VEHICLE NAVIGATION ON THE BASIS OF SATELLITE POSITIONING DATA AND VEHICLE SENSOR DATA
First Claim
1. A method for vehicle navigation, comprising:
- obtaining satellite positioning data from a satellite positioning device of a vehicle;
obtaining vehicle sensor data from a number of sensors of the vehicle; and
combining the satellite positioning data and the vehicle sensor data by a Kalman filter to obtain a combined state vector estimate of the vehicle;
wherein the Kalman filter comprisesa first filter which receives the satellite positioning data and generates a first state vector estimate of the vehicle and a corresponding first state error covariance matrix,a second filter which receives the vehicle sensor data and generates a second state vector estimate of the vehicle and a corresponding second state error covariance matrix, anda third filter which receives the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix and generates the combined state vector estimate and a corresponding combined state error covariance matrix,wherein the third filter comprises a prediction processor which generates a predicted state vector estimate on the basis of the combined state vector estimate and a predicted state error covariance matrix on the basis of the combined state error covariance matrix, andwherein the predicted state vector estimate and the predicted state error covariance matrix are fed back to the first filter, the second filter, and the third filter.
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Accused Products
Abstract
A vehicle navigation system includes a Kalman filter having a first filter receiving satellite positioning data and a second filter receiving vehicle sensor data. The first filter generates a first state vector estimate and a corresponding first state error covariance matrix. The second filter generates a second state vector estimate and a corresponding second state error covariance matrix. A third filter receives the first and second state vector estimates and the first and second state error covariance matrices, and generates a combined state vector estimate and a corresponding combined state error covariance matrix. A prediction processor generates a predicted state vector estimate and a predicted state error covariance matrix from the combined state vector estimate and the combined state error covariance matrix. The predicted state vector estimate and the predicted state error covariance matrix are provided to the first filter, the second filter, and the third filter.
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Citations
25 Claims
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1. A method for vehicle navigation, comprising:
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obtaining satellite positioning data from a satellite positioning device of a vehicle; obtaining vehicle sensor data from a number of sensors of the vehicle; and combining the satellite positioning data and the vehicle sensor data by a Kalman filter to obtain a combined state vector estimate of the vehicle; wherein the Kalman filter comprises a first filter which receives the satellite positioning data and generates a first state vector estimate of the vehicle and a corresponding first state error covariance matrix, a second filter which receives the vehicle sensor data and generates a second state vector estimate of the vehicle and a corresponding second state error covariance matrix, and a third filter which receives the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix and generates the combined state vector estimate and a corresponding combined state error covariance matrix, wherein the third filter comprises a prediction processor which generates a predicted state vector estimate on the basis of the combined state vector estimate and a predicted state error covariance matrix on the basis of the combined state error covariance matrix, and wherein the predicted state vector estimate and the predicted state error covariance matrix are fed back to the first filter, the second filter, and the third filter. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A navigation system for a vehicle, comprising:
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a satellite positioning device configured to obtain satellite positioning data; and a Kalman filter configured to obtain a combined state vector estimate of the vehicle by combining the satellite positioning data and vehicle sensor data received from a number of vehicle sensors of the vehicle, wherein the Kalman filter comprises a first filter configured to receive the satellite positioning data and generate a first state vector estimate of the vehicle and a corresponding first state error covariance matrix, a second filter configured to receive the vehicle sensor data and generate a second state vector estimate of the vehicle and a corresponding second state error covariance matrix, and a third filter configured to receive the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix and generate the combined state vector estimate and a corresponding combined state error covariance matrix, wherein the third filter comprises a prediction processor configured to generate a predicted state vector estimate on the basis of the combined state vector estimate and a predicted state error covariance matrix on the basis of the combined state error covariance matrix, and wherein the Kalman filter comprises a feedback arrangement configured to feed back the predicted state vector estimate and the predicted state error covariance matrix to the first filter, the second filter, and the third filter. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. A vehicle comprising a navigation system and a number of vehicle sensors providing vehicle sensor data, the navigation system comprising:
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a satellite positioning device configured to obtain satellite positioning data; and a Kalman filter configured to obtain a combined state vector estimate of the vehicle by combining the satellite positioning data and the vehicle sensor data received from the vehicle sensors of the vehicle, wherein the Kalman filter comprises a first filter configured to receive the satellite positioning data and generate a first state vector estimate of the vehicle and a corresponding first state error covariance matrix, a second filter configured to receive the vehicle sensor data and generate a second state vector estimate of the vehicle and a corresponding second state error covariance matrix, and a third filter configured to receive the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix and generate the combined state vector estimate and a corresponding combined state error covariance matrix, wherein the third filter comprises a prediction processor configured to generate a predicted state vector estimate on the basis of the combined state vector estimate and a predicted state error covariance matrix on the basis of the combined state error covariance matrix, and wherein the Kalman filter comprises a feedback arrangement configured to feed back the predicted state vector estimate and the predicted state error covariance matrix to the first filter, the second filter, and the third filter.
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Specification