High-precision data fusion method oriented to high-dynamic non-Gaussian-model robustness measurement

Abstract

Disclosed is a high-precision data fusion method oriented to high-dynamic non-Gaussian-model robustness measurement. The method includes: taking sampling period fluctuations of a hardware sensor in a high-dynamic system as system random uncertainty for consideration, establishing a filtering model set comprising a UKF (unscented Kalman filter) model and a fuzzy inference system, computing match probability of the UKF model and a current high-dynamic system state according to the Bayes' theorem, updating the match probability in real time, taking the updated match probability as input of the fuzzy inference system, obtaining adaptive estimation probability by the fuzzy inference system, and finally, fusing multiple states based on the adaptive estimation probability to estimate to obtain a final mean value and covariance estimate of state variables of the high-dynamic system. The high-precision data fusion method oriented to high-dynamic non-Gaussian-model robustness measurement has the advantages that not only can data fusion for a high-dynamic, strong-nonlinearity and non-Gaussian-model combined system be achieved, but also the number of pre-stored model sets can be decreased; meanwhile, computational efficiency for model probability updating and measurement robustness of the high-dynamic system are improved.

Description

technical field [0001] The invention relates to a high-precision data fusion method oriented to robust measurement of a high-dynamic non-Gaussian model, and its applicable field is combined navigation and other multi-sensor information fusion fields. Background technique [0002] Global Navigation Satellite System (GNSS) is a navigation system that can provide all-weather precise positioning services, but it is susceptible to human and non-human interference, resulting in poor positioning robustness. Inertial Navigation System (INS) is a completely autonomous navigation system with good anti-interference ability, high accuracy in short-term and low navigation accuracy in long-term work. Combining the two navigation systems can learn from each other and obtain better navigation effects, so it has become a hotspot in navigation research. The data fusion algorithm of multi-sensor output is the focus of integrated navigation research. In recent years, the Kalman filter (KF) and...

AI Technical Summary

Problems solved by technology

Traditional IMM information fusion is mostly used for tracking applications of dynamic targets. Recently, there have been discussions on applying it to integrated navigation data fusion. However, there are still many problems in directly using IMM, such as constructing a large enough model to accurately match the operating state of the system. At the same time, the accuracy of the model set is greatly affected by the prior knowledge of the algorithm designer, while the variable structure multi-mode interaction (VSIMM) algorithm can reduce the number of model sets stored in the system in advance and reduce the probability transfer. The computational load of the matrix, and the algorithm adaptively generates a new model in the limited model set to adapt to the change of the statistical characteristics of the system process noise

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