Many researchers are interested in networked control systems with packet losses. The research on packet losses can be traced back to Nahi [1] and Hadidi [2]. Recently, the packet loss problem has been studied this using jump linear systems (JLS), which is a hybrid system with model transitions modeled as Markov chains that switches among several discrete models [3�C5]. JLS methods restrict their formulation Inhibitors,Modulators,Libraries to the steady-state case, where Kalman gain is constant. Furthermore, the transition probability and state error covariance matrices are required being computed exactly [6]. Sinopoli et al. [7] consider general case of time varying Kalman gain and discusses how packet dropouts can affect state estimation. They illustrate that Inhibitors,Modulators,Libraries there exists a certain threshold of the packet dropout rate.

Under packet dropouts, their filter has a smaller linear minimum mean square error than static counterpart. Liu et al. [8] extend the idea to the case where there are multiple sensors and packets from different sensors dropping independently. In practice, it is assumed that packets are dropped independently, Inhibitors,Modulators,Libraries which is certainly not true in case where burst packets are dropped or in queuing networks where consecutive packets are not dropped [7,8]. They also use the expected value of the error covariance matrix as the measure of performance. This might ignore the fact that events with arbitrarily low probability can make the expected value diverge. So Epstein et al. [9] give a more complete characterization of the estimator performance instead of considering a probabilistic description of the error covariance.

The optimal filtering problem is considered for systems where multiple packets are dropped in an unreliable network [10�C12]. Different from [10�C12], where only the multiple packet dropouts are considered, reference [13] investigates both the estimation problem for systems with bounded random measurement Inhibitors,Modulators,Libraries delays and packet dropouts, which Batimastat are described by some binary random variables whose probabilities are only known. Schenato [14] proposes a probabilistic framework to design the minimum error covariance estimator in a generic digital communication network where sensor’s observation packets are subject to random delays and packet losses. Speranzon et al. [15] analyze and design a distributed adaptive algorithm to estimate a time-varying signal, measured by WSNs, where measurement noise and packet losses are considered.

But they do not consider multiple packet losses in WSNs.On the other hand, mobile target tracking with multiple third sensors measurement is an important application of WSNs in recent years. There are great deals of wireless sensor nodes deployed randomly in a monitored field. One node or several nodes are scheduled as tasking nodes in target tracking application at each time step.