A New Data Fusion Method for GNSS/INS Integration Based on Weighted Multiple Criteria
Abstract
:1. Introduction
2. Related Theory of the Cubature Kalman Filter and the H∞ Filter
2.1. Models of the Cubature Kalman Filter
- a.
- Time update:
- b.
- Measurement update:
2.2. Principles of the H∞ Filter
- Time update:
- Measurement update:
3. Weighted-Multiple-Criteria-Based Data Fusion Algorithm for the GNSS/INS Integration
3.1. Loosely-Coupled GNSS/INS Integrated Navigation Systems
3.2. Weighted-Multiple-Criteria-Based Parameter Estimation Algorithm
3.3. Theoretical Analysis of the Proposed Parameter Estimation Algorithm
4. Test and Analysis
- Algorithm 1: the conventional cubature Kalman filter algorithm (marked as CKF);
- Algorithm 2: the nonlinear H∞ filter algorithm (marked as HF);
- Algorithm 3: the weighted-multiple-criteria-based filter algorithm (λ = 0.5, marked as MCF-0.5).
- Algorithm 4: the weighted-multiple-criteria-based filter algorithm (λ = 0.2, marked as MCF-0.2);
- Algorithm 5: the weighted-multiple-criteria-based filter algorithm (λ = 0.4, marked as MCF-0.4);
- Algorithm 6: the weighted-multiple-criteria-based filter algorithm (λ = 0.6, marked as MCF-0.6);
- Algorithm 7: the weighted-multiple-criteria-based filter algorithm (λ = 0.8, marked as MCF-0.8);
- Algorithm 1: the conventional cubature Kalman filter algorithm (marked as CKF);
- Algorithm 2: the nonlinear H∞ filter algorithm (marked as HF);
- Algorithm 3: the weighted-multiple-criteria-based filter algorithm (λ = 0.5, marked as MCF-0.5).
5. Conclusions
- (1)
- The experiments in this paper demonstrate that the H∞ filter manifests superior robustness and precision than the cubature Kalman filter in GNSS/INS integrated navigation systems, but sometimes the H∞ filter may introduce some redundant errors.
- (2)
- By integrating the strengths of different filtering algorithms, the proposed weighted-multiple-criteria-based filtering algorithm with a weight adjustment factor outperforms conventional algorithms based on a single criterion.
- (3)
- Different weight adjustment factors can lead to varying filtering performances. Therefore, selecting an appropriate weight adjustment factor is crucial for achieving precise and stable filtering solutions.
- (4)
- A series of data fusion methods with multiple criteria can be constructed with different criteria and different adjustment factors.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Options | Bias | Scale Factor | Random Walk (RW) |
---|---|---|---|
Accelerometer | 50 mg | 4000 ppm | 55 μg/rt-Hz (velocity RW) |
Gyroscope | 20 deg/h (rate bias) | 1500 ppm | 0.067 deg/rt-h (angle RW) |
Algorithm | Index | Px (cm) | PY (cm) | Vx (cm/s) | VY (cm/s) | Pitch (°) | Yaw (°) |
---|---|---|---|---|---|---|---|
CKF | RMS | 3.26 | 3.86 | 0.79 | 1.03 | 0.12 | 0.45 |
HF | 2.72 | 3.36 | 0.53 | 0.81 | 0.10 | 0.37 | |
MCF-0.5 | 2.37 | 3.02 | 0.30 | 0.51 | 0.09 | 0.29 | |
CKF | SD | 3.26 | 3.84 | 0.78 | 1.02 | 0.12 | 0.30 |
HF | 2.71 | 3.40 | 0.53 | 0.80 | 0.10 | 0.32 | |
MCF-0.5 | 2.36 | 2.98 | 0.30 | 0.51 | 0.09 | 0.25 |
Algorithm | Index | Px (cm) | PY (cm) | Vx (cm/s) | VY (cm/s) | Pitch (°) | Yaw (°) |
---|---|---|---|---|---|---|---|
MCF-0.2 | RMS | 2.53 | 3.19 | 0.25 | 0.43 | 0.09 | 0.36 |
MCF-0.4 | 2.41 | 3.03 | 0.27 | 0.46 | 0.09 | 0.32 | |
MCF-0.5 | 2.37 | 3.02 | 0.30 | 0.51 | 0.09 | 0.29 | |
MCF-0.6 | 2.40 | 3.07 | 0.39 | 0.66 | 0.10 | 0.26 | |
MCF-0.8 | 2.66 | 3.35 | 0.46 | 0.72 | 0.10 | 0.22 | |
MCF-0.2 | SD | 2.48 | 3.13 | 0.22 | 0.38 | 0.08 | 0.36 |
MCF-0.4 | 2.40 | 2.98 | 0.28 | 0.46 | 0.09 | 0.29 | |
MCF-0.5 | 2.36 | 2.98 | 0.30 | 0.51 | 0.09 | 0.25 | |
MCF-0.6 | 2.39 | 3.06 | 0.45 | 0.60 | 0.10 | 0.24 | |
MCF-0.8 | 2.62 | 3.21 | 0.49 | 0.75 | 0.10 | 0.20 |
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Jiang, C.; Zhang, Q.; Zhao, D. A New Data Fusion Method for GNSS/INS Integration Based on Weighted Multiple Criteria. Remote Sens. 2024, 16, 3275. https://doi.org/10.3390/rs16173275
Jiang C, Zhang Q, Zhao D. A New Data Fusion Method for GNSS/INS Integration Based on Weighted Multiple Criteria. Remote Sensing. 2024; 16(17):3275. https://doi.org/10.3390/rs16173275
Chicago/Turabian StyleJiang, Chen, Qiuzhao Zhang, and Dongbao Zhao. 2024. "A New Data Fusion Method for GNSS/INS Integration Based on Weighted Multiple Criteria" Remote Sensing 16, no. 17: 3275. https://doi.org/10.3390/rs16173275