带一步随机延迟量测非线性序列贝叶斯估计的条件后验克拉美罗下界

为了解决带一步随机延迟量测非线性状态估计器可获得最优性能的评价问题,提出了一种适用于带一步随机延迟量测非线性系统的条件后验克拉美罗下界(Conditional posterior Cramr-Rao lower bound, CPCRLB),且现有的CPCRLB仅是所提出的CPCRLB在延迟概率为零时的一种特例. 为了递归地计算提出的CPCRLB,本文提出了一种带一步随机延迟量测的粒子滤波器(Particle filter, PF),继而推导了提出的CPCRLB 一般近似解和在高斯噪声情况下的特殊近似解. 单变量非平稳增长模型、纯方位跟踪和频率调制信号模型的数值仿真证明了本文提出方法与现有方法相比的有效性和优越性.

Conditional Posterior Cramér-Rao Lower Bound for Nonlinear Sequential Bayesian Estimation with One-step Randomly Delayed Measurements

In order to solve the problem of assessing the achievable optimal performance of nonlinear state estimator with one-step randomly delayed measurements, a new conditional posterior Cramér-Rao lower bound (CPCRLB) for nonlinear systems with one-step randomly delayed measurements is proposed. The existing CPCRLB is only a special case of the proposed CPCRLB when the latency probability is zero. In order to calculate the proposed CPCRLB recursively, a new particle filter (PF) with one-step randomly delayed measurements is proposed, based on which a general approximate formulation and a special approximate formulation for Gaussian noises case of the proposed CPCRLB are developed. The effectiveness and superiority of the proposed method as compared with the existing methods are illustrated in numerical examples concerning univariate non-stationary growth model, bearings-only tracking and frequency modulated signal model.