Abstract
Along with the development of computer technology and informatization, the unmanned vehicle has become an important equipment in military, civil and some other fields. The navigation system is the basis and core of realizing the autonomous control and completing the task for unmanned vehicles, and the Strapdown Inertial Navigation System (SINS) is the preferred due to its autonomy and independence. The initial alignment technique is the premise and the foundation of the SINS, whose performance is susceptible to system nonlinearity and uncertainty. To improving system performance for SINS, an improved initial alignment algorithm is proposed in this manuscript. In the procedure of this presented initial alignment algorithm, the original signal of inertial sensors is denoised by utilizing the improved signal denoising method based on the Empirical Mode Decomposition (EMD) and the Extreme Learning Machine (ELM) firstly to suppress the high-frequency noise on coarse alignment. Afterwards, the accuracy and reliability of initial alignment is further enhanced by utilizing an improved Robust Huber Cubarure Kalman Filer (RHCKF) method to minimize the influence of system nonlinearity and uncertainty on the fine alignment. In addition, real tests are used to verify the availability and superiority of this proposed initial alignment algorithm.
Highlights
The unmanned vehicle has been used as a platform for aerial photogrammetry, marine monitoring, geodetic surveying, hazard state investigation and security protection based on different sensors equipped on it
The signal of inertial sensors is denoised by the ELMEMD-Shannon method, and, on this basis, a coarse alignment based on the solidification coordinate frame is used, suppressing the alignment error caused by dynamic noises; secondly, nonlinear system equations of the Strapdown Inertial Navigation System (SINS)/Global Navigation Satellite System (GNSS) integrated fine alignment are established; the Robust Huber Cubarure Kalman Filer (RHCKF) filter is used to complete the state estimation, inhibited the impact of system nonlinearity and uncertainty, and improved the alignment accuracy and robustness of unmanned vehicles under dynamic conditions
The blue solid line indicates the original signal, the red-brown dashed line represents the signal with the wavelet denoising method, and the green dot-dashed line represents the signal with the traditional Empirical Mode Decomposition (EMD) denoising method while the red dotted line denotes the signal with the proposed ELMEMD-Shannon denoising method
Summary
The unmanned vehicle has been used as a platform for aerial photogrammetry, marine monitoring, geodetic surveying, hazard state investigation and security protection based on different sensors equipped on it. Sensors 2018, 18, 3297 technology and the inertial navigation technology have their own advantages and limitations, in the navigation system of unmanned vehicles, GNSS and SINS are often integrated together to enhance the redundancy and accuracy [1,3,4,5]. It is urgent to propose an effective signal denoising method to preprocess the inertial sensor data, enhancing the performance of coarse alignment. In order to enhance the initial alignment accuracy of low-precision SINS for unmanned vehicles, a novel initial alignment algorithm is proposed in this paper In this novel initial alignment algorithm, the inertial sensor signal is preprocessed by utilizing an improved EMD denoising method based on Extreme Learning Machine (ELM) [26] and Shannon entropy to eliminate the effect of random noises on the coarse alignment firstly.
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