The parameter optimization of kalman filter based on multi-objective memetic algorithm

Huo, Yu Dan; Cai, Zhi Hua; Gong, Wen Yin; Liu, Qin

doi:10.1145/2576768.2598242

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…Since the real RSS vector at any moment is not available, the initial value P 0 of P t−1 is difficult to obtain. Generally, when P 0 is not a zero matrix, it will not affect the convergence of AKF [18].…”

Section: Amendatory Akf Methods To Reduce Noisementioning

confidence: 99%

Wireless Localization Method Based on AHP‐WKNN and Amendatory AKF

Hua

Pan

et al. 2021

Wireless Communications and Mobile Computing

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In this paper, we propose an AHP-WKNN method for indoor localization which combines the Analytic Hierarchy Process (AHP) technique and the Weighted K -nearest Neighbor (WKNN) algorithm. AHP serves to assign weights when WKNN is employed to select fingerprints for indoor positioning. The AHP technique can reasonably enlarge the influence that the received signal strength (RSS) gap between reference points has on the weights, achieving better performance in positioning. This paper also modifies the adaptive Kalman filter (AKF) noise reduction method by correcting the output based on the error between the RSS measurement and the expected output. The modified AKF can track the changes of RSS more effectively and achieve better performance of noise reduction. The simulation result shows that the proposed AHP-WKNN method and the modified AKF can improve positioning accuracy effectively.

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Section: Amendatory Akf Methods To Reduce Noisementioning

confidence: 99%

Wireless Localization Method Based on AHP‐WKNN and Amendatory AKF

Hua

Pan

et al. 2021

Wireless Communications and Mobile Computing

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“…The Trial-and-error method for EKF tuning is commonly used but can be tedious. To overcome this situation, heuristic approaches, such as a Memetic algorithm [22], PSO algorithm [20] or Genetic algorithm [23] were used to automatically optimize both the state and measurement noise matrices. In this work, the cyclic method [18], which is a simple and an efficient technique, is applied to determine the optimal state and measurement noises.…”

Section: Application Of Cyclic Methods For Tuning Ekfmentioning

confidence: 99%

Real time observer-based stator fault diagnosis for IM

Merraoui¹,

Ferdjouni²,

Bounekhla³

2020

IJECE

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This paper proposes a delta connected IM model that takes the Stator winding Inter-Turn Short Circuit (SITSC) fault into account. In order to detect the fault and evaluate its severity, an observer based FDI method is suggested. It allows the generation of residual using extended Kalman filter (EKF). To overcome the problem of the EKF initialization, the cyclic optimization method is applied to determine its tuning parameters. The advantage of the proposed approach is the real-time quantification of the fault severity and the quick fault detection. Using numerical simulation under both the healthy and the faulty conditions, the proposed IM model and EKF-based FDI approach are confirmed. Experimental results obtained by a real-time implementation on test-bench validate the simulated results.

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“…A highly popular MA for solving optimisation problems is used to identify the fingerprint estimate with the optimum weight, which does not require an accurate estimation of the initial node location and can avoid falling into local optimisation so as to achieve the global optimum compared with other optimisation algorithms such as least square optimisation [33–35]. The main steps of the MA algorithm are as follows: Step 1.…”

Section: Proposed Algorithmmentioning

confidence: 99%