Fixed-Point Kalman Filter on PYNQ-Z2 FPGA


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

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.


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 :

  1. 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.
  2. G. Welch and G. Bishop, “An introduction to the Kalman filter,” UNC-Chapel Hill Department of Computer Science, Tech. Rep. 95-041, 2006.
  3. S. Haykin, Adaptive Filter Theory, 5th ed. Pearson, 2013.
  4. B. D. O. Anderson and J. B. Moore, Optimal Filtering. Prentice-Hall, 1979.
  5. A. H. Jazwinski, Stochastic Processes and Filtering Theory. Academic Press, 1970.
  6. M. S. Grewal and A. P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, 4th ed. Wiley-IEEE Press, 2015.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. Xilinx Inc., AXI4-Stream Interface Protocol Specification, UG761, Ver. 1.0, 2023.
  12. PYNQ Community, “PYNQ-Z2 board and overlay development documentation,” pynq.io, 2024. [Online]. Available: https://pynq.io
  13. 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.
  14. 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.

CALL FOR PAPERS


Paper Submission Last Date
30 - November - 2025

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe