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Gaussian Mixture Model with Bayesian approach for maximizing RSS-based localization in underwater Wireless Sensor Networks

Kiruthiga V., Narmatha V.

International Journal of Electronics and Telecommunications

2025

Vol. 71, No. 1

317-323

Source localization is a highly challenging and complex task in underwater environments due to uncertainties and unknown sound propagation speed profiles in underwater channels, as well as increased Doppler effects and constraints on the energy sources of the sensor nodes. To address these issues, we propose an energy-efficient J oint G aussian M ixture Model with a Bayesian approach for localization algorithms, aiming to improve Received Signal Strength (RSS) accuracy. In this article, we represent the additive noise using a Gaussian Mixture Model to calculate the maximum likelihood estimation. The Bayesian statistical approach solves the convex optimization problem to find effective globally optimal solutions. These joint methods help mitigate the underwater Doppler spread effects and improve the estimation of sensor node positions. The simulated results are analyzed, and the performance metrics show that the proposed GMM-Bayesian approach is very close to the Cramér-Rao Lower Bound and this method also outperforms other existing localization algorithms in terms of lower Root Mean Squared Error (RMSE) relative to anchor nodes and a better Cumulative Distribution Function (CDF) for localization errors. From the simulation results, it is evident that the proposed approach achieves substantial performance gains in the localization of underwater wireless sensor networks.

Automated tracking and real time following of moving person for robotics applications

Patoliya Jignesh J., Mewada Hiren K.

Journal of Automation Mobile Robotics and Intelligent Systems

2019

Vol. 13, No. 4

31--37

Presently the interaction of robots with human plays an important role in various social applications. Reliable tracking is an important aspect for the social robots where robots need to follow the moving person. This paper proposes the implementation of automated tracking and real time following algorithm for robotic automation. Occlusion and identity retention are the major challenges in the tracking process. Hence, a feature set based identity retention algorithm is used and integrated with robot operating system. The tracking algorithm is implemented using robot operating system in Linux and using OpenCV. The tracking algorithm achieved 85% accuracy and 72.30% precision. Further analysis of tracking algorithm corresponds to the integration of ROS and OpenCV is presented. The analysis of tracking algorithm concludes that ROS linking required 0.64% more time in comparison with simple OpenCV code based tracking algorithm.