Design and implementation of a realtime co-processor for denoising Fiber Optic Gyroscope signal

• Published in: Digital signal processing 2013-09, Vol.23 (5), p.1813-1825
• Main Authors: Peesapati, Rangababu, Sabat, Samrat L., Anumandla, Kiran Kumar, Kandyala, Palani Karthik, Nayak, Jagannath
• Format: Article
• Language: English
• Subjects: Algorithms; Allan variance analysis; Auxiliary processing unit; Cobalt; Dynamics; Embedded computer systems; Fiber optic gyroscopes; Field programmable gate array; Field programmable gate arrays; Fog; Kalman filter; Noise reduction; System on chip
• ISSN: 1051-2004; 1095-4333
• DOI: 10.1016/j.dsp.2013.04.010

The amount of noise present in the Fiber Optic Gyroscope (FOG) signal limits its applications and has a negative impact on navigation system. Existing algorithms such as Discrete Wavelet Transform (DWT), Kalman Filter (KF) denoise the FOG signal under static environment, however denoising fails in dynamic environment. Therefore in this paper an Adaptive Moving Average Dual Mode Kalman Filter (AMADMKF) is developed for denoising the FOG signal under both the static and dynamic environments. Performance of the proposed algorithm is compared with DWT and KF techniques. Further, a hardware Intellectual Property (IP) of the algorithm is developed for System on Chip (SoC) implementation using Xilinx Virtex-5 Field Programmable Gate Array (Virtex-5FX70T-1136). The developed IP is interfaced as a Co-processor/ Auxiliary Processing Unit (APU) with the PowerPC (PPC440) embedded processor of the FPGA. It is proved that the proposed system is an efficient solution for denoising the FOG signal in real-time environment. Hardware acceleration of developed Co-processor is 65× with respect to its equivalent software implementation of AMADMKF algorithm in the PPC440 embedded processor. •Algorithm for denoising of Fiber Optics Gyroscope sensor signal is proposed.•Hardware architecture of the algorithm is developed.•The resource utilization of the proposed algorithm is compared with DWT and Kalman filter.•Co-processor of the proposed algorithm is developed.•An acceleration of 65× over software implementation is obtained using the developed co-processor.