Adaptive signal fusion based on relative fluctuations of variable signals

Meng, Zong; Zhang, Pan; Chen, Zijun; Shi, Ying

doi:10.1016/j.measurement.2019.106909

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…Tong et al 16 proposed an adaptive visible light and infrared image fusion method, which can adaptively adjust the coefficient fusion weight to obtain a better image. Meng et al 17 proposed an improved adaptive random weighted data fusion algorithm by introducing an equalization factor to adjust the proportional relationship between the current measurement value and the historical measurement value, which can be very close to the estimation of the true value.…”

Section: Introductionmentioning

confidence: 99%

Front vehicle detection based on improved fusion method for lidar and visual image

Chen

Ning

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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In order to ensure the detection accuracy, an improved adaptive weighted (IAW) method is proposed in this paper to fuse the data of images and lidar sensors for the vehicle object’s detection. Firstly, the IAW method is proposed in this paper and the first simulation is conducted. The unification of two sensors’ time and space should be completed at first. The traditional adaptive weighted average method (AWA) will amplify the noise in the fusion process, so the data filtered with Kalman Filter (KF) algorithm instead of with the AWA method. The proposed IAW method is compared with the AWA method and the Distributed Weighted fusion KF algorithm in the data fusion simulation to verify the superiority of the proposed algorithm. Secondly, the second simulation is conducted to verify the robustness and accuracy of the IAW algorithm. In the two experimental scenarios of sparse and dense vehicles, the vehicle detection based on image and lidar is completed, respectively. The detection data is correlated and merged through the IAW method, and the results show that the IAW method can correctly associate and fuse the data of the two sensors. Finally, the real vehicle test of object vehicle detection in different environments is carried out. The IAW method, the KF algorithm, and the Distributed Weighted fusion KF algorithm are used to complete the target vehicle detection in the real vehicle, respectively. The advantages of the two sensors can give full play, and the misdetection of the target objects can be reduced with proposed method. It has great potential in the application of object acquisition.

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Section: Introductionmentioning

confidence: 99%

Front vehicle detection based on improved fusion method for lidar and visual image

Chen

Ning

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…Among the numerous methods of processing dynamic measurements under noisey conditions, focused on various specific features of certain measured processes [3,[11][12][13], the most universal and effective today are the methods of the stochastic filtration theory [4,11,[14][15][16][17][18][19], which provide an optimal evaluation of the measured process by a given criterion.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Stochastic Filtration Based on the Estimation of the Covariance Matrix of Measurement Noises Using Irregular Accurate Observations

2021

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In measurement systems operating under various disturbances the probabilistic characteristics of measurement noises are usually known approximately. To improve the observation accuracy, a new approach to the Kalman’s filter adaptation is proposed. In this approach, the Covariance Matrix of Measurement Noises (CMMN) is estimated by accurate measurements detected irregularly by the mobile object observation system (from radiofrequency identifiers, etalon reference, fixed points etc.). The problem of adaptive estimation of the observer’s noises covariance matrix in the Kalman filter is solved analytically for two cases: mutual noises correlation, and its absence. The numerical example for adaptive filtration of complexing navigation system parameters of a mobile object using irregular accurate measurements is given to illustrate the effectiveness of the proposed algorithm. Coordinate estimating errors have changed in comparison with the traditional scheme from 100 m to 2 m in latitude, and from 200 m to 1.5 m in longitude.

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High-precision bearing signal recovery based on signal fusion and variable stepsize forward-backward pursuit

Zhang

Meng

Ying

et al. 2021

Mechanical Systems and Signal Processing

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Adaptive signal fusion based on relative fluctuations of variable signals

Cited by 4 publications

References 28 publications

Front vehicle detection based on improved fusion method for lidar and visual image

Front vehicle detection based on improved fusion method for lidar and visual image

Adaptive Stochastic Filtration Based on the Estimation of the Covariance Matrix of Measurement Noises Using Irregular Accurate Observations

High-precision bearing signal recovery based on signal fusion and variable stepsize forward-backward pursuit

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