Unknown biases or perturbations in the INS/GNSS integrated navigation system may produce unforeseeable negative effects when the navigation states are estimated by using the Kalman filtering and its variants. To mitigate these undesirable effects in the INS/GNSS integrated navigation, a novel partially strong tracking extended consider Kalman filtering (PSTECKF) is proposed. In the presented PSTECKF algorithm, the biases are not estimated, but their covariance and co-covariance are incorporated into the state estimation covariance by using a nonlinear ''consider'' approach. Based on the above, the PSTECKF also partially introduces an adaptive fading factor into the predicted covariance of the states, which excludes the co-covariance between the states and biases, to compensate the nonlinear approximation errors and navigation system covariance uncertainties. Simulation results demonstrate the performance of the proposed PSTECKF for INS/GNSS integrated navigation is superior to that of the EKF and ECKF when the biases or perturbations happen in a navigation system.INDEX TERMS INS/GNSS integrated navigation, consider Kalman filter, adaptive filtering, bias, strong tracking.
In order to counteract the adverse effects of biases in direct inertial navigation system/global navigation satellite system integration, a novel robust partly strong tracking consider state-dependent Riccati equation filter (PSTCSDREF) algorithm is proposed. A nonlinear ‘consider’ approach is utilized to incorporate bias statistics into a state estimation error covariance of the state-dependent Riccati equation filter (SDREF), and a new consider SDREF (CSDREF) is proposed. Next, the prediction covariance of the state is partly multiplied by a strong tracking factor, which does not include bias covariance, so as to mitigate the adverse effects of model uncertainties on navigation performance. Numerical simulation demonstrates that the proposed PSTCSDREF demonstrates better navigational accuracy as compared with the extended Kalman filter, SDREF, and CSDREF.
For solving the problem of quality detection in the production and processing of stuffed food, this paper suggests a small neighborhood clustering algorithm to segment the frozen dumpling image on the conveyor belt, which can effectively improve the qualified rate of food quality. This method builds feature vectors by obtaining the image's attribute parameters. The image is segmented by a distance function between categories using a small neighborhood clustering algorithm based on sample feature vectors to calculate the cluster centers. Moreover, this paper gives the selection of optimal segmentation points and sampling rate, calculates the optimal sampling rate, suggests a search method for optimal sampling rate, as well as a validity judgment function for segmentation. Optimized small neighborhood clustering (OSNC) algorithm uses the fast frozen dumpling image as a sample for continuous image target segmentation experiments. The experimental results show the accuracy of defect detection of OSNC algorithm is 95.9%. Compared with other existing segmentation algorithms, OSNC algorithm has stronger anti-interference ability, faster segmentation speed as well as more efficiently saves key information ability. It can effectively improve some disadvantages of other segmentation algorithms.
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