Authors :
Kalluri Bhavana; T. Satya Savithri
Volume/Issue :
Volume 10 - 2025, Issue 11 - November
Google Scholar :
https://tinyurl.com/455hjmes
Scribd :
https://tinyurl.com/mvxjp5d5
DOI :
https://doi.org/10.38124/ijisrt/25nov569
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Abstract :
This paper presents the design of a Fixed-point Kalman Filter Bank Architecture and its implementation on the
PYNQ-Z2 FPGA. The proposed architecture comprises Q1.15 fixed-point arithmetic, saturation logic and reciprocal-based
safe division, which are utilized to ensure numerical stability and hardware efficiency. Initially, the architecture for a single
filter is designed and simulated for a sinusoidal input, and then replicated 8 times to create a single Kalman filter bank
module with a span of angular frequencies from 0.001 to 0.029 rad/sample. The constructed Kalman filter bank is created
as an IP and implemented on the PYNQ Z2 FPGA board. The design communicates via AXI4-DMA interface between the
Processing System (PS) and Programmable Logic (PL). Experimental results demonstrate effective denoising of sinusoidal
signals under varying noise levels- low, medium and high noise. The obtained results show an average RMSE below 0.15
and a correlation coefficient above 0.95. Post place-and-route results on the device indicate a resource utilization of 37,596
LUTs (70.67%), 124 DSP slices (56.36%), and 2BRAMs (1.79%), with a maximum operating frequency of 21.4 MHz and
total power consumption of 1.48 W.
Keywords :
Kalman Filter, Fixed-Point Arithmetic, FPGA, PYNQ-Z2, Filter Bank, AXI4-Stream, Signal Denoising, Q1.15.
References :
- R. E. Kalman, “A new approach to linear filtering and prediction problems,” Transactions of the ASME – Journal of Basic Engineering, vol. 82, no. 1, pp. 35–45, Mar. 1960.
- G. Welch and G. Bishop, “An introduction to the Kalman filter,” UNC-Chapel Hill Department of Computer Science, Tech. Rep. 95-041, 2006.
- S. Haykin, Adaptive Filter Theory, 5th ed. Pearson, 2013.
- B. D. O. Anderson and J. B. Moore, Optimal Filtering. Prentice-Hall, 1979.
- A. H. Jazwinski, Stochastic Processes and Filtering Theory. Academic Press, 1970.
- M. S. Grewal and A. P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, 4th ed. Wiley-IEEE Press, 2015.
- S. Liu, M. Chan, and P. Li, “FPGA implementation of a fixed-point Kalman filter for real-time signal tracking,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 8, pp. 5857–5866, Aug. 2020.
- J. Lee and H. Kim, “Efficient hardware implementation of adaptive Kalman filters using fixed-point arithmetic,” IEEE Access, vol. 9, pp. 148 750–148 763, 2021.
- V. Kumar and S. Mishra, “FPGA-based implementation of fixed-point digital filters using Vivado and AXI interface,” IEEE Trans. Education, vol. 63, no. 4, pp. 543–551, Nov. 2020.
- M. A. El-Sayed, F. Mahmoud, and H. Abd-El-Kader, “Hardware efficient Kalman filter for sensor fusion applications,” IEEE Access, vol. 10, pp. 49 320–49 333, 2022.
- Xilinx Inc., AXI4-Stream Interface Protocol Specification, UG761, Ver. 1.0, 2023.
- PYNQ Community, “PYNQ-Z2 board and overlay development documentation,” pynq.io, 2024. [Online]. Available: https://pynq.io
- H. Gao, X. Zhu, and L. Wang, “Resource-optimized Kalman filter for embedded FPGA sensor systems,” IEEE Embedded Systems Letters, vol. 16, no. 3, pp. 112–115, 2024.
- A. Singh and T. Patel, “Real-time FPGA-based adaptive Kalman filter for dynamic signal estimation,” IEEE Access,2023.
This paper presents the design of a Fixed-point Kalman Filter Bank Architecture and its implementation on the
PYNQ-Z2 FPGA. The proposed architecture comprises Q1.15 fixed-point arithmetic, saturation logic and reciprocal-based
safe division, which are utilized to ensure numerical stability and hardware efficiency. Initially, the architecture for a single
filter is designed and simulated for a sinusoidal input, and then replicated 8 times to create a single Kalman filter bank
module with a span of angular frequencies from 0.001 to 0.029 rad/sample. The constructed Kalman filter bank is created
as an IP and implemented on the PYNQ Z2 FPGA board. The design communicates via AXI4-DMA interface between the
Processing System (PS) and Programmable Logic (PL). Experimental results demonstrate effective denoising of sinusoidal
signals under varying noise levels- low, medium and high noise. The obtained results show an average RMSE below 0.15
and a correlation coefficient above 0.95. Post place-and-route results on the device indicate a resource utilization of 37,596
LUTs (70.67%), 124 DSP slices (56.36%), and 2BRAMs (1.79%), with a maximum operating frequency of 21.4 MHz and
total power consumption of 1.48 W.
Keywords :
Kalman Filter, Fixed-Point Arithmetic, FPGA, PYNQ-Z2, Filter Bank, AXI4-Stream, Signal Denoising, Q1.15.