Asynchronous Multisensor Data Fusion Based on Minimum Trace of Error Covariance

Hui-dong, Guo; Zhang, Xinhua; Xu, Li; Yuan, Shuiqiao; Xu, Chen; Tang, Shaobo

doi:10.1109/icif.2006.301673

Cited by 6 publications

(1 citation statement)

References 4 publications

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“…Some asynchronous fusion algorithms introduce some traditional data registration methods to the fusion systems for realizing the synchronization of asynchronous data before fusion process, such as the interpolation, extrapolation, least squares method and so on. Some fusion algorithms deal with asynchronous data on the basis of its receiving time, then select a proper fusion method for this asynchronous data fusion, such as asynchronous fusion algorithms based on information matrix [8], [9], asynchronous track fusion algorithm under the principle of minimum error covariance matrix trace [10] − [12], time-varying bias estimation for asynchronous multi-sensor multi-target tracking systems [13], distributed weighted fusion estimators with random delays [14] and Step by Step Prediction Fusion based on Asynchronous Multi-sensor System (SSPFA) [15], [16], etc. The SSPFA algorithm mainly uses the multi-sensor's measurement information in a data fusion cycle to get the filtering estimation, to obtain the local state estimation and the corresponding error covariance of each sensor at the last moment of data fusion cycle.…”

Section: Introductionmentioning

confidence: 99%

An Asynchronous Data Fusion Algorithm for Target Detection Based on Multi-Sensor Networks

Zhang

Wang²,

Guo

et al. 2020

IEEE Access

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The time interval of the observational data changes irregularly because of the difference of sensors' sampling rate, the communication delay and the target leaving observation region of the sensor sometimes. These problems of asynchronous observation data greatly reduce the tracking accuracy of the multi-sensors system. Therefore, asynchronous data fusion system is more practical than synchronous data fusion system, and worthier of study. By establishing an asynchronous track fusion model with irregular time interval of observation data and combining with the Track Quality with Multiple Model (TQMM), an asynchronous track fusion algorithm with information feedback is proposed, and the TQMM is used for weight allocation to improve the performance of the asynchronous multi-sensor fusion system. The simulation result shows that the algorithm has better tracking performance compared with other algorithms, so that this kind of problem of track-to-track fusion for asynchronous sensors is solved effectively. INDEX TERMS Asynchronous fusion, multi sensors, track fusion, track quality with multiple model.

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Section: Introductionmentioning

confidence: 99%

An Asynchronous Data Fusion Algorithm for Target Detection Based on Multi-Sensor Networks

Zhang

Wang²,

Guo

et al. 2020

IEEE Access

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Secure State Estimation with Asynchronous Measurements against Malicious Measurement-Data and Time-Stamp Manipulation

Li,

Nguyen,

Teixeira

et al. 2023

2023 62nd IEEE Conference on Decision and Control (CDC)

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Asynchronous track fusion with global feedback

Wen

Liu

et al. 2008

2008 Third International Conference on Communications and Networking in China

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Asynchronous fusion is one of the barriers among multisensor data fusion, and asynchronous track fusion is an important research aspect of asynchronous fusion for its practicability in application. At present, local prediction weighted fusion method is used extensively in processing asynchronous track fusion. In the current algorithms, the global fusion estimate isn't obtained by local sensors; thereby the local estimate can't be improved by the global estimate. This is because that there is no feedback communication link between the fusion center and local sensors, accordingly, the performance of the track fusion system is reduced. In order to improve the estimate precision of the fusion system, the global feedback is introduced in this paper, and the corresponding asynchronous track fusion algorithm is presented. Compared with the current algorithm without feedback, the proposed algorithm can effectively improve the estimate precision not only in local sensors but also in the fusion center, and the proofs are given in the appendix. The simulations and algorithm analysis both show the advantages of the novel algorithm.

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Asynchronous Multisensor Data Fusion Based on Minimum Trace of Error Covariance

Cited by 6 publications

References 4 publications

An Asynchronous Data Fusion Algorithm for Target Detection Based on Multi-Sensor Networks

An Asynchronous Data Fusion Algorithm for Target Detection Based on Multi-Sensor Networks

Secure State Estimation with Asynchronous Measurements against Malicious Measurement-Data and Time-Stamp Manipulation

Asynchronous track fusion with global feedback

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