are derived in both static and movable target scenarios. Finally, we extend our work to the path planning problem with constraints and interior penalty method is applied to settle the constrained nonlinear optimization problem. The simulation results show that the path optimization verifies the accuracy of the analytical findings.