Multi-sensor optimal information fusion Kalman filter

Distributed Kalman filtering

“…Other related publications cited in the Table 2.8 are [67], [68], [91], [93,94], [95], [115], [116], [117], [124], [125], [127] and [128]. Other related publications cited in the Table 2.9 are [131], [169,170], [170,171,172], [183], [187], [200], [240], [241], [244], [249], [252], [253], [254,255,256], [257], [259], [275], [277], [278], [304] and [307]. …”

“…Other related publications cited in the Table 2.7 are [338]- [339], [337], [332], [333,334], [37], [46], [47], [48], [48], [60], [59,58], [57], [56], [55], [54], [53], [52]. Other related publications cited in the Table 2.8 are [67], [68], [91], [93,94], [95], [115], [116], [117], [124], [125], [127] and [128]. Other related publications cited in the Table 2.9 are [131], [169,170], [170,171,172], [183], [187], [200], [240], [241], [244], …”

“…Other DKF applications can be seen in [335], [336], [338], [339] . [38], [39], [40], [41], [42], [43], [44], [105], [106], [109], [114], [119], [156], [179], [191], [197], [213], [214], [215], [216], [221], [233], [237] , [238] and [242]. [204] • Only the estimates at each Kalman update over-head are exchanged [205] • Analyzes the number of messages to exchange between successive updates in DKF [206] • Global Optimality of DKF fusion exactly equal to the corresponding centralized optimal Kalman filtering fusion [276] • A parallel and distributed state estimation structure developed from an hierarchical estimation structure [297] • A computational procedure to transform an hierarchical Kalman filter into a partially decentralized estimation structure [298] • Optimal DKF based on a-priori determination of measurements [300] 2.3.…”

“…Other estimation methods can be a sensor-less approach [382] During estimation, the problem of multi-target tracking utilizing information from multiple sensors employed has been in focus since last many years [388]- [396]. While achieving this approach, many fusion algorithms and filters were derived to combine local estimates local estimates [397][398] [399][400] to prove better efficiency and effectiveness. For example, the state vectors can be fused using weighted covariance [406][407] [408], information matrix [401], and covariance intersection [402] [403].…”

Distributed Weighting Fusion and Covariance Intersection Fusion Kalman Smoother for Systems with Colored Measurement Noises

“…Unified fusion rules for the optimal linear estimation fusion and several distributed weighting state fusers were presented in [2][3][4][5], where the three distributed weighting fusers have the accuracy relations: the accuracy of the fuser weighted by matrices is higher than that of the fuser weighted by scalars, and the accuracy of the fuser weighted by diagonal matrices is between of them. However, all of the above weighting fusers have the limitation that in order to compute the optimal weights, the computation of the cross-covariances between the local estimation errors is required, while the cross-covariances are usually unknown [6] or their computation is very complex [7] in many applications.…”