A performance compensation method for GPS/INS integrated navigation system based on CNN–LSTM during GPS outages

Abstract

When the global position system (GPS) signal is unavailable, the performance of the GPS/inertial navigation system (INS) integrated navigation system degrades severely. In this article, the performance of the ultra-low cost inertial measurement unit (IMU) is studied and the objective is to enhance its performance during GPS outages. To be specific, a performance compensation method is proposed, which consists of two parts. First, to deal with the large noise and drift of the micro-electro-mechanical system (MEMS)-based inertial measurement unit (IMU), a wavelet regional correlation threshold denoising algorithm is proposed. Then, to improve the performance of traditional LSTM network when dealing with navigation data with strong coupling, a convolutional neural network-long short-term memory (CNN–LSTM) model is formulated. It employs CNN to quickly extract the features of the input, and utilizes LSTM network to output pseudo-GPS signals as the compensation object. Finally, simulation experiments and real road tests are implemented to evaluate the proposed method. Comparison experiment results show that the proposed method can effectively improve the performance of the integrated navigation system during GPS outages.