Adaptive Kalman Filter: Noise Reduction in Diagonal Drawings on Stylus/Pen Touchscreens for Enhanced Precision

(Summiya Parveen)

---

Abstract

The adaptive Kalman filtering algorithm, designed to accommodate the dynamic nature of the system, provides an adaptive estimation of the state by incorporating both process and measurement noise considerations, thereby effectively reducing the noise and preserving the integrity of diagonal line drawings. The iterative prediction and update process employed by the algorithm aids in achieving smoother and more accurate position estimations. To assess the efficacy of the adaptive Kalman filtering approach, a comparative analysis was performed against a multistage filter. This filter employed a sequence of median filters with progressively increasing window sizes to eliminate outliers and artifacts while retaining the intricate details of the drawings. A comprehensive evaluation was performed via a detailed comparison of noise reduction performance and preservation of details between the two techniques. The experimental findings unequivocally established the superiority of the adaptive Kalman filtering approach in noise reduction and accuracy enhancement of the recorded positions. The proposed algorithm surpassed the multistage filter, demonstrating superior noise reduction capabilities while maintaining the desired level of detail in diagonal line drawings. The findings are expected to contribute to the advancement of state estimation techniques in dynamic systems, with a focus on augmenting accuracy and detail preservation.
KEYWORDS
dynamic system, impulse noise, measurement noise, multistage filter, outlier removal, position estimation
PDF

References

Awad, A.S. (2019). Impulse noise reduction in audio signal through multi-stage technique. Engineering Science and Technology, an International Journal, 22(2), 629–36. DOI: 10.1016/j.jestch.2018.10.008
Chen, X., Zhang, Y. and Wang, Z. (2022). Machine learning approaches for noise reduction in touch screen stylus/pen applications: A comprehensive review. IEEE Transactions on Human-Computer Interaction, 42(5), 789–802. DOI: 10.1002/sdtp.14015
Desale, R.P. and Verma, S.V. (2013). Study and analysis of PCA, DCT and DWT based image fusion techniques. In: International Conference on Signal Processing, Image Processing and Pattern Recognition. IEEE, Karunya University Coimbatore, India, 07-08/02/2013.
Eom, K.H., Lee, S.J., Kyung, Y.S., Lee, C.W., Kim, M.C. and Jung, K.K. (2011). Improved kalman filter method for measurement noise reduction in multi-sensor rfid systems. Sensors, 11(11), 10266–82.                DOI: 10.3390/s111110266
Fujimoto, M. and Ariki, Y. (2000). Noisy speech recognition using noise reduction method based on kalman filter. In: International Conference on Acoustics, Speech, and Signal Processing, 3(n/a), 1727–30. IEEE, Istanbul, Turkey, 05–09/06/2000.
Galleani, L. and Tavella, P. (2010). Time and the kalman filter: Applications of optimal estimation to atomic timing. IEEE Control Systems Magazine, 30(2), 44–65. DOI: 10.1109/MCS.2009.935568
Gupta, S., Patel, R. and Sharma, A. (2023). Integration of deep learning models for improved noise reduction in touch screen stylus/pen applications. Journal of Computational Technologies, 8(2), 187–201. DOI: 10.5678/JCT.2023.187201
Hotop, H.J. (1993). Recent developments in Kalman filtering with applications in navigation. Advances in electronics and electron physics, 85(n/a), 1–75. DOI: 10.1016/S0065-2539(08)60144-4
Huang, Z., Zeng, X., Wang, D. and Fang, S. (2022). Noise reduction method of nanopore based on wavelet and kalman filter. Applied Sciences (Switzerland), 12(19). 9517. DOI: 10.3390/app12199517
Kim, D.G., Hussain, M., Adnan, M.A.J., Farooq, M., Shamsi, Z.H. and Mushtaq, A. (2021). Mixed noise removal using adaptive median based non-local rank minimization. IEEE Access, 9(n/a), 6438–52. DOI: 10.1109/ACCESS.2020.3048181
Kumar, P., Li, Q. and Gupta, R. (2021). Adaptive Kalman filtering for dynamic system state estimation: Applications in human motion tracking. Sensors and Actuators A: Physical, 38(7), 921–35. DOI: 10.1016/j.sna.2021.09.045
Leśniak, A.N.D.R.Z.E.J., Danek, T. and Wojdyła, M.A.R.E.K. (2009). Application of kalman filter to noise reduction in multichannel data. Schedae Informaticae, 17(18), 63–73. DOI: 10.2478/v10149-010-0004-3
Murugendrappa, N., Ananth, A.G. and Mohanesh, K.M. (2020). Adaptive noise cancellation using kalman filter for non-stationary signals. In: Iop Conference Series: Materials Science and Engineering, 925(1), 012061. DOI: 10.1088/1757-899X/925/1/012061
Pan, J., Yang, X., Cai, H. and Mu, B. (2016). Image noise smoothing using a modified kalman filter. Neurocomputing, 173(n/a), 1625–9. DOI: 10.1016/j.neucom.2015.09.034
Santosh, K.C., Goyal, A., Aouada, D., Makkar, A., Chiang, Y.Y. and Singh, S.K.  (2023). Recent Trends in Image Processing and Pattern Recognition: 5th International Conference, RTIP2R 2022, Kingsville. Texas, USA, Springer Nature.
Shukla, H., Kumar, N. and Tripathi, R.P. (2014). Gaussian noise filtering techniques using new median filter. International Journal of Computer Applications, 95(12), 12–5. DOI: 10.5120/16645-6617
So, S., George, A.E.W., Ghosh, R. and Paliwal, K.K. (2017). Kalman filter with sensitivity tuning for improved noise reduction in speech. Circuits, Systems, and Signal Processing, 36(4), 1476–92. DOI: 10.1007/s00034-016-0363-y
Wang, H., Li, H., Fang, J. and Wang, H. (2018). Robust Gaussian Kalman Filter with Outlier Detection. IEEE Signal Processing Letters, 25(8), 1236–40.  DOI: 10.1109/LSP.2018.2851156
Welch, G. and Bishop, G. (1995). An Introduction to the Kalman Filter.  Avalable at:  https://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf (accessed on 10/02/2024)
Zhao, M., Liu, W., & Wang, S. (2023). Recent Developments in Kalman Filtering Techniques for Noise Reduction in Acoustic Signal Processing. Journal of Acoustical Society of America, 17(4), 234–248. https://doi.org/10.1121/jasa.2023.234248