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The posterior CramérRao lower bound (PCRLB) for sequential Bayesian estimators, which was derived by Tichavsky in 1998, provides a performance bound for a general nonlinear filtering problem. However, it is an offline bound whose corresponding Fisher information matrix (FIM) is obtained by taking the expectation with respect to all the random variables, namely the measurements and the system states. As a result, this unconditional PCRLB is not well suited for adaptive resource management for dynamic systems. The new concept of conditional PCRLB is proposed and derived in this paper, which is dependent on the actual observation data up to the current time, and is implicitly dependent on the underlying system state. Therefore, it is adaptive to the particular realization of the underlying system state and provides a more accurate and effective online indication of the estimation performance than the …