ADVANCED SENSOR FUSION: SIGNAL PROCESSING ARCHITECTURES FOR AUTONOMOUS ENGINEERING PLATFORMS
Indonesia 
China China 
Abstract
The reliability of autonomous engineering platforms depends fundamentally on the seamless integration of high-bandwidth, multi-modal sensory data to perceive dynamic environments. This research addresses the persistent challenge of computational latency and perception failure in traditional sensor fusion architectures when operating under adverse conditions. The study aims to evaluate a novel hybrid signal processing architecture that optimizes the balance between edge-level feature extraction and centralized semantic synthesis. Utilizing a “Hardware-in-the-Loop” methodology, the proposed framework was tested on an embedded GPU testbed using synchronized LiDAR, RADAR, and camera datasets across 500 diverse navigational scenarios. Results demonstrate that the hybrid architecture achieves a 56% reduction in processing latency, maintaining a mean response time of 12.4 milliseconds without compromising positional accuracy, which remained stable at 0.11 meters RMSE. Furthermore, the implementation of an entropy-driven weighting mechanism allowed the system to maintain 99.2% anomaly detection accuracy during simulated sensor failures. This research concludes that decentralized feature processing is essential for the operational continuity of energy-constrained autonomous systems. The findings provide a scalable blueprint for developing resilient, low-power perception modules, asserting that hardware-aware signal processing is a prerequisite for achieving Level 5 autonomy in complex, real-world engineering applications.
Full text article
References
ALMahadeen, S. (2025). GPS and LiDAR optimizing transformation parameters for localization in autonomous vehicles. The Egyptian Journal of Remote Sensing and Space Sciences, 28(4), 597–606. https://doi.org/https://doi.org/10.1016/j.ejrs.2025.09.004
Alshuhail, A., Alshahrani, A., Mahgoub, H., Ghaleb, M., Darem, A. A., Aljehane, N. O., Alotaibi, M., & Alzahrani, F. (2025). Machine edge-aware IoT framework for real-time health monitoring: Sensor fusion and AI-driven emergency response in decentralized networks. Alexandria Engineering Journal, 129, 1349–1361. https://doi.org/https://doi.org/10.1016/j.aej.2025.08.030
Aslam, S., Khan, D., Mondal, S., & Chang, K. (2025). RSU-assisted V2X message fusion via PC5 and Uu with actor–critic modeling for autonomous driving under intersection scenario. Vehicular Communications, 56, 100972. https://doi.org/https://doi.org/10.1016/j.vehcom.2025.100972
Butt, M. Z., Nasir, N., & A??Rashid, R. B. (2024). A review of perception sensors, techniques, and hardware architectures for autonomous low-altitude UAVs in non-cooperative local obstacle avoidance. Robotics and Autonomous Systems, 173, 104629. https://doi.org/https://doi.org/10.1016/j.robot.2024.104629
Cai, J., & Wu, Y. (2025). Three-dimensional object detection for autonomous driving via deep learning: A review. Engineering Applications of Artificial Intelligence, 161, 112238. https://doi.org/https://doi.org/10.1016/j.engappai.2025.112238
Chen, J., Li, B., Zhang, C., Gui, X., Jie, B., & Wang, S. (2025). Integrated method for autonomous capture of non-cooperative targets in earth orbit based on multimodal information fusion. Journal of Space Safety Engineering, 12(4), 770–781. https://doi.org/https://doi.org/10.1016/j.jsse.2025.09.009
Chen, Z., Wang, W., Yu, S., Gao, B., & Deng, Z. (2025). Bioinspired fabrication of micro- and nanostructured flexible sensors from surface architecture to interface engineering: A review. Sensors and Actuators A: Physical, 394, 116931. https://doi.org/https://doi.org/10.1016/j.sna.2025.116931
Daglarli, E. (2025). A Generative Neuro-Cognitive Architecture Using Quantum Algorithms for the Autonomous Behavior of a Smart Agent in a Simulation Environment. Computers, Materials and Continua, 84(3), 4511–4537. https://doi.org/https://doi.org/10.32604/cmc.2025.065572
Ding, S., Tang, S., Yin, L., & Wang, J. (2025). Multimodal wearable sensors for chronic wound monitoring. Device, 3(11), 100874. https://doi.org/https://doi.org/10.1016/j.device.2025.100874
Du, H., Ren, L., Wang, Y., Cao, X., & Sun, C. (2025). Advancements in perception system with multi-sensor fusion for embodied agents. Information Fusion, 117, 102859. https://doi.org/https://doi.org/10.1016/j.inffus.2024.102859
George, D., Pavithra, S., & Das, J. (2025). Cyber-resilient autonomous vehicles: securing networks and enhancing decision-making with next-gen security measures. Results in Engineering, 28, 107179. https://doi.org/https://doi.org/10.1016/j.rineng.2025.107179
Hussain, I., Han, X., Zhang, Z., Mao, X., & Guo, J. (2025). Enhancing precision agriculture with advanced variable-rate spraying and autonomous vehicles: A comprehensive review. Computers and Electronics in Agriculture, 238, 110812. https://doi.org/https://doi.org/10.1016/j.compag.2025.110812
Hussain, K. F., El-Din Safwat, N., Thangavel, K., & Sabatini, R. (2025). Space-based debris trajectory estimation using vision sensors and track-based data fusion techniques. Acta Astronautica, 229, 814–830. https://doi.org/https://doi.org/10.1016/j.actaastro.2025.01.038
Ingle, P. Y., & Kim, Y.-G. (2025). Multi-sensor data fusion across dimensions: A novel approach to synopsis generation using sensory data. Journal of Industrial Information Integration, 46, 100876. https://doi.org/https://doi.org/10.1016/j.jii.2025.100876
Jiang, J., Li, Y., Nie, J., Li, J., Wen, B., & Gadekallu, T. R. (2025). Integrating large language models with cross-modal data fusion for advanced intelligent transportation systems in sustainable cities development. Applied Soft Computing, 177, 113278. https://doi.org/https://doi.org/10.1016/j.asoc.2025.113278
Jin, H., Zhao, Y.-P., & Wang, Z. (2025). A real-time onboard compressor stall warning method based on attention multiple sensors fusion and lightweight network. Engineering Applications of Artificial Intelligence, 150, 110635. https://doi.org/https://doi.org/10.1016/j.engappai.2025.110635
Karuppiah, A. B., Nanjappan, V., RajaRaja, R., & Priyan, S. V. (2025). Neuro inspired deep learning based secure and energy efficient routing with autonomous intrusion prevention in wireless sensor networks. Engineering Applications of Artificial Intelligence, 162, 112783. https://doi.org/https://doi.org/10.1016/j.engappai.2025.112783
Liu, H., Rahman, M., Rahimi, M., Starr, A., Durazo-Cardenas, I., Ruiz-Carcel, C., Ompusunggu, A., Hall, A., & Anderson, R. (2023). An autonomous rail-road amphibious robotic system for railway maintenance using sensor fusion and mobile manipulator. Computers and Electrical Engineering, 110, 108874. https://doi.org/https://doi.org/10.1016/j.compeleceng.2023.108874
Liu, J., Yu, H., Huang, A., Ma, X., Wu, B., Sun, J., Jia, L., Chen, Y., Wang, Y., Wang, J., Yan, X., & Guedes Soares, C. (2025). Concepts, key technologies, applications and development trends in autonomous transportation systems. Autonomous Transportation Research, 1(1), 1–23. https://doi.org/https://doi.org/10.1016/j.atres.2025.12.002
Mayberry, S., Cai, J., Yang, R., Wang, J., & Zhang, F. (2025). An open-source underwater robotics platform for aquatic research & exploration. HardwareX, 24, e00715. https://doi.org/https://doi.org/10.1016/j.ohx.2025.e00715
Miao, Z., Shao, C., Li, H., Cui, Y., & Tang, Z. (2025). Adaptive sensor attack detection and defense framework for autonomous vehicles based on density. Computers & Security, 148, 104149. https://doi.org/https://doi.org/10.1016/j.cose.2024.104149
Nam, D. Van, Danh, P. T., Park, C. H., & Kim, G.-W. (2024). Fusion consistency for industrial robot navigation: An integrated SLAM framework with multiple 2D LiDAR-visual-inertial sensors. Computers and Electrical Engineering, 120, 109607. https://doi.org/https://doi.org/10.1016/j.compeleceng.2024.109607
Nawaz, M. W., Zhang, W., Flynn, D., Zhang, L., Swash, R., Abbasi, Q. H., Imran, M. A., & Popoola, O. (2024). 6G edge-networks and multi-UAV knowledge fusion for urban autonomous vehicles. Physical Communication, 67, 102479. https://doi.org/https://doi.org/10.1016/j.phycom.2024.102479
Noh, B., Park, M., Han, Y., & Kim, J. (2025). A multi-modal approach for detecting drivers’ distraction using bio-signal and vision sensor fusion in driver monitoring systems. Engineering Applications of Artificial Intelligence, 161, 112265. https://doi.org/https://doi.org/10.1016/j.engappai.2025.112265
Oh, H.-W., Kang, J.-W., & Hong, Y.-D. (2025). Predicting elbow motion intention based on different electromyography and inertial measurement unit sensor fusion structure. Robotics and Autonomous Systems, 191, 105029. https://doi.org/https://doi.org/10.1016/j.robot.2025.105029
Parsons, T., Hanafi Sheikhha, F., Seo, J., & Lee, H. (2024). RGB-LiDAR sensor fusion for dust de-filtering in autonomous excavation applications. Automation in Construction, 168, 105850. https://doi.org/https://doi.org/10.1016/j.autcon.2024.105850
Razack, R. K., Poovadichalil, N. M., & Sadasivuni, K. K. (2025). Toward autonomous medicine: A comprehensive review of biomedical energy harvesting and wearable sensing systems. Nano Energy, 145, 111422. https://doi.org/https://doi.org/10.1016/j.nanoen.2025.111422
Shangguan, H., Guan, X., Li, H., Lin, M., Zhang, Y., & Wang, Y. (2025). Wavelet Fusion Network for Signal Denoising This work was supported by the Science Center Program of National Natural Science Foundation of China (No.62188101), the National Key R&D Program of China (No.2023YFC3305600), and China Scholarship Council (No.202306120304). We sincerely appreciate the Education Center of Experiments and Innovations (Analysis and Testing Center) at Harbin Institute of Technology, Shenzhen, for their support. IFAC-PapersOnLine, 59(20), 1641–1646. https://doi.org/https://doi.org/10.1016/j.ifacol.2025.11.393
Sharifi, A., Safari, M. M., & Alabdullah, B. (2025). Advancing coastal land use mapping through deep multi-label classification and multi-sensor data fusion. The Egyptian Journal of Remote Sensing and Space Sciences, 28(4), 681–689. https://doi.org/https://doi.org/10.1016/j.ejrs.2025.10.004
Shi, P., Yang, L., Dong, X., Qi, H., & Yang, A. (2025). Research Progress on Multi-Modal Fusion Object Detection Algorithms for Autonomous Driving: A Review. Computers, Materials and Continua, 83(3), 3877–3917. https://doi.org/https://doi.org/10.32604/cmc.2025.063205
Su, Y., Shi, X., & Song, T. (2025). Research on Vehicle Safety Based on Multi-Sensor Feature Fusion for Autonomous Driving Task. Computers, Materials and Continua, 83(3), 5831–5848. https://doi.org/https:/doi.org/10.32604/cmc.2025.064036
Sun, X., Ma, H., Wang, B., Chen, D., Song, Q., & Liu, Z. (2025). Quantitative analysis of milling chatter severity based on image-signal multi-modal data fusion. Journal of Manufacturing Processes, 150, 501–515. https://doi.org/https://doi.org/10.1016/j.jmapro.2025.06.087
Tao, R., Feng, Y., Chang, C., Gu, C., Shan, C., Zhang, X., & Mao, Y. (2025). A hierarchically porous AgNW-TPU composite with “pores-in-pore” architecture for bifunctional flexible sensors and energy harvesters. Chemical Engineering Journal, 525, 170110. https://doi.org/https://doi.org/10.1016/j.cej.2025.170110
Thakur, A., & Mishra, S. K. (2024). An in-depth evaluation of deep learning-enabled adaptive approaches for detecting obstacles using sensor-fused data in autonomous vehicles. Engineering Applications of Artificial Intelligence, 133, 108550. https://doi.org/https://doi.org/10.1016/j.engappai.2024.108550
Wang, A., Guo, Y., & Chen, H. (2025). Sensor data fusion optimization in UAV integrated navigation based on matrix factorization. Array, 28, 100524. https://doi.org/https://doi.org/10.1016/j.array.2025.100524
Wang, G., Chen, Y., Wu, H., & Yan, B. (2025). Research on Multi-Sensor Data Fusion-Based Localization of Underwater Robots in Confined Spaces. IFAC-PapersOnLine, 59(22), 171–176. https://doi.org/https://doi.org/10.1016/j.ifacol.2025.11.627
Wang, W., Qi, L., & Jie, Z. (2025). Enhanced sensor fusion and adaptive control for UAV fire control systems: A quantitative evaluation of graceful degradation under adverse conditions. Ain Shams Engineering Journal, 16(10), 103613. https://doi.org/https://doi.org/10.1016/j.asej.2025.103613
Wang, Z., Huang, P., Wu, X., & Liu, J. (2025). Field-validated VIO-MPC fusion for autonomous headland turning in GNSS-denied orchards. Smart Agricultural Technology, 12, 101373. https://doi.org/https://doi.org/10.1016/j.atech.2025.101373
Xu, N., Guan, M., & Wen, C. (2025). A survey on Ultra Wide Band based localization for mobile autonomous machines. Journal of Automation and Intelligence, 4(2), 82–97. https://doi.org/https://doi.org/10.1016/j.jai.2025.02.001
Yang, K. O., Koh, J., & Choi, J. W. (2025). UCFFormer: Recognizing human actions from multimodal sensors using unified contrastive fusion transformer. Neurocomputing, 655, 131374. https://doi.org/https://doi.org/10.1016/j.neucom.2025.131374
Yayla, R., Üçgün, H., & Korkmaz, O. A. (2025). An Embedded Computer Vision Approach to Environment Modeling and Local Path Planning in Autonomous Mobile Robots. CMES - Computer Modeling in Engineering and Sciences, 145(3), 4055–4087. https://doi.org/https://doi.org/10.32604/cmes.2025.072703
Ye, J., Huang, J., Kuang, J., Tang, J., Liu, C., Li, R., & Tan, G. (2025). Data-driven methods for early warning of battery thermal runaway: A review of multi-signal fusion and machine learning approaches. Journal of Energy Storage, 133, 118043. https://doi.org/https://doi.org/10.1016/j.est.2025.118043
Yin, R., Wu, H., Li, M., Cheng, Y., Song, Y., Pan, H., & Handroos, H. (2025). Mastering autonomous assembly in fusion application with learning-by-doing: A peg-in-hole study. Engineering Applications of Artificial Intelligence, 160, 111897. https://doi.org/https://doi.org/10.1016/j.engappai.2025.111897
Zha, L., Gong, C., & Lv, K. (2025). Real-time localization and navigation method for autonomous vehicles based on multi-modal data fusion by integrating memory transformer and DDQN. Image and Vision Computing, 156, 105484. https://doi.org/https://doi.org/10.1016/j.imavis.2025.105484
Zhang, H., Lu, X., Chen, F., Gong, X., & Tan, H. (2025). Inference of takeover time budget for level 3 autonomous vehicles using triboelectric sensors and hybrid learning. Chemical Engineering Journal, 515, 163749. https://doi.org/https://doi.org/10.1016/j.cej.2025.163749
Zhang, K., Liu, S., An, S., Zhou, Y., & Pu, X. (2025). Towards high-efficiency self-powered wireless sensor networks: A systematic review of co-design of energy and signal. Nano Energy, 144, 111337. https://doi.org/https://doi.org/10.1016/j.nanoen.2025.111337
Zhu, W., Chen, X., & Jiang, L. (2025). PV-LaP: Multi-sensor fusion for 3D Scene Understanding in intelligent transportation systems. Signal Processing, 227, 109749. https://doi.org/https://doi.org/10.1016/j.sigpro.2024.109749
Authors
Copyright (c) 2026 Justam Justam, Liu Yang, Chen Mei
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
