Underwater wireless sensor networks (UWSNs) widely used for maritime object detection or monitoring of oceanic parameters that plays vital role prediction tsunami to life-cycle marine species by deploying nodes at random locations. However, the dynamic and unpredictable underwater environment poses significant challenges in communication, including interference, collisions, energy inefficiency. In changing make routing possible among or/and base station (BS) an adaptive receiver-initiated...

Extracting useful features from ship-radiated noise can improve the performance of passive sonar. The entropy feature is an important supplement to existing technologies for ship classification. However, extraction methods are less reliable under noisy conditions because they lack reduction procedures or single-scale based. In order simultaneously solve these problems, a new method proposed based on improved complementary ensemble empirical mode decomposition with adaptive (ICEEMDAN),...

Parameter estimation of Direction Arrival (DOA) using deterministic and stochastic computing paradigms is an enabling development for underwater acoustic signal processing beside its applications in the field seismology, astronomy, earthquake bio-medicine. In this work, comparative study between state art heuristics algorithms presented viable DOA different dynamic objects. A Uniform Linear Array (ULA) eight hydrophones used impinging waves from far-field targets. order to evaluate...

In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation underwater passive target. scenarios, real-time motion parameters objects are usually extracted filtering techniques. algorithms, measurements associated linear kinetics target, governing by space methodology. To improve tracking accuracy, effective feature and minimizing position...

In underwater environments, the accurate estimation of state features for passive object is a critical aspect various applications, including robotics, surveillance, and environmental monitoring. This study presents an innovative neuro computing approach instantaneous reckoning marine following dynamic Markov chains. paper introduces potential intelligent Bayesian regularization backpropagation (IBRBNC) precise object. The proposed paradigm combines power artificial neural network with...

The traditional target tracking is a process of estimating the state moving using measurement information obtained by sensors. However, underwater passive acoustic will confront further challenges, among which system incomplete observability and time delay caused signal propagation create great impact on performance. Passive sensors cannot accurately obtain range information. introduction Doppler frequency can improve performance; be ignored in environments. It varies with time, has...

To minimize the major decline in direction of arrival (DOA) estimation performance for an acoustic vector sensor array (AVSA) with coexistence axial deviation and non-uniform noise, a two-step iterative minimization (TSIM) method is proposed this paper. Initially, measurement model AVSA formulated by incorporating disturbance parameter into signal model, then novel manifold matrix defined to estimate sparse power noise mutually. After that, mitigate joint optimization problem achieve power,...

In this paper, an application of spherical radial cubature Bayesian filtering and smoothing algorithms is presented to solve a typical underwater bearings only passive target tracking problem effectively. Generally, problems in the ocean environment are represented with state-space model having linear system dynamics merged nonlinear measurements, analyzed algorithms. present scheme, efficiently investigated for accurate state estimation far-field moving complex environments. The Kalman...

This study proposes a novel application of neural computing based on deep learning for the real-time prediction motion parameters underwater maneuvering object. The intelligent strategy utilizes capabilities Scaled Conjugate Gradient Neural Intelligence (SCGNI) to estimate dynamics target that adhere discrete-time Markov chain. Following state-space methodology in which are combined with noisy passive bearings, nonlinear probabilistic computational algorithms frequently used applications...

In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation the Markov chain underwater maneuvering object. The designed intelligent strategy exploiting strength nonlinear autoregressive with exogenous input (NARX) network model, which has capability estimating dynamics systems that follow discrete-time chain. Nonlinear Bayesian filtering techniques are often applied applications by following state-space methodology. robustness...

Deploying and effectively utilizing wireless sensor networks (WSNs) in underwater habitats remains a challenging task. In sensors (UWSNs), the availability of continuous energy source for communicating with nodes is either very costly or prohibited due to marine life law enforcement agencies. So, order address this issue, we present Q-learning-based approach designing an energy-efficient medium access control (MAC) protocol UWSNs through collision avoidance. The main goal prolong network’s...

Compared with the traditional tracking problem, underwater 3-D passive manoeuvring target faces following challenging problems: (1) Since signal propagation delay is varying and unknown, relationship between two adjacent states hard to describe; (2) limited by environment, it more difficult establish a motion model; (3) speed of acoustic varies depth, path will be curved instead straight. Various parameters including delay, pitch angle receiving frequency measured observer cannot calculated...

Developing the parameter estimation, particularly direction of arrival (DOA), utilizing swarming intelligence-based flower pollination algorithm (FPA) is considered an optimistic solution. Therefore, in this paper, features FPA are applied for viable DOA case several robust underwater scenarios. Moreover, acoustic waves impinging from far-field multitarget evaluated using different number hydrophones uniform linear array (ULA). The measuring parameters like robustness against noise and...

Optimal passive target tracking in noisy ocean environments is an important research problem. This issue frequently dealt with nonlinear Bayesian filtering techniques, which measurements are while the dynamic system model considered linear. The key objective of underwater object to accurately find motion parameters over uncertain extracted from array elements. In this paper, rule unscented transform used examine convergence Kalman filter (UKF) and Rauch Tung Striebel (URTS) type post...

A major advantage of the use passive sonar in tracking multiple underwater targets is that they can be kept covert, which reduces risk being attacked. However, nonlinearity Doppler and bearing measurements, range unobservability problem, complexity data association between measurements make problem target challenging. To deal with these problems, cardinalized probability hypothesis density (CPHD) recursion, based on Bayesian information theory, developed to handle uncertainty, acquire...

Recently, many direction-of-arrival (DOA) estimation techniques based on sparse representation have been proposed. However, these often suffer from performance degradation issues in the presence of impulsive noise. This paper aims to overcome this challenge conventional sparse-based an acoustic vector sensor array (AVSA). Firstly, remove high outliers output data, information AVSA is weighted by using infinite norm. To further suppress outliers, a p-order cost function formulated extending...

This paper presents a strategy called the alternating iterative minimization method (AIMM), aimed at enhancing precision of direction arrival (DOA) estimation when utilizing an acoustic vector sensor array (AVSA) with unknown swing deviation elements (SDEs). The AVSA model SDEs is formulated by incorporating parameter. Later, to estimate matrix (SDM) and sparse signal power using iteration method, auxiliary cost functions respect SDM are based on regularized weighted least squares (RWLS)...