Performance evaluation of track fusion with information matrix filter

Chang, Kuo‐Chu; Tian, Zhigang; Saha, Rajat

doi:10.1109/taes.2002.1008979

Cited by 122 publications

(76 citation statements)

References 7 publications

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“…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], …”

Section: Msdf Systemsmentioning

confidence: 99%

“…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]. …”

Section: Msdf Systemsmentioning

confidence: 99%

“…Perform track fusion optimally for a multiple-sensor system with a specific processing architecture is treated in [295]. Other work cited in Table 2.11 are [338], [15,22,23,24], [16], [17], [19], [21], [20], [71], [72], [73]- [74], [75]- [76], [77], [78]- [79], [124], [126], [260], [261,262], [264,265], [266], [296], [303], [305] and [306]. [266] • Perform track fusion optimally for a multiple-sensor system with a specific processing architecture [295] • Track-to-track fusion for multi-sensor data fusion [296] • Common process noise on the two-sensor fused-track covariance [303] • Track association and track fusion with non-deterministic target dynamics [305] • Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion [306] 2.9.…”

Section: Msdf Systemsmentioning

confidence: 99%

“…As for the performance of different algorithms, [404] shows that the performance of weighted covariance algorithm is consistently worse as compared to the measurement fusion method. Moreover, it has been pointed out in [405] that results of weighted covariance algorithm are showing the behavior to be a maximum likelihood estimate. At the same time, Chang indicates that information matrix approach is optimal when the tracking systems are deterministic (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Kalman filtering

Mahmoud¹

2012

Distributed Control and Filtering for Industrial Systems

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Section: Msdf Systemsmentioning

confidence: 99%

Section: Msdf Systemsmentioning

confidence: 99%

Section: Msdf Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Kalman filtering

Mahmoud¹

2012

Distributed Control and Filtering for Industrial Systems

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“…Decision level fusion involves combining results obtained separately from all data sources. An ensemble of classifiers is one such example, as is track fusion [7]. The advantage of these two…”

Section: Introductionmentioning

confidence: 99%

Fusion and inference from multiple data sources in a commensurate space

Marchette

Priebe

2012

Statistical Analysis

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Given objects measured under multiple conditions-for example, indoor lighting versus outdoor lighting for face recognition, multiple language translation for document matching, etc.-the challenging task is to perform data fusion and utilize all the available information for inferential purposes. We consider two exploitation tasks: (i) how to determine whether a set of feature vectors represent a single object measured under different conditions; and (ii) how to create a classifier based on training data from one condition in order to classify objects measured under other conditions. The key to both problems is to transform data from multiple conditions into one commensurate space, where the (transformed) feature vectors are comparable and would be treated as if they were collected under the same condition. Toward this end, we studied Procrustes analysis and developed a new approach, which uses the interpoint dissimilarities for each condition. We impute the dissimilarities between measurements of different conditions to create one omnibus dissimilarity matrix, which is then embedded into Euclidean space. We illustrate our methodology on English and French documents collected from Wikipedia, demonstrating superior performance compared to that obtained via standard Procrustes transformation. 

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Optimal and self‐tuning weighted measurement fusion Kalman filters and their asymptotic global optimality

Gao

Ran

Sun

et al. 2010

Adaptive Control & Signal

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For the multisensor linear discrete time-invariant stochastic systems with unknown noise variances, using the correlation method, the information fusion noise variance estimators with consistency are given by taking the average of the local noise variance estimators. Substituting them into two optimal weighted measurement fusion steady-state Kalman filters, two new self-tuning weighted measurement fusion Kalman filters with a self-tuning Riccati equation are presented. By the dynamic variance error system analysis (DVESA) method, it is rigorously proved that the self-tuning Riccati equation converges to the steady-state optimal Riccati equation. Further, by the dynamic error system analysis (DESA) method, it is proved that the steady-state optimal and self-tuning Kalman fusers converge to the global optimal centralized Kalman fuser, so that they have the asymptotic global optimality. Compared with the centralized Kalman fuser, they can significantly reduce the computational burden. A simulation example for the target tracking systems shows their effectiveness.

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Performance evaluation of track fusion with information matrix filter

Cited by 122 publications

References 7 publications

Distributed Kalman filtering

Distributed Kalman filtering

Fusion and inference from multiple data sources in a commensurate space

Optimal and self‐tuning weighted measurement fusion Kalman filters and their asymptotic global optimality

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