Cramér-Rao lower bounds for stochastic-bias discrete-time system with incomplete measurements
不完全量测下随机有偏离散系统的Cramér-Rao下界

A modified recursive Cramér-Rao lower bound(CRLB) of the statistical estimation error variance is derived for the state estimation with incomplete and stochastic-biased measurement sequences. Firstly, a mathematical model of the discrete-time system with incomplete and stochastic-biased measurements is built; and then, the enumeration CRLB and the statistical CRLB are derived, respectively. The proposed statistical CRLB is a lower bound of the enumeration CRLB, but its calculation complexity is far lower than that of the enumeration CRLB. Simulation is performed in an optical-electrical tracking system with pre-specified detection probability and biased occurrence probability.