Recursive joint Cramer-Rao lower bound for parametric systems with two-adjacent-states dependent measurements

Joint Cramer-Rao lower bound (JCRLB) is very useful for the performance evaluation of joint state and parameter estimation (JSPE) of non-linear systems, in which the current measurement only depends on the current state. However, in reality, the non-linear systems with two-adjacent-states dependent (TASD) measurements, that is, the current measurement is dependent on the current state as well as the most recent previous state, are also common. First, the recursive JCRLB for the general form of such non-linear systems with unknown deterministic parameters is developed. Its relationships with the posterior CRLB for systems with TASD measurements and the hybrid CRLB for regular parametric systems are also provided. Then, the recursive JCRLBs for two special forms of parametric systems with TASD measurements, in which the measurement noises are autocorrelated or cross-correlated with the process noises at one time step apart, are presented, respectively. Illustrative examples in radar target tracking show the effectiveness of the JCRLB for the performance evaluation of parametric TASD systems.

Automated summary

Joint Cramer-Rao lower bound (JCRLB) is a valuable tool for evaluating the performance of joint state and parameter estimation in non-linear systems, including those with TASD measurements. The recursive JCRLB for general and special forms of parametric systems provides insights into the effectiveness of JCRLB for TASD systems, as illustrated through radar target tracking examples.