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Aiming at the problem that the accuracy and stability of SINS/BDS integrated navigation system decrease due to uncertain model and observation anomalies, a SINS/BDS integrated navigation method based on classified weighted adaptive filtering is proposed. Firstly, the innovation covariance matching technology is used to detect whether there is any abnormality in the system as a whole. Then the types of anomalies are distinguished by hypothesis test. Different types of anomalies have different effects on state estimation. Based on the dynamic changes of innovation, different adaptive weighting methods are adopted to correct navigation information. The simulation results show that this method can effectively improve the fault-tolerant performance of integrated navigation system in complex environment with unknown anomaly types. When both model anomalies and observation anomalies exist, the speed and position accuracy are increased by 42% and 24% compared with the standard KF, 38% and 22% compared with the innovation orthogonal adaptive filtering, which has higher navigation accuracy.

The development of high-speed cruise aircraft puts higher demands on the accuracy and fault tolerance of navigation systems [

The online matching technology of innovation and its covariance matrix is widely used in the anomaly detection of navigation systems. Many literatures have carried out in-depth research and improvement [

The innovation variance can be used to test whether the system is abnormal. However, due to the uncertainty of interference factors, it is impossible to determine whether the anomaly that occurred is model anomaly or measurement anomaly. For the above problem, the previous references did not do too much analysis, but designed the fault-tolerant filtering algorithm by simply considering the innovation covariance matching error as a known type of anomaly. However, in the case where the type of the anomaly is uncertain, this method will seriously affect the accuracy of the navigation information and even produce the opposite fault-tolerant effect. So a SINS/BDS integrated navigation method based on classification weighted adaptive filtering is designed in this paper. Firstly, according to the orthogonality of the innovation, the method uses the innovation covariance matching technology to identify whether the system is abnormal. Based on the innovation covariance, the weighting factors are constructed. Then the type of the anomaly is further tested. According to the test results, different weighting methods are adopted to correct the filtering error caused by the uncertain factors. In addition, the vector-form weighting factors are used instead of the scalar-form weighting factors in the weighting process to further improve the accuracy of the navigation filtering.

The ENU geography coordinate system is selected as the basic coordinate system of the navigation solution, and the state equation is established as formula (

Take the difference between the position and velocity information of the INS and the BDS as the observation measurement, and establish the system measurement equation as formula (

Innovation

When the uncertainty causes the navigation system to measure abnormally, the measurement equation can be expressed as formula (

Based on the above analysis, the actual innovation covariance will deviate from the theoretical value regardless of whether the model is abnormal or the measurement is abnormal. The covariance matching method can be used to determine whether the system has an abnormality, but the type of the anomaly cannot be determined. In theory, when the measurement is abnormal, the gain is usually reduced to weaken the influence of the abnormal measurement on the filtering. When the model is abnormal, the gain is usually increased to enhance the correction effect of the measurement on the model error [

First of all, the one-step prediction error equation can be described as follows.

Firstly, the innovation covariance matching method is used to judge whether the system as a whole is abnormal. For the calculation of innovation covariance, a calculation method with sliding window can be used [

After the above judgment, when the whole system is abnormal; the next hypothesis test is carried out:

H0: There are anomalies in the measurements.

H1: There are anomalies in the system model.

The measurement error equation can be expressed as equation (

When

Otherwise, we regard the anomaly of navigation system as model anomaly and establish error function

The simulation parameters are set as follows:

The initial position of the aircraft is the north latitude 31.9°, the east longitude 106.5° and the height 1000m. The initial speed is 100m/s. The yaw angle, pitch angle, and the roll angle are 30°, 0°, 0° respectively. The error gyro constant drift is 0.1°/h(1

Flight path of the aircraft.

In order to verify the performance of the classified weighted adaptive filter (CWAF) proposed in this paper, it is assumed that both model and measurement anomalies exist in INS/BDS integrated navigation system during the simulation period. In the simulation time of 500s-520s, we set up anomalous measurements. In the time of 500s-510s, the mean square root of velocity measurement errors in east, north and up directions are 2m/s, -3m/s and 2m/s respectively. In 510s-520s, the velocity errors are 3m/s, -1m/s and-3m/s respectively. We set abnormal model errors in 1000s-1020s. For convenience of observation, we set the root mean square of one-step prediction position error and velocity error caused by abnormal system model as 2m, 2m, -2m, 0.2m/s, 0.2m/s and -0.2m/s, respectively. In this paper, the standard Kalman filter and the innovation orthogonality adaptive filtering algorithm(IOAF) proposed in [

Eastward velocity error comparison.

Northward velocity error comparison.

Upward velocity error Comparison.

Latitude error comparison.

Longitude error comparison.

Height error comparison.

It can be seen from Figures

Navigation information error data statistics.

Statistics | Filtering method | | | | | | |
---|---|---|---|---|---|---|---|

Standard deviation | Standard KF | 0.289 | 0.277 | 0.329 | 8.429 | 6.885 | 7.842 |

IOAF | 0.277 | 0.287 | 0.283 | 7.237 | 7.177 | 7.788 | |

CWAF | 0.166 | 0.144 | 0.213 | 5.319 | 5.177 | 7.211 |

In Table

Aiming at the problem that the filtering accuracy and stability of SINS/BDS integrated navigation system decrease due to uncertain model and observation anomalies, a new SINS/BDS integrated navigation method based on classified weighted adaptive filtering is proposed. Firstly, the types of anomalies in navigation system are distinguished by this method. Then, based on the analysis of the influence of model anomaly and observation anomaly on navigation filtering solution, different weighting methods are adopted for two types of system anomalies to improve the accuracy and adaptability of integrated navigation system. Finally, the simulation results show that the proposed method can effectively improve the fault-tolerant performance of integrated navigation system in complex environments with unknown types of anomalies. When both model and observation anomalies exist, the velocity and position accuracy of SINS/BDS integrated navigation system are improved by 42% and 24% compared with the standard KF, 38% and 22% compared with the innovation orthogonal adaptive filtering. It greatly improves the accuracy of SINS/BDS integrated navigation system and has high fault-tolerance and feasibility in complex environment with uncertainties.

The data used to support the findings of this study are included within the article.

The authors declare that there are no conflicts of interest regarding the publication of this paper.

This study was co-supported by the National Natural Science Foundation of China (Grant no. 61703424).