Multi-sensor Estimation Problem Research Articles

Linear Convergence of distributed estimation with constraints and communication delays

Motivated by the multi-sensor estimation problem, we consider a distributed constraint parameter estimation problem on a directed graph. Our goal is to design a fast distributed algorithm to handle constraint parameter estimation problems, especially considering the communication delays when sensors interact on a directed graph. Based on the gradient-tracking algorithm and indirect projection method, the Projected Distributed Parameter Estimation Algorithm (PDPEA) is proposed, which can deal with the closed convex set constraint and use the historical information stored by the sensors to achieve fast and accurate mean gradient estimation. The analysis shows that the PDPEA converges to a global and consensual minimizer even under the influence of measurement noise, arbitrarily bounded communication delays, and directed information topologies. Under the assumption that the local cost function is strongly convex and smooth, we show that the algorithm converges at a linear rate for an appropriate choice of stepsize. Lastly, we demonstrate the effectiveness of the proposed algorithm by setting up a multi-sensor parameter estimation network.

Multi-Sensor Estimation for Unreliable Wireless Networks with Contention-Based Protocols

The state estimation plays an irreplaceable role in many real applications since it lays the foundation for decision-making and control. This paper studies the multi-sensor estimation problem for a contention-based unreliable wireless network. At each time step, no more than one sensor can communicate with the base station due to the potential contention and collision. In addition, data packets may be lost during transmission since wireless channels are unreliable. A novel packet arrival model is proposed which simultaneously takes into account the above two issues. Two scenarios of wireless sensor networks (WSNs) are considered: the sensors transmit the raw measurements directly and the sensors send the local estimation instead. Based on the obtained packet arrival model, necessary and sufficient stability conditions of the estimation at the base station side are provided for both network scenarios. In particular, all offered stability conditions are expressed by simple inequalities in terms of the packet arrival rates and the spectral radius of the system matrix. Their relationships with existing related results are also discussed. Finally, the proposed results are demonstrated by simulation examples and an environment monitoring prototype system.

Multisensor estimation: New distributed algorithms

The multisensor estimation problem is considered in this paper. New distributed algorithms, which are able to locally process the information and which deliver identical results to those generated by their centralized counterparts are presented. The algorithms can be used to provide robust and computationally efficient solutions to the multisensor estimation problem. The proposed distributed algorithms are theoretically interesting and computationally attractive.

Comparative computational analysis of a new per-sample partitioning linear filter and the Kalman filter

The linear estimation problem constitutes one of the simplest classes of problems, to which the unifying and powerful partitioning approach of Lainiotis applies. In the Lainiotis and Andrisani partitioning algorithm, model partitioning consists simply of decomposing both the initial state and process noise vectors into the sum of two independent random vectors. In this paper, a comparative computational analysis with respect to computer time and storage requirements is made between a new partitioning linear estimation algorithm and the Kalman filter for time varying models. The new algorithm constitutes a per-sample version of the above Lainiotis and Andrisani partitioning algorithm. The practical usefulness of the new algorithm is demonstrated by applying the comparative computational analysis to the important class of multisensor estimation problems.

A new per-sample partitioning filter

The partitioning approach essentially constitutes an adaptive framework which is a unifying and powerful setting for optimal linear and nonlinear estimation. In particular, for the class of linear estimation problems, model partitioning has yielded [Lainiotis (1971–1979) and Lainiotis and Andrisani (1979, 1983)] two novel and practically useful estimation algorithms. The first algorithm results by partitioning the initial state vector into the sum of two independent random vectors, arbitrarily chosen by the filter designer. Similarly, the second algorithm results by partitioning both the initial state, and the process-noise vectors, respectively, into the sum of two random vectors. These algorithms were shown to posses several computational advantages, and interesting properties. In this paper, based on the above results, a new linear estimation algorithm is obtained. This constitutes a per-sample partitioning version of the above Lainiotis-Andrisani algorithm. Moreover, a computational analysis of the new algorithm is made with respect to computer time and storage requirements. This is compared to the computational requirements of the Kalman filter, and relevant conclusions are drawn. Finally, to demonstrate the practical usefulness of the new algorithm to signal processing, they are applied to the important class of multi-sensor estimation problems, encountered in geophysical data processing, multi-radar problems, etc.