The covariance intersection fusion estimation algorithm weighted by diagonal matrix based on genetic simulated annealing algorithm and machine learning

Liu, Jingang; Hao, Gang

doi:10.1002/asjc.2941

Cited by 5 publications

(2 citation statements)

References 58 publications

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“…However, despite not being optimal, the distributed fusion method can reduce computational burden, and it is suitable for fault detection and isolation [2,3]. Thus, the distributed fusion method has been widely used with the development of networked system over the past two decades, and the three fused filters weighted by matrix, diagonal matrix, and scalar are presented based on linear unbiased minimum variance (LUMV) in [2][3][4], the covariance intersection (CI) fusion algorithm and its derivations are presented in [5][6][7] that can avoid computing local cross-covariance.…”

Section: Introductionmentioning

confidence: 99%

“…The system noise variance is assumed to be exactly known in [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] but usually uncertain in many applications. For example, the measurement noise of global positioning system (GPS) will be changed with ambient temperature and instability of GPS signal, and the system noise variance is difficult to determine due to the speed of mobile terminal and transmission medium in mobile communication.…”

Section: Introductionmentioning

confidence: 99%

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Robust weighted fusion Kalman filters under linearly correlated noise and mixed uncertainties of noise variances, multiplicative noises, and multiple networked inducements

Yang,

Zhao,

Liu

2023

Asian Journal of Control

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The paper solves the robust weighted fusion Kalman filtering problem for systems with linearly correlated noise and mixed uncertainties of noise variances, multiplicative noises, and multiple networked inducements including missing measurements, packets dropouts, and two‐step random measurement delays. It is assumed that system noise variances are uncertain but bounded above, and the other four uncertainties are compensated to fictitious white noise by the proposed model‐transformation method. For the transformed local multimodel system with correlated fictitious noise, the robust local time‐varying recursive Kalman filters are presented by decorrelation technique and minimax robust filtering principle. Then the six weighted fusion robust Kalman filters are presented in a unified form. The robustness of local and fused robust Kalman filters is proved by the extended Lyapunov equation approach, matrix factorization, and elementary transformation. Further, the local and fused steady‐state robust Kalman filters are designed. Finally, a simulation study applied to F404 aircraft engine system is provided to examine effectiveness and applicability of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust weighted fusion Kalman filters under linearly correlated noise and mixed uncertainties of noise variances, multiplicative noises, and multiple networked inducements

Yang,

Zhao,

Liu

2023

Asian Journal of Control

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Recursive multi‐sensor fusion estimation under the coding‐based relay network

Zhang,

Liu,

Deng

2024

Asian Journal of Control

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This paper studies the recursive fusion estimation issue for a class of linear time‐varying multi‐sensor systems with amplify‐and‐forward (AF) relays. The AF relay is located between the sensor and the estimator to forward measurement signals for facilitating long‐distance transmission. A binary encoding scheme is used to regulate signal transmission via a digital network where the signal is encoded into a bit string and decoded at the endpoint of the receiver. Because of the uncertainty of the network and the existence of channel noises, a set of Bernoulli distributed random variables is introduced to characterize the random bit flip phenomenon. The purpose of the addressed problem is to design a fusion estimator to simultaneously reflect the impact of multiplicative noises, the AF relay, the binary encoding scheme, and the random bit flip on the filtering error covariance. First of all, an upper bound for the filtering error covariance of the local estimator can be acquired, and by minimizing such an upper bound, the parametric form of the gain matrix is obtained. Subsequently, the fusion estimation is given based on the covariance intersection fusion strategy. Finally, a simulation example is presented to verify the effectiveness of the proposed multi‐sensor fusion estimation.

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Data fusion method for temperature monitoring of bio-oxidation with wireless sensor networks

LI,

NAN,

CAI

et al. 2024

Measurement

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The covariance intersection fusion estimation algorithm weighted by diagonal matrix based on genetic simulated annealing algorithm and machine learning

Cited by 5 publications

References 58 publications

Robust weighted fusion Kalman filters under linearly correlated noise and mixed uncertainties of noise variances, multiplicative noises, and multiple networked inducements

Robust weighted fusion Kalman filters under linearly correlated noise and mixed uncertainties of noise variances, multiplicative noises, and multiple networked inducements

Recursive multi‐sensor fusion estimation under the coding‐based relay network

Data fusion method for temperature monitoring of bio-oxidation with wireless sensor networks

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