Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements

You, Heewon; Byun, Sung‒Hoon; Choo, Youngmin

doi:10.3390/s22145088

Cited by 4 publications

(4 citation statements)

References 22 publications

(45 reference statements)

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“…The theme of this Special Issue focuses on underwater signal and ocean signal processing. This Special Issue highlights 12 articles that can be divided into four categories: optimization of ship navigation [1,2], underwater acoustic communication [3][4][5][6], underwater acoustic signal recognition [7][8][9], and underwater detection and positioning [10][11][12]. In addition to traditional underwater acoustic signals, research objects also include underwater sensors, underwater environments, ships, underwater images, etc.…”

Section: Discussionmentioning

confidence: 99%

“…Reducing noise and efficiently acquiring target underwater acoustic signals by sensors are equally crucial in signal recognition. In [8], the researchers proposed a method using Hidden Markov Models (HMM) to detect sequence acoustic data without separate training data. The stability and accuracy of detecting signals of interest (SOI) were improved using genetic algorithms and multiple measurements.…”

Section: Underwater Acoustic Signal Recognitionmentioning

confidence: 99%

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Recent Advances in Underwater Signal Processing

Zhang¹,

Sun

2023

Sensors

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Section: Discussionmentioning

confidence: 99%

Section: Underwater Acoustic Signal Recognitionmentioning

confidence: 99%

Recent Advances in Underwater Signal Processing

Zhang¹,

Sun

2023

Sensors

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“…Acoustic features represent the frequency characteristics of each phoneme to be recognized and are termed acoustic models. As an acoustic model expression, a hidden Markov model with a mixed normal distribution as the output probability is widely used [28][29][30]. Linguistic features represent restrictions on the arrangement of phonemes and are called language models.…”

Section: Recognition Technologymentioning

confidence: 99%

Development and Practical Applications of Computational Intelligence Technology

Matsuzaka,

Yashiro

2024

BioMedInformatics

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Computational intelligence (CI) uses applied computational methods for problem-solving inspired by the behavior of humans and animals. Biological systems are used to construct software to solve complex problems, and one type of such system is an artificial immune system (AIS), which imitates the immune system of a living body. AISs have been used to solve problems that require identification and learning, such as computer virus identification and removal, image identification, and function optimization problems. In the body’s immune system, a wide variety of cells work together to distinguish between the self and non-self and to eliminate the non-self. AISs enable learning and discrimination by imitating part or all of the mechanisms of a living body’s immune system. Certainly, some deep neural networks have exceptional performance that far surpasses that of humans in certain tasks, but to build such a network, a huge amount of data is first required. These networks are used in a wide range of applications, such as extracting knowledge from a large amount of data, learning from past actions, and creating the optimal solution (the optimization problem). A new technique for pre-training natural language processing (NLP) software ver.9.1by using transformers called Bidirectional Encoder Representations (BERT) builds on recent research in pre-training contextual representations, including Semi-Supervised Sequence Learning, Generative Pre-Training, ELMo (Embeddings from Language Models), which is a method for obtaining distributed representations that consider context, and ULMFit (Universal Language Model Fine-Tuning). BERT is a method that can address the issue of the need for large amounts of data, which is inherent in large-scale models, by using pre-learning with unlabeled data. An optimization problem involves “finding a solution that maximizes or minimizes an objective function under given constraints”. In recent years, machine learning approaches that consider pattern recognition as an optimization problem have become popular. This pattern recognition is an operation that associates patterns observed as spatial and temporal changes in signals with classes to which they belong. It involves identifying and retrieving predetermined features and rules from data; however, the features and rules here are not logical information, but are found in images, sounds, etc. Therefore, pattern recognition is generally conducted by supervised learning. Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning.

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“…However, this method faces a high time complexity during the SD process. You et al 36 considered the hidden Markov model (HMM) for SD in UWAC‐OFDM systems. In addition, a genetic algorithm was introduced to optimize the parameters of the HMM model.…”

Section: Related Workmentioning

confidence: 99%

Optimized deep learning driven signal detection and adaptive channel estimation in underwater acoustic IoT networks

Kapileswar,

Phani Kumar

2023

Int J Communication

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SummaryNowadays, multiple input multiple outputs with orthogonal frequency division multiplexing (MIMO‐OFDM) provide better communication performance that can be applied to the fast‐growing Internet of Things (IoTs). In underwater IoT, information fades away rapidly due to varying water conditions. Therefore, the MIMO model can be applied to the OFDM acoustic system, enabling high‐speed data transmission without affecting the channel effectively. However, detecting the underwater signal and estimating the channel is highly necessary for enhancing underwater acoustic communication (UWAC). Recently, many techniques have been introduced for effectively performing signal detection and channel estimation. However, those techniques face high time complexity due to increased channel interferences and noises during data transmission. Hence, this article brings a novel technique for SD and CE for the UWAC‐IoT‐enabled MIMO‐OFDM system. An adaptive recursive least square (ARLS) technique is proposed in this study for CE that aids in evaluating the acoustic channel parameters effectively. For performing SD, a bi‐directional deep pelican convolutional neural network (BDPCNN) technique is introduced to ensure the presence and absence of signals at the receiver end. The proposed method is analyzed via the MATLAB platform, and the performances are analyzed under different water types like turbid water, coastal water, clear ocean water, and pure seawater. Different performance metrics like bit error rate (BER), mean square error (MSE), energy efficiency (EE), and time complexity are analyzed with different existing techniques. The experimental section obtains the BER of 0.0086, 0.013, 0.017, and 0.021 for turbid, coastal, clear, and pure seawater, respectively.

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Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements

Cited by 4 publications

References 22 publications

Recent Advances in Underwater Signal Processing

Recent Advances in Underwater Signal Processing

Development and Practical Applications of Computational Intelligence Technology

Optimized deep learning driven signal detection and adaptive channel estimation in underwater acoustic IoT networks

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