Citation :

Afsar Ali, Kaja Mohideen, Vedachalam, "Physics-Informed Hybrid Marine Predator and Harris Hawks Optimization for Deep-Sea Black Box Localization Using Passive Sonar Modeling," International Journal of Electrical and Electronics Engineering, vol. 13, no. 7, pp. 91-112, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I7P105

Abstract

Locating Underwater Locator Beacons (ULBs) in deep-sea conditions is still a significant challenge due to the severe Attenuation (acoustic) and complex multipath transmission, and the presence of high ambient ocean noise. Current localization methods tend to be based on simplified acoustic propagation models or computationally intensive physics-based solvers, thus limiting the accuracy of localization and the ability to deploy it in real-time in practical Search and Rescue (SAR) missions. In a bid to overcome these shortcomings, this paper suggests a physics-informed hybrid optimization framework of localizing black boxes in the deep sea through a combination of passive sonar modeling and a hybrid Marine Predator Algorithm-Harris Hawks Optimization (MPA-HHO) strategy. The proposed structure uses the standard passive sonar equation and realistic transmission loss models that consider the effects of spherical spreading, frequency-dependent absorption (approximately 6.57 dB/km at 37.5 kHz), and major deep-water propagation processes such as surface reflection, surface ducting, bottom bouncing, convergence zone propagation, deep sound channel effects, and dependable acoustic paths. The proposed hybrid model is in contrast to traditional standalone optimization models that typically do not incorporate the global exploration properties of MPA or the effective local exploitation properties of HHO to enhance localization accuracy and convergence behavior. The results of the simulation under the realistic conditions of a deep-ocean environment show that the proposed structure reaches a high level of fitness of 10.1 dB and that it converges in almost 45 iterations. The distance error is minimized to 0.03 km with a probability of detection of 0.98 in a 3 km range. Moreover, the framework reduces the search area of 10,000 km² to about 3.14 km2, which corresponds to over 99.9% search-space reduction, and only requires 11.8 seconds of execution time, a nearly 87% reduction in computational complexity compared to traditional ray-tracing techniques. The findings prove that the incorporation of realistic ocean acoustic physics with adaptable hybrid bio-inspired optimization has offered a robust, computationally efficient and reliable solution to deep-sea underwater locator beacon detection and near real-time SAR operations.

Keywords

Underwater Locator Beacon, Passive Sonar, Deep-Sea Acoustics, Transmission Loss Modeling, Multipath Propagation, Marine Predator Algorithm, Harris Hawks Optimization, Hybrid Metaheuristic Optimization, Search And Rescue.

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