AI-Driven Adaptive Radar Systems for Real-Time Target Tracking in Urban Environments
DOI:
https://doi.org/10.51903/jtie.v4i1.289Keywords:
Adaptive Radar, Artificial Intelligence, Target Tracking, Urban Environment, Signal ProcessingAbstract
Radar systems play a crucial role in target tracking within urban environments, where challenges such as clutter, multipath effects, and electromagnetic interference significantly impact detection accuracy. Traditional radar methods often struggle to adapt to dynamic urban conditions, leading to decreased reliability in real-time target tracking. This study aims to develop and evaluate an AI-driven adaptive radar system that enhances tracking accuracy in urban settings. The research employs a quantitative approach using simulations to model radar signal processing under various environmental conditions. The AI model, based on Convolutional Neural Networks (CNN), is trained to optimize radar performance by filtering out noise and dynamically adjusting detection parameters. The results indicate that the AI-based radar system achieves a tracking accuracy of 95.2%, significantly outperforming traditional radar systems, which only reach 80% accuracy. Additionally, the AI-enhanced radar reduces response time to 120 milliseconds, compared to 250 milliseconds in conventional systems, demonstrating improved real-time processing capabilities. The system also exhibits greater resilience to high-clutter environments, maintaining stable target detection despite signal interference. These findings highlight the potential of AI in enhancing radar functionality for applications such as surveillance, traffic monitoring, and security. Future research should focus on integrating AI-driven radar with real-world radar hardware, exploring multi-sensor fusion, and refining adaptive learning techniques to further optimize tracking performance in complex environments
References
Abdelfattah, T., Maher, A., Youssef, A., & Driessen, P. F. (2024). Seamless Optimization of Wavelet Parameters for Denoising LFM Radar Signals: An AI-Based Approach. Remote Sensing, 16(22), 4211. https://doi.org/10.3390/rs16224211
Arafat, M. Y., Alam, M. M., & Moh, S. (2023). Vision-Based Navigation Techniques for Unmanned Aerial Vehicles: Review and Challenges. Drones, 7(2), 89. https://doi.org/10.3390/drones7020089
Cai, L., Qian, H., Xing, L., Zou, Y., Qiu, L., Liu, Z., Tian, S., & Li, H. (2023). A Software-Defined Radar for Low-Altitude Slow-Moving Small Targets Detection Using Transmit Beam Control. Remote Sensing, 15(13), 3371. https://doi.org/10.3390/rs15133371
Cheng, P., Xiong, Z., Bao, Y., Zhuang, P., Zhang, Y., Blasch, E., & Chen, G. (2023). A Deep Learning-Enhanced Multi-Modal Sensing Platform for Robust Human Object Detection and Tracking in Challenging Environments. Electronics, 12(16), 3423. https://doi.org/10.3390/electronics12163423
Dasgupta, S., Irfan, M. S., Rahman, M., & Chowdhury, M. (2025). Detection and Mitigation of Spoofing Attacks in-Based Autonomous Ground Vehicle Navigation Systems. Data Analytics for Intelligent Transportation Systems, 15, 403–427. https://doi.org/10.1016/b978-0-443-13878-2.00016-3
Feng, W., Hu, X., & He, X. (2024). Artificial Intelligence (AI)-Based Radar Signal Processing and Radar Imaging. Electronics, 13(21), 4251. https://doi.org/10.3390/electronics13214251
Hashmi, U. S., Akbar, S., Adve, R., Moo, P. W., & Ding, J. (2023). Artificial Intelligence Meets Radar Resource Management: A Comprehensive Background and Literature Review. IET Radar, Sonar and Navigation, 17(2), 153–178. https://doi.org/10.1049/rsn2.12337
Huang, K., Ding, J., & Deng, W. (2024). An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications. Sensors, 24(11), 3310. https://doi.org/10.3390/s24113310
Jiang, R., Xu, H., Gong, G., Kuang, Y., & Liu, Z. (2022). Spatial-Temporal Attentive LSTM for Vehicle-Trajectory Prediction. ISPRS International Journal of Geo-Information, 11(7), 354. https://doi.org/10.3390/ijgi11070354
Jiang, W., Ren, Y., Liu, Y., Gegov, A., Jafari, R., Morris, D., Jiang, W., Ren, Y., Liu, Y., & Leng, J. (2022). Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review. Electronics, 11(1), 156. https://doi.org/10.3390/electronics11010156
Jiang, W., Wang, Y., Li, Y., Lin, Y., & Shen, W. (2023). Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review. Remote Sensing, 15(15), 3742. https://doi.org/10.3390/rs15153742
Kurtoğlu, E., Biswas, S., Gurbuz, A. C., & Gurbuz, S. Z. (2023). Boosting Multi-Target Recognition Performance With Multi-Input Multi-Output Radar-Based Angular Subspace Projection and Multi-View Deep Neural Network. IET Radar, Sonar and Navigation, 17(7), 1115–1128. https://doi.org/10.1049/rsn2.12405
Li, Z., Braun, T., Sun, D., Isaia, C., & Michaelides, M. P. (2023). A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G. Signals, 4(1), 90–136. https://doi.org/10.3390/signals4010006a
Massimi, F., Ferrara, P., & Benedetto, F. (2023). Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks. Sensors, 23(1), 124. https://doi.org/10.3390/s23010124
Mohanty, A., & Gao, G. (2024). A Survey of Machine Learning Techniques for Improving Global Navigation Satellite Systems. Eurasip Journal on Advances in Signal Processing, 2024(1), 73. https://doi.org/10.1186/s13634-024-01167-7
Pico, N., Montero, E., Vanegas, M., Erazo Ayon, J. M., Auh, E., Shin, J., Doh, M., Park, S. H., & Moon, H. (2024). Integrating Radar-Based Obstacle Detection with Deep Reinforcement Learning for Robust Autonomous Navigation. Applied Sciences, 15(1), 295. https://doi.org/10.3390/app15010295
Qiao, S., Fan, Y., Wang, G., Mu, D., & He, Z. (2022). Radar Target Tracking for Unmanned Surface Vehicle Based on Square Root Sage–Husa Adaptive Robust Kalman Filter. Sensors, 22(8), 2924. https://doi.org/10.3390/s22082924
Rosado-Sanz, J., Jarabo-Amores, M. P., De la Mata-Moya, D., & Rey-Maestre, N. (2022). Adaptive Beamforming Approaches to Improve Passive Radar Performance in Sea and Wind Farms’ Clutter. Sensors, 22(18), 6865. https://doi.org/10.3390/s22186865
Ruiz-Perez, F., López-Estrada, S. M., Tolentino-Hernández, R. V., & Caballero-Briones, F. (2022). Carbon-Based Radar Absorbing Materials: A Critical Review. Journal of Science: Advanced Materials and Devices, 7(3), 100454. https://doi.org/10.1016/j.jsamd.2022.100454
Sénica, A. L., Marques, P. A. C., & Figueiredo, M. A. T. (2024). Artificial Intelligence applications in Noise Radar Technology. IET Radar, Sonar and Navigation, 18(7), 986–1001. https://doi.org/10.1049/rsn2.12503
Sharma, A. ;, Sharma, V. ;, Jaiswal, M. ;, Wang, H.-C. ;, Jayakody, D. N. K. ;, Basnayaka, C. M. W. ;, Sharma, A., Sharma, V., Jaiswal, M., Wang, H.-C., Nalin, D., Jayakody, K., Wijerathna Basnayaka, C. M., & Muthanna, A. (2022). Recent Trends in AI-Based Intelligent Sensing. Electronics, 11(10), 1661. https://doi.org/10.3390/electronics11101661
Soumya, A., Krishna Mohan, C., & Cenkeramaddi, L. R. (2023). Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review. Sensors, 23(21), 8901. https://doi.org/10.3390/s23218901
Xu, X., Fan, W., Wang, S., & Zhou, F. (2024). WBIM-GAN: A Generative Adversarial Network Based Wideband Interference Mitigation Model for Synthetic Aperture Radar. Remote Sensing, 16(5), 910. https://doi.org/10.3390/rs16050910
Zhang, J., Dang, X., & Hao, Z. (2024). TWPT: Through-Wall Position Detection and Tracking System Using IR-UWB Radar Utilizing Kalman Filter-Based Clutter Reduction and CLEAN Algorithm. Electronics, 13(19), 3792. https://doi.org/10.3390/electronics13193792
Zhao, L., & Bai, Y. (2024). Unlocking the Ocean 6G: A Review of Path-Planning Techniques for Maritime Data Harvesting Assisted by Autonomous Marine Vehicles. Journal of Marine Science and Engineering, 12(1), 126. https://doi.org/10.3390/jmse12010126
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Technology Informatics and Engineering
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).
This license allows others to copy, distribute, display, and perform the work, and derivative works based upon it, for both commercial and non-commercial purposes, as long as they credit the original author(s) and license their new creations under identical terms.
Licensed under CC BY-SA 4.0: https://creativecommons.org/licenses/by-sa/4.0/
