Mesh-based consensus distributed particle filtering for sensor networks

Following the Bayesian inference framework, this paper investigates the problem of distributed particle filtering over a sensor network to achieve consensus. The objective of the posterior-consensus strategy is to fuse the posterior probability distribution functions (PDFs) at different sensor nodes, so that an agreement of belief can be established in terms of the Kullback-Leibler average (KLA). To facilitate the consensus process and reduce the communication load, the local PDFs are approximated with weighted meshes and transmitted between neighboring nodes. The mesh representations are constructed by resorting to a grid partition of the state space, such that the PDF can be approximated by a linear combination of indicator functions. To derive a particle representation of the fused PDFs, a novel importance density function is designed to draw particles with respect to the information from all neighboring nodes. The weights of the particles are calculated via the recursive solution of the KLA. The effectiveness of the proposed filtering approach is demonstrated through two target tracking examples.</p

Auxiliary particle filtering over sensor networks under protocols of amplify-and-forward and decode-and-forward relays

In this paper, the particle filtering problem is investigated for a class of stochastic systems with multiple sensors under signal relays. To improve the performance of signal transmissions, a relay is deployed between each sensor and the remote filter. Both amplify-and-forward (AF) and decode-and-forward (DF) relays are considered under certain transmission protocols. Stochastic series are employed to describe multiplicative channel gains and additive transmission noises. Novel likelihood functions are derived based on the AF/DF relay models under different protocols. With the measurements collected from all the sensor nodes, a new centralized auxiliary particle filter (APF) is designed by resorting to the statistical information of the channel gains and transmission noises. Next, a consensus-based distributed APF is further established at each node that requires only locally available information. Finally, the effectiveness of the proposed filtering approach is demonstrated through target tracking simulation examples in different situations. </p

A novel algorithm for quantized particle filtering with multiple degrading sensors: degradation estimation and target tracking

This paper addresses the particle filtering problem for a class of nonlinear/non-Gaussian systems with quantized measurements and multiple degrading sensors. The measurement output of each sensor is quantized by a uniform quantizer before being sent to the remote filter. An augmented system is constructed which aggregates the original system state and the degradation variables. In the presence of the sensor degradation and the quantization errors, a new likelihood function at the remote filter is calculated by resorting to all the transmitted measurements. According to the mathematical characterization of the likelihood function, a novel particle filtering algorithm is developed where the parameters of both the degradation processes and the quantization functions are exploited to obtain the modified importance weights. Finally, the effectiveness of the proposed method is shown via a target tracking example with bearing measurements

Distributed filtering for complex networks under multiple event-triggered transmissions within node-wise communications

This paper focuses on the distributed filtering and fault estimation problems for a class of complex networks, where the communications between filters at different nodes are subject to dynamic event-triggered (DET) transmissions. A filter is constructed at each node by resorting to local measurements and information from neighboring nodes and thus the developed algorithm can be carried out distributedly. Different from the clock-driven signal transmissions in traditional distributed filtering schemes, the transmissions of both state estimates and the upper bounds of filtering error covariances (FECs) between the nodes are monitored by a multiple DET strategy to reduce unnecessary burdens in the links. Under DET transmissions, an upper bound of the FEC is obtained and then minimized via parameterizing the filter recursively. Novel sufficient conditions, which are dependent on locally available information, are provided to guarantee the uniform boundedness of the FEC at each node. The proposed method is used to solve the fault estimation problem in complex networks, where the estimation error is ensured to be exponentially bounded. Some illustrative examples are employed to show the effectiveness of our algorithm

Joint state and fault estimation of complex networks under measurement saturations and stochastic nonlinearities

In this paper, the joint state and fault estimation problem is investigated for a class of discrete-time complex networks with measurement saturations and stochastic nonlinearities. The difference between the actual measurement and the saturated measurement is regarded as an unknown input and the system is thus re-organized as a singular system. An appropriate estimator is designed for each node which aims to estimate the system states and the loss of the actuator effectiveness simultaneously. In the presence of measurement saturations and stochastic nonlinearities, upper bounds of the error covariances of the fault estimates are recursively obtained and then minimized. Sufficient conditions are proposed to guarantee the existence and the unbiasedness of the developed estimator. Our developed estimator design algorithm is distributed because it depends only on the local information and the information from the neighboring subsystems, thereby avoiding the usage of a center estimator. Finally, simulation results are presented to show the performance of the proposed strategy in simultaneously estimating the states and faults