An Improved GPS/INS Integration Based on EKF and AI During GPS Outages

Abstract

Inertial navigation system (INS) is often integrated with satellite navigation systems to achieve the required precision at high-speed applications. In global navigation system (GPS)/INS integration systems, GPS outages are unavoidable and a severe challenge. Moreover, because of the usage of low-cost microelectromechanical sensors (MEMS) with noisy outputs, the INS will get diverged during GPS outages, and that is why navigation precision severely decreases in commercial applications. In this paper, we improve GPS/INS integration system during GPS outages using extended Kalman filter (EKF) and artificial intelligence (AI) together. In this integration algorithm, the AI receives the angular rates and specific forces from the inertial measurement unit (IMU) and velocity from the INS at [Formula: see text] and [Formula: see text]. Therefore, the AI has positioning and timing data of the INS. While the GPS signals are available, the output of the AI is compared with the GPS increment; so that the AI is trained. During GPS outages, the AI will practically play the GPS role. Thus, it can prevent the divergence of the GPS/INS integration system in GPS-denied environments. Furthermore, we utilize neural networks (NNs) as an AI module in five different types: multi-layer perceptron (MLP) NN, radial basis function (RBF) NN, wavelet NN, support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed approach, we utilize a real dataset that has been gathered by a mini-airplane. The results demonstrate that the proposed approach outperforms the INS and GPS/INS integration systems with the EKF during GPS outages. Meanwhile, the ANFIS also reached more than 47.77% precision compared to the traditional method.

Artificial neural network based on strong track and square root UKF for INS/GNSS intelligence integrated system during GPS outage

“…This paper uses these parameters to evaluate the performance of intelligence integrated system during GPS outage: root mean square error (RMSE), mean absolute error (MAE) scatter index (SI), and correlation coefficient (CC). These parameters are expressed as follows 28 . where and are the i th target and predicted value, and are the mean of target and predicted value, and n is the number of the data samples.…”

“…Moreover, to have a comprehensive evaluation of the proposed approach, the uncertainty interval U95 and the reliability percent of the NN models are calculated as follows 28 . where T i is obtained in two steps.…”