{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:50:21Z","timestamp":1740149421291,"version":"3.37.3"},"reference-count":34,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T00:00:00Z","timestamp":1630195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"In this paper, an innovative optimal information fusion methodology based on adaptive and robust unscented Kalman filter (UKF) for multi-sensor nonlinear stochastic systems is proposed. Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive UKF scheme for the time-varying noise covariance is developed and serves as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations. Based on the hypothesis testing theory with the Mahalanobis distance, the new adaptive UKF scheme utilizes both the innovation and the residual sequences to adapt the process noise covariance timely. The results of the target tracking simulations indicate that the proposed method is effective under the condition of time-varying process-error and measurement noise covariance.<\/jats:p>","DOI":"10.3390\/s21175808","type":"journal-article","created":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T02:58:15Z","timestamp":1630465095000},"page":"5808","source":"Crossref","is-referenced-by-count":28,"title":["Adaptive Unscented Kalman Filter for Target Tacking with Time-Varying Noise Covariance Based on Multi-Sensor Information Fusion"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3319-3593","authenticated-orcid":false,"given":"Dapeng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian Distirct, Beijing 100083, China"}]},{"given":"Hai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian Distirct, Beijing 100083, China"},{"name":"Science and Technology on Aircraft Control Laboratory, Beihang University, No. 37 Xueyuan Road, Haidian Distirct, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7570-3948","authenticated-orcid":false,"given":"Baoshuang","family":"Ge","sequence":"additional","affiliation":[{"name":"Yancheng State-Owned Assets Investment Group Co., Ltd., No. 669 Century Avenue, Yandu District, Yancheng 224000, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1016\/j.automatica.2004.01.014","article-title":"Multi-sensor optimal information fusion Kalman filter","volume":"40","author":"Sun","year":"2004","journal-title":"Automatica"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.automatica.2018.11.043","article-title":"Globally optimal sequential and distributed fusion state estimation for multi-sensor systems with cross-correlated noises","volume":"101","author":"Lin","year":"2019","journal-title":"Automatica"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/S1566-2535(01)00019-7","article-title":"State estimation and multi-sensor data fusion using data-based neurofuzzy local linearization process models","volume":"2","author":"Chirs","year":"2001","journal-title":"Inf. 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