Improved LANDMARC Downhole Positioning Algorithm Based on Adaptive CKF
In order to solve the problem of low accuracy and large fluctuation of positioning results caused by the tradi-tional LANDMARC algorithm due to the complex environmental environment of the mine,an improved LANDMARC under-ground positioning algorithm based on adaptive CKF is proposed.Firstly,the algorithm combines the traditional LANDMARC positioning algorithm to establish a downhole three-dimensional spatial model and solve the target location state estimation.Sec-ondly,using the generalization mapping ability of BP neural network,neuronal parameters are introduced to optimize the volu-metric Kalman filter(CKF),which fully combines the characteristics of BP neural network iterative learning and volumetric Kalman filter(CKF)to maintain stability in a strong nonlinear system,and improve the adaptive ability of the positioning algo-rithm.Finally,the location state estimation is used as an observation measurement for adaptive CKF filtering processing,and the optimized result is used as the true value output of the target location coordinates,which improves the accuracy of downhole po-sitioning.The experimental results show that the introduction of adaptive CKF for filtering can greatly improve the stability of the traditional LANDMARC positioning algorithm,and the distribution of localization deviation is more concentrated,and the deviation below 1 m accounts for more than 90%.The positioning deviation of the proposed algorithm reaches 60%for labels below 0.612 m,which can meet the high stability requirements of the complex dynamic environment of downhole,and has bet-ter applicability to downhole positioning compared with the traditional LANDMARC positioning algorithm and the LANDMARC positioning algorithm filtered by HIF.
万方数据
维普
