Observation Density Based Static Optimization of Sensor Parameter

Since the estimation performance of bayesian filters is largely affected by the observations, sensor management has attracted many researchers in bayesian filtering. State Prior Probability Distribution (SPPD) at each time step plays an important role in the dynamic optimization of sensor parameter. However, before all measurements came, SPPD at each time step is not available in the static optimization. In order to solve this problem, an objective function, which is based on the observation density, is built to evaluate the effect of the sensor parameter, named Minimum Expected Relative Observation Density (MEROD). Considering the problem when the objective function does not have the analytical solution, a numerical method, which is based on Monte Carlo Integration, is proposed. The validity of this numerical method is proved here. A bearings only tracking experiment is implemented to compare the optimized sensor parameter with another four randomly selected sensor parameters. Simulation results show that Sampling Importance Resampling(SIR) and Unscented Particle Filter(UPF) achieved better estimation performance when using the optimized sensor parameter.