Comparative Study on Wavelet Filter and Thresholding Selection for GPS/Ins Data Fusion

Abstract

Navigation and guidance of an autonomous vehicle require determination of the position and velocity of the vehicle. Therefore, fusing the Inertial Navigation System (INS) and Global Positioning System (GPS) is important. Various methods have been applied to smooth and predict the INS and GPS errors. Recently, wavelet de-noising methodologies have been applied to improve the accuracy and reliability of the GPS/INS system. In this work, analysis of real data to identify the optimal wavelet filter for each GPS and INS component for high quality error estimation is presented. A comprehensive comparison of various wavelet thresholding selections with different level of decomposition is conducted to study the effect on GPS/INS error estimation while maintaining the original features of the signal. Results show that while some wavelet filters and thresholding selection algorithms perform better than others on each of the GPS and INS components, no specific parameter selection perform uniformly better than others.

Improvement of a Low Cost MEMS Inertial-GPS Integrated System Using Wavelet Denoising Techniques

“…One of the important elements infl uencing the performance in thresholding technique is how the standard value of λ is set (Chan and Peng, 2003;Hasan et al, 2010). Generally, it is determined by Eq.…”

“…Furthermore, the choice of threshold is crucial to maintain the quality of the denoising process. This should be made carefully (Hasan et al, 2010).…”

“…These MEMS-based inertial sensors have been integrated with the GPS to provide reliable positioning solutions in case of GPS outages that commonly occur in the urban areas. Especially, low cost MEMS-GPS integrated navigation system is used for mobile robots, unmanned aerial vehicle or micro-aerial vehicle and pedestrian navigation system (Hasan et al, 2010;Noureldin et al, 2004;Yoon and Vaidyanathan, 2004). …”

“…In this paper, the wavelet denoising technique is implemented to eliminate the sensor noise for improving the accuracy of the navigation results (Hasan et al, 2010;Kang and Park, 2009;Nassar and El-Sheimy, 2005;Noureldin et al, 2004). Wavelet transforms have been successfully applied for denoising, classification, recognition, compression, and other applications.…”

Intelligently tuned wavelet parameters for GPS/INS error estimation

“…However, these navigation systems experience several problems such as the need for a predefined INS error model and presence of noise effects. The wavelet technique can conquer the abovementioned limitations and provide complete observability unlike Kalman filtering [8][9][10]21] . Hence, wavelet analysis has been utilized to de-noise the GPS and INS outputs before being fed into other compensation techniques such as artificial intelligence [13] .…”