Gain-Constrained Recursive Filtering With Stochastic Nonlinearities and Probabilistic Sensor Delays
Gain-Constrained Recursive Filtering With Stochastic Nonlinearities and Probabilistic Sensor Delays
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DOI：
10.1109/tsp.2012.2232660
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发表时间：
2013-03
影响因子：
5.4
通讯作者：
Jun Hu;Zidong Wang;Bo Shen;Huijun Gao
中科院分区：
文献类型：
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作者：
Jun Hu;Zidong Wang;Bo Shen;Huijun Gao
研究了一类具有随机时滞和相关噪声的时变非线性随机系统的增益约束递归滤波问题。随机非线性描述的统计手段，包括乘法随机干扰作为一种特殊情况。通过引入由取值为1或0的伯努利分布随机变量组成的对角矩阵来建模概率传感器延迟现象，这意味着传感器可能会经历具有个体延迟特性的随机发生的延迟。过程噪声是有限步自相关的。所解决的增益约束滤波问题的目的是设计一个滤波器，使得对于所有的概率传感器延迟，随机非线性，增益约束以及相关的噪声，有关的滤波误差的成本函数最小化在每个采样时刻，其中滤波器增益满足一定的等式约束。提出了一种新的递归滤波算法，该算法保证了滤波器在每个采样时刻的局部最优性和无偏性，达到了预先给定的滤波器增益约束。仿真结果表明了该方法的有效性。
This paper is concerned with the gain-constrained recursive filtering problem for a class of time-varying nonlinear stochastic systems with probabilistic sensor delays and correlated noises. The stochastic nonlinearities are described by statistical means that cover the multiplicative stochastic disturbances as a special case. The phenomenon of probabilistic sensor delays is modeled by introducing a diagonal matrix composed of Bernoulli distributed random variables taking values of 1 or 0, which means that the sensors may experience randomly occurring delays with individual delay characteristics. The process noise is finite-step autocorrelated. The purpose of the addressed gain-constrained filtering problem is to design a filter such that, for all probabilistic sensor delays, stochastic nonlinearities, gain constraint as well as correlated noises, the cost function concerning the filtering error is minimized at each sampling instant, where the filter gain satisfies a certain equality constraint. A new recursive filtering algorithm is developed that ensures both the local optimality and the unbiasedness of the designed filter at each sampling instant which achieving the pre-specified filter gain constraint. A simulation example is provided to illustrate the effectiveness of the proposed filter design approach.