Optimal Kalman Filter With Information-Weighted Consensus

• 成果类型: Article
• 署名作者: Khan, Shiraz; Deshmukh, Raj; Hwang, Inseok
• 署名单位: Purdue University System; Purdue University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3220528
• 发表日期: 2023
• 页码: 5624-5629
• 关键词: Kalman filters; State estimation; Robot sensing systems; mathematical models; Filtering algorithms; Information filters; estimation error; consensus; Distributed state estimation; multiagent systems; wireless sensor networks

摘要

The use of wireless sensor networks for distributed state estimation has been a popular research topic in the signal processing community. However, there is a distinct lack of emphasis on formal derivation and optimality of distributed state estimation algorithms in the literature. Furthermore, many existing algorithms utilize unweighted average consensus filtering, which has been shown to lead to poor estimation performance in the presence of sensor agents that cannot make measurements due to environmental obstructions or sensor limitations. In this article, a novel distributed minimum mean-squared error estimator is developed by generalizing the Kalman consensus filter to incorporate consensus on a weighted directed graph. By employing weighted consensus, the algorithm is able to achieve a directional flow of information in heterogeneous sensor networks, leading to improved performance in the presence of sensors that have low observability. Unlike several existing algorithms, the proposed algorithm does not rely on approximations or ad hoc parameter tuning and achieves optimal performance in a fully distributed setting. Through numerical simulations, it is demonstrated that the proposed algorithm has a smaller mean-squared estimation error and is robust in the aforementioned scenarios.