On state estimation for linear discrete-time systems with unknown noise covariances

Tugnait, J.K.; Haddad, A.

doi:10.1109/tac.1979.1101972

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…qv i is the constant speed of the i th sensor and is set to 0.5m/s. The initial position of the sensors are set to (18,27), (31, 43), (62, 41), (86, 33), (15,45), (13,98), (38, 105), (60, 99), (89, 93), (110, 106)], and the unit is meter. The communication ranges and the sensing ranges of the sensors are all set to r c = 45m and r s = 60m respectively.…”

Section: B Mobile Sensor Networkmentioning

confidence: 99%

“…The minimax approach provides a fixed system whose worst performance among an assumed possible uncertainty set is the best possible. The advantages and disadvantages of the approach have been discussed in [26], [27]. The subspace methods formulate the estimation problem as projections of Hankel matrices and the model can be retrieved from the row and column spaces of the projected data matrix.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Noise Covariance Matrices Estimation in Sensor Networks

Deng

2020

Preprint

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Adaptive algorithms based on in-network processing over networks are useful for online parameter estimation of historical data (e.g., noise covariance) in predictive control and machine learning areas. This paper focuses on the distributed noise covariance matrices estimation problem for multi-sensor linear time-invariant (LTI) systems. Conventional noise covariance estimation approaches, e.g., auto-covariance least squares (ALS) method, suffers from the lack of the sensor's historical measurements and thus produces high variance of the ALS estimate. To solve the problem, we propose the distributed auto-covariance least squares (D-ALS) algorithm based on the batch covariance intersection (BCI) method by enlarging the innovations from the neighbors. The accuracy analysis of D-ALS algorithm is given to show the decrease of the variance of the D-ALS estimate. The numerical results of cooperative target tracking tasks in static and mobile sensor networks are demonstrated to show the feasibility and superiority of the proposed D-ALS algorithm.

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Section: B Mobile Sensor Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Noise Covariance Matrices Estimation in Sensor Networks

Deng

2020

Preprint

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show abstract

“…Remark 2: According to the optimal MJLSs state estimation method from [31], given a specific overall mode sequence I k,i ∈ I k and an original measurement sequence denoted by…”

Section: Optimal Estimationmentioning

confidence: 99%