Abstract
Aiming at the GNSS receiver vulnerability in challenging urban environments and low power consumption of integrated navigation systems, an improved robust adaptive Kalman filter (IRAKF) algorithm with real-time performance and low computation complexity for single-frequency GNSS/MEMS-IMU/odometer integrated navigation module is proposed. The algorithm obtains the scale factor by the prediction residual, and uses it to adjust the artificially set covariance matrix of the observation vector under different GNSS solution states, so that the covariance matrix of the observation vector changes continuously with the complex scene. Then, the adaptive factor is calculated by the Mahalanobis distance to inflate the state prediction covariance matrix. In addition, the one-step prediction Kalman filter is introduced to reduce the computational complexity of the algorithm. The performance of the algorithm is verified by vehicle experiments in the challenging urban environments. Experiments show that the algorithm can effectively weaken the effects of abnormal model deviations and outliers in the measurements and improve the positioning accuracy of real-time integrated navigation. It can meet the requirements of low power consumption real-time vehicle navigation applications in the complex urban environment.
Highlights
The Mahalanobis distance based on the prediction residual is used to detect disturbance anomalies and obtain the adaptive factor to inflate the state prediction covariance matrix
This paper focuses on improving positioning accuracy and reducing the power consumption of low-cost integrated navigation and positioning modules in complex scenes to be widely used in vehicle navigation
According to the presented results, the improved robust adaptive Kalman filter (IRAKF) applied to single-frequency Global navigation satellite system (GNSS)/MEMS-IMU/odometer integrated navigation in the challenging urban environment can improve the robustness ability of the system and significantly reduce the computational complexity of the algorithm
Summary
Global navigation satellite system (GNSS) has high positioning accuracy and no cumulative error. The GNSS receiver needs at least four satellites to be able to locate [1]. In the environment of avenues, viaducts, urban canyons, and tunnels, the satellite signal is vulnerable to occlusion and multipath effects [2]. The GNSS receiver with the real-time kinematic (RTK) algorithm is prone to losing lock in the complex environment. It takes several seconds to fix the integer ambiguity when reentering the open environment, which makes the continuity and availability of GNSS positioning not guaranteed. Inertial navigation has high short-term positioning accuracy and is not restricted by the environment. The INS positioning accuracy will diverge exponentially with time due to the errors of device noise, zero offset, scale factor, axis deviation, and nonlinearity [3]
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