FILTERING STRATEGIES FOR SMARTPHONE EMITTED DIGITAL SIGNALS
Alexandru Marius OBRETIN
Bucharest University of Economic Studies (Romania)
Andreea Alina CORNEA
andreea.cornea@csie.ase.roBucharest University of Economic Studies (Romania)
Abstract
In today's digitalized and technology-driven society, where the number of IoT devices and the volume of collected data is exponentially increasing, the use of sensor data has become a necessity in certain fields of activity. This paper presents a concise history of sensor evolution and specialization, with a focus on the sensors used for localization, particularly those present in microelectromechanical systems (MEMS) that make up inertial measurement units. The study starts with a general overview and progresses towards a more specific analysis of data sets collected from an accelerometer. In the materials and methods section, it emphasizes the imperfect nature of sensor measurements and the need to filter digital signals. Three different digital signal filtering techniques belonging to distinct filter categories are comparatively analyzed, with each technique having its own particularities, advantages and disadvantages. The analysis considers the effectiveness in reducing measurement errors, the impact of the filtering process on the original signal, the ability to highlight the underlying phenomenon, as well as the performance of the analyzed algorithms. The ultimate purpose of this article is to determine which filtration method is best suited for the signal at hand in the context of an indoor localization application.
Keywords:
signal processing, digital filters, sensors, technologyReferences
Acharya, D., Rani, A., Agarwal, S., & Singh, V. (2016). Application of adaptive Savitzky–Golay filter for EEG signal processing. Perspectives in science, 8, 677-679.
Google Scholar
Ahn, H. S., & Ko, K. H. (2009). Simple pedestrian localization algorithms based on distributed wireless sensor networks. IEEE Transactions on Industrial Electronics, 56(10), 4296-4302.
Google Scholar
Awal, M. A., Mostafa, S. S., & Ahmad, M. (2011). Performance analysis of Savitzky-Golay smoothing filter using ECG signal. International Journal of Computer and Information Technology, 1(02), 24.
Google Scholar
Azami, H., Mohammadi, K., & Bozorgtabar, B. (2012). An improved signal segmentation using moving average and Savitzky-Golay filter.
Google Scholar
Bentler, R., & Chiou, L. K. (2006). Digital noise reduction: An overview. Trends in amplification, 10(2), 67-82.
Google Scholar
Bloecher, H. L., Dickmann, J., & Andres, M. (2009, September). Automotive active safety & comfort functions using radar. In 2009 IEEE International Conference on Ultra-Wideband (pp. 490-494). IEEE.
Google Scholar
Chen, Z., Sun, Z., & Wang, W. (2011). Design and implementation of Kalman filter. In 2011 IET International Conference on Communication Technology and Application (p. 901-904).
Google Scholar
Deep, A., Mittal, M., & Mittal, V. (2018, December). Application of Kalman filter in GPS position estimation. In 2018 IEEE 8th Power India International Conference (PIICON) (pp. 1-5). IEEE.
Google Scholar
Dinakar, J. R., & Vagdevi, S. (2017, December). A study on storage mechanism for heterogeneous sensor data on big data paradigm. In 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (Iceeccot) (pp. 342-345). IEEE.
Google Scholar
Ellis, C. A., & Parbery, S. A. (2005). Is smarter better? A comparison of adaptive, and simple moving average trading strategies. Research in International Business and Finance, 19(3), 399-411.
Google Scholar
Farhad, A., Kwon, G. R., & Pyun, J. Y. (2023, January). Mobility Adaptive Data Rate Based on Kalman Filter for LoRa-Empowered IoT Applications. In 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC) (pp. 321-324). IEEE.
Google Scholar
Gujarathi, T., & Bhole, K. (2019, July). Gait analysis using imu sensor. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
Google Scholar
Hansun, S., & Kristanda, M. B. (2017, November). Performance analysis of conventional moving average methods in forex forecasting. In 2017 International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS) (pp. 11-17). IEEE.
Google Scholar
Hasan, K., Meraj, S. T., Othman, M. M., Lipu, M. H., Hannan, M. A., & Muttaqi, K. M. (2022). Savitzky–Golay Filter-Based PLL: Modeling and Performance Validation. IEEE Transactions on Instrumentation and Measurement, 71, 1-6.
Google Scholar
Hidayat, A. A., Arief, Z., & Happyanto, D. C. (2015, September). Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In 2015 International Electronics Symposium (IES) (pp. 1-6). IEEE.
Google Scholar
Johnston, F. R., Boyland, J. E., Meadows, M., & Shale, E. (1999). Some properties of a simple moving average when applied to forecasting a time series. Journal of the Operational Research Society, 50(12), 1267-1271.
Google Scholar
Krishnan, S. R., & Seelamantula, C. S. (2012). On the selection of optimum Savitzky-Golay filters. IEEE transactions on signal processing, 61(2), 380-391.
Google Scholar
Kwon, J., & Park, D. (2020, February). Implementation of computation-efficient sensor network for Kalman filter-based intelligent position-aware application. In 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (pp. 565-568). IEEE.
Google Scholar
Li, Q., Li, R., Ji, K., & Dai, W. (2015, November). Kalman filter and its application. In 2015 8th International Conference on Intelligent Networks and Intelligent Systems (ICINIS) (pp. 74-77). IEEE.
Google Scholar
Liu, Y., Dang, B., Li, Y., Lin, H., & Ma, H. (2016). Applications of savitzky-golay filter for seismic random noise reduction. Acta Geophysica, 64, 101-124.
Google Scholar
Meinhold, R. J., & Singpurwalla, N. D. (1983). Understanding the Kalman filter. The American Statistician, 37(2), 123-127.
Google Scholar
Olivares, A., Olivares, G., Gorriz, J. M., & Ramirez, J. (2009, December). High-efficiency low-cost accelerometer-aided gyroscope calibration. In 2009 International Conference on Test and Measurement (Vol. 1, pp. 354-360). IEEE.
Google Scholar
Poulose, A., Kim, J., & Han, D. S. (2019, January). Indoor localization with smartphones: Magnetometer calibration. In 2019 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1-3). IEEE.
Google Scholar
Promrit, P., Chokchaitam, S., & Ikura, M. (2018, November). In-vehicle MEMS IMU calibration using accelerometer. In 2018 IEEE 5th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) (pp. 1-3). IEEE.
Google Scholar
Purnama, S. I., Afandi, M. A., & Purba, E. V. (2022, June). Global Positioning System Data Processing Improvement for Blind Tracker Device Based Using Moving Average Filter. In Proceedings of the 2nd International Conference on Electronics, Biomedical Engineering, and Health Informatics: ICEBEHI 2021, 3–4 November, Surabaya, Indonesia (pp. 177-188). Singapore: Springer Nature Singapore.
Google Scholar
Quan, Q., & Cai, K. Y. (2012). Time-domain analysis of the Savitzky–Golay filters. Digital Signal Processing, 22(2), 238-245.
Google Scholar
Schafer, R. W. (2011, January). On the frequency-domain properties of Savitzky-Golay filters. In 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE) (pp. 54-59). IEEE.
Google Scholar
Schmid, M., Rath, D., & Diebold, U. (2022). Why and How Savitzky–Golay Filters Should Be Replaced. ACS Measurement Science Au, 2(2), 185-196.
Google Scholar
Schulze, H. G., Foist, R. B., Ivanov, A., & Turner, R. F. (2008). Fully automated high-performance signal-to-noise ratio enhancement based on an iterative three-point zero-order Savitzky–Golay filter. Applied spectroscopy, 62(10), 1160-1166.
Google Scholar
Sung, F. Y., Fang, S. H., & Chien, Y. R. (2014, May). An experimental study of MEMS-based magnetometers on Android mobile phones. In 2014 IEEE International Conference on Consumer Electronics-Taiwan (pp. 227-228). IEEE.
Google Scholar
Tan, L., & Jiang, J. (2018). Digital signal processing: fundamentals and applications. Academic Press.
Google Scholar
Thinh, D. T., Quan, N. B. H., & Maneetien, N. (2018, November). Implementation of Moving Average Filter on STM32F4 for Vibration Sensor Application. In 2018 4th International Conference on Green Technology and Sustainable Development (GTSD) (pp. 627-631). IEEE.
Google Scholar
Truzman, S., Revach, G., & Klein, I. (2021, October). On the influence of home appliances on the smartphone’s inertial sensors. In 2021 IEEE Sensors (pp. 1-4). IEEE.
Google Scholar
Vaseghi, S. V. (2008). Advanced digital signal processing and noise reduction. John Wiley & Sons.
Google Scholar
Welch, G. F. (2020). Kalman filter. Computer Vision: A Reference Guide, 1-3.
Google Scholar
Xie, Q., Wang, Q., Cao, N., Gao, S., Liang, G., Zhang, T., ... & Li, N. (2017, October). A Survey of Wireless Sensor Technique Applications for Medical Care. In 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) (pp. 412-415). IEEE.
Google Scholar
Zhang, P., Gu, J., Milios, E. E., & Huynh, P. (2005, July). Navigation with IMU/GPS/digital compass with unscented Kalman filter. In IEEE International Conference Mechatronics and Automation, 2005 (Vol. 3, pp. 1497-1502). IEEE.
Google Scholar
Zhang, X., & Yu, W. (2022, April). Research on the Application of Kalman Filter Algorithm in Aircraft Trajectory Analysis. In 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) (pp. 196-199). IEEE.
Google Scholar
Authors
Alexandru Marius OBRETINBucharest University of Economic Studies Romania
Authors
Andreea Alina CORNEAandreea.cornea@csie.ase.ro
Bucharest University of Economic Studies Romania
Statistics
Abstract views: 223PDF downloads: 77
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Katarzyna ORZECHOWSKA, Tymon RUBEL, Robert KURJATA, Krzysztof ZAREMBA, A DISTRIBUTED ALGORITHM FOR PROTEIN IDENTIFICATION FROM TANDEM MASS SPECTROMETRY DATA , Applied Computer Science: Vol. 18 No. 2 (2022)
- Błażej BADZIO, Agnieszka BODZIAK, Bartłomiej BRODAWKA, Karol BUCHAJCZUK, Maria SKUBLEWSKA-PASZKOWSKA, Mariusz DZIEŃKOWSKI, Paweł POWROŹNIK, ANALYSIS OF THE USABILITY AND ACCESSIBILITY OF WEBSITES IN VIEW OF THEIR UNIVERSAL DESIGN PRINCIPLES , Applied Computer Science: Vol. 18 No. 3 (2022)
- Piotr Miś, Przemysław Szulim, ANALYSIS OF THE POSSIBILITY OF USING MARKERS EMITTING PULSATING LIGHT IN THE TASK OF LOCALIZATION , Applied Computer Science: Vol. 17 No. 1 (2021)
- Jakub ANCZARSKI, Adrian BOCHEN, MArcin GŁĄB, Mikolaj JACHOWICZ, Jacek CABAN, Radosław CECHOWICZ, A METHOD OF VERIFYING THE ROBOT'S TRAJECTORY FOR GOALS WITH A SHARED WORKSPACE , Applied Computer Science: Vol. 18 No. 1 (2022)
- Grzegorz KŁOSOWSKI, Tomasz KLEPKA, Agnieszka NOWACKA, NEURAL CONTROLLER FOR THE SELECTION OF RECYCLED COMPONENTS IN POLYMER-GYPSY MORTARS , Applied Computer Science: Vol. 14 No. 2 (2018)
- Jacek CABAN, Grzegorz LITAK, Bartłomiej AMBROŻKIEWICZ, Leszek GARDYŃSKI, Paweł STĄCZEK, Piotr WOLSZCZAK, IMPACT-BASED PIEZOELECTRIC ENERGY HARVESTING SYSTEM EXCITED FROM DIESEL ENGINE SUSPENSION , Applied Computer Science: Vol. 16 No. 3 (2020)
- Waldemar SUSZYŃSKI, Małgorzata CHARYTANOWICZ, Wojciech ROSA, Leopold KOCZAN, Rafał STĘGIERSKI, DETECTION OF FILLERS IN THE SPEECH BY PEOPLE WHO STUTTER , Applied Computer Science: Vol. 17 No. 4 (2021)
- Yuriy TRYUS, Nataliya ANTIPOVA, Kateryna ZHURAVEL, Grygoriy ZASPA, INFORMATION TECHNOLOGY OF STOCK INDEXES FORECASTING ON THE BASE OF FUZZY NEURAL NETWORKS , Applied Computer Science: Vol. 13 No. 1 (2017)
- Robert KARPIŃSKI, Anna MACHROWSKA, Marcin MACIEJEWSKI, APPLICATION OF ACOUSTIC SIGNAL PROCESSING METHODS IN DETECTING DIFFERENCES BETWEEN OPEN AND CLOSED KINEMATIC CHAIN MOVEMENT FOR THE KNEE JOINT , Applied Computer Science: Vol. 15 No. 1 (2019)
- Thanh-Nghia NGUYEN, Thanh-Hai NGUYEN, Ba-Viet NGO, R PEAK DETERMINATION USING A WDFR ALGORITHM AND ADAPTIVE THRESHOLD , Applied Computer Science: Vol. 18 No. 3 (2022)
You may also start an advanced similarity search for this article.
