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Active and passive sonar are the two types of empirical underwater acoustic channel models (UWACMs). Passive sonar UWACMs have applications in military, ocean exploration, and search and rescue (SAR) activities. However, high transmission loss (TL), multipath propagation, and ambient noise pose significant challenges to signal-to-noise ratio (SNR) and communication effectiveness. To address these challenges, this paper develops a UWACM based on the passive sonar equation method to determine SNR in deep-sea environments, specifically for SAR operations. Determining SNR involves characterizing signal propagation in terms of TL. Existing models lack analysis of TL and SNR for various deep-sea multipath propagation scenarios relevant to SAR applications. Therefore, this paper analyses TL and SNR for both direct and various multipath propagation modes, including surface reflection (SR), surface duct (SD), bottom bounce (BB), convergence zone (CZ), deep sound channel (DSC), and reliable acoustic paths (RAPs) in the deep sea. This work aims to quantify the detection capabilities of underwater location beacons (ULBs) under various deep-sea scenarios and configurations. By analyzing ULB signal propagation characteristics, this research seeks to address key challenges related to ULB performance and ultimately improve SAR operations. The results of the proposed model significantly correlate with existing literature, confirming its accuracy. [ABSTRACT FROM AUTHOR]

Selecting the number of sensors to be actually used in acoustical signal beamformings that employ a given sensor array with a fixed number of elements is important for performance optimization in signal detection applications. Adding sensors to the beamforming computation might introduce excess noise into the measured signal, but selecting too few sensors reduces the beampattern resolution. Additionally, the sensor signals may be weighted to compensate for the background noise or known signal sources. In this paper, a maximum likelihood estimator is developed for the signal beamforming and the signal-to-noise ratio is used as a key performance indicator for determining the point at which adding sensors deteriorates the overall signal readout efficiency. As a case study, an experimental onshore passive sonar readout chain is analyzed through signal beamforming with a circular array with 32 sensors. The experimental results were supported by simulations and showed that the described method was able to calibrate the delay-and-sum algorithm for its best performance under noisy conditions. Good agreement has been obtained with a heuristic approximation from expert knowledge, and the method expands it to general applications. [ABSTRACT FROM AUTHOR]

The target motion element is important information in anti-submarine warfare, and its calculation results have a great influence on the hitting probability of the target, thus affecting combat decision-making. At present, active sonar is the main source of information in the calculation method for target motion elements in anti-submarine warfare of surface ships. However, active sonar uses a fixed number of sending periods, and there are gaps in the target information during the continuous tracking process. As a result, there are large errors and slow convergence in the calculation results of the target motion elements. In order to obtain the target motion elements more quickly and accurately, the detection information of passive sonar was added to the filtering process. The unscented Kalman filter(UKF) method was used to simulate the information detection methods using only active sonar and both active and passive sonars, and the results were compared. The simulation results show that under the same conditions, the proposed method can significantly improve the convergence accuracy and speed compared with the traditional method. It can improve the calculation accuracy of speed, azimuth, and heading angle by 33.55%, 38.99%, and 35.29% on average, verifying its effectiveness.

Objective: Artificial intelligence is a part of computer science that emphasizes the creation of intelligent machines in defense equipment and military equipment. Intelligent systems for automatic underwater target recognition are increasingly used in passive sonar to reduce human intervention and related challenges in accurately identifying vessels. Today, highly advanced methods of machine learning and deep learning are being used by the world's navies to identify acoustic targets.Methodology: In this article, recent works in the field of automatic underwater acoustic target recognition are reviewed, and a new method based on deep learning algorithms is presented. In this method, first, the raw audio signals are received from the hydrophones, and after performing the necessary pre-processing, using the Short-time Fourier transform, the spectrogram images related to the passive sonar acoustic data are generated and fed to the model layers for model validation and classification.Results: The obtained results show that the multi-layer structures of the proposed model can automatically extract several features are required for the classification of different ship classes. In this article, common deep learning algorithms are used to identify targets, which can increase identification accuracy and reduce evaluation errors by searching for the most informative features of sonar data.Conclusion: The obtained results show that the recognition accuracy of the proposed model is more than 97%, and its validation loss is less than 3%. In this method, with the relative improvement of classification accuracy, the speed of target recognition has increased significantly.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Beamforming technology is very important for passive sonar to detect targets. However, the performance of a beamformer is seriously degraded in practical applications due to the complex and changeable underwater environment. In this paper, a null broadening algorithm for passive sonar based on a weighted similarity vector is proposed for underwater fast-moving strong interference signals. First, the covariance matrix was reconstructed through the correlation between the steering vector and the subspace eigenvector, which was used to calculate the similarity vector. Then, the maximum power in the interference angle sector was used as the virtual interference source power to broaden the null in the angle sector. Next, the difference between the optimal weight vector and the similar vector was minimized, the interference-plus-noise power constraints and norm constraints were added, and the equation was written as a quadratic constrained quadratic programming (QCQP) problem, which was converted into a convex optimization problem by using the semidefinite relaxation technique. Finally, the optimal solution was calculated by using eigen decomposition. The simulation results show that the algorithm can guarantee deep nulling and effectively suppress sidelobe height under various error conditions, which shows that the proposed algorithm has a good suppression effect and strong robustness for fast strong interference. [ABSTRACT FROM AUTHOR]

The low-frequency narrowband components (known as lines) in the radiated noise of underwater acoustic targets are an important feature of passive sonar detection. Conventional adaptive line enhancer (ALE) based on the least mean square algorithm has limited performance under colored background noise and low signal-to-noise ratio (SNR). In this paper, by combining the frequency domain sparse model of lines and maximum correntropy criterion (MCC), a β-adaptive l0-MCC-ALE is proposed to solve the above-mentioned problem. The proposed ALE uses a sparse-driven MCC algorithm to update the weight vector in the frequency domain to further suppress the colored background noise. For the problem that the value of parameter β is sensitive to the performance, β is updated adaptively according to the frequency response of ALE in each iteration. Simulation and real data processing results show that the proposed ALE is insensitive to the given parameter β and has excellent performance for line enhancement. Compared with conventional ALE, the SNR of lines can be improved by 7~8 dB. [ABSTRACT FROM AUTHOR]

In submarine warfare systems, passive SONAR is commonly used to detect enemy targets while concealing one's own submarine. The bearing information of a target obtained from passive SONAR can be accumulated over time and visually represented as a two-dimensional image known as a BTR image. Accurate measurement of bearing–time information is crucial in obtaining precise information on enemy targets. However, due to various underwater environmental noises, signal reception rates are low, which makes it challenging to detect the directional angle of enemy targets from noisy BTR images. In this paper, we propose a deep-learning-based segmentation network for BTR images to improve the accuracy of enemy detection in underwater environments. Specifically, we utilized the spatial convolutional layer to effectively extract target objects. Additionally, we propose novel loss functions for network training to resolve a strong class imbalance problem observed in BTR images. In addition, due to the difficulty of obtaining actual target bearing data as military information, we created a synthesized BTR dataset that simulates various underwater scenarios. We conducted comprehensive experiments and related discussions using our synthesized BTR dataset, which demonstrate that the proposed network provides superior target segmentation performance compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Beamforming technology is very important for passive sonar to detect targets. However, the performance of a beamformer is seriously degraded in practical applications due to the complex and changeable underwater environment. In this paper, a null broadening algorithm for passive sonar based on a weighted similarity vector is proposed for underwater fast-moving strong interference signals. First, the covariance matrix was reconstructed through the correlation between the steering vector and the subspace eigenvector, which was used to calculate the similarity vector. Then, the maximum power in the interference angle sector was used as the virtual interference source power to broaden the null in the angle sector. Next, the difference between the optimal weight vector and the similar vector was minimized, the interference-plus-noise power constraints and norm constraints were added, and the equation was written as a quadratic constrained quadratic programming (QCQP) problem, which was converted into a convex optimization problem by using the semidefinite relaxation technique. Finally, the optimal solution was calculated by using eigen decomposition. The simulation results show that the algorithm can guarantee deep nulling and effectively suppress sidelobe height under various error conditions, which shows that the proposed algorithm has a good suppression effect and strong robustness for fast strong interference.

A closer look into the history of maritime geomedia reveals a historical intermezzo in which navigation in cases of unstable sight was realized with "Submarine Signaling" (SS). This infrastructure for the purpose of safe wayfinding along coasts operated from around 1900 to the early 1920s, relying upon submarine bells in the global oceans, and hydrophones and telephonic user interfaces in vessels. SS can be regarded as a starting point to the era of electrotechnological navigation, and is of media-historical interest insofar as its operability was based on undersea acoustics: shifting its epistemic focus from sight to sound, SS thus lay beyond the "visual regime" that characterizes our current digital geomedia cultures. Herein, I will reconstruct the genesis and history of SS, use it as a contrasting foil to our postmodern geomedia practices, and finally argue for an understanding of geomedia as historically variable phenomena. [ABSTRACT FROM AUTHOR]

The detection of tonal signals with unknown frequencies is an important area of study in underwater signal processing. A common approach to address this issue is to use the Discrete Fourier Transform (DFT) for observations. When a tone does not lie precisely at the discrete DFT frequency point, its energy will leak to adjacent frequency point. This phenomenon is known as scalloping loss or Picket Fence Effect (PFE). PFE leads to the degradation of detection performance based on DFT. This paper studies the problem of robust detection in the case of PFE. A coherently-averaged power processor utilizing the information of adjacent frequency bins is designed. The results of simulations and experiments show that the proposed method is robust against PFE, and is highly suitable for tone detection in practical circumstances. [ABSTRACT FROM AUTHOR]

Automated target recognition systems are increasingly employed in sonar systems to reduce manning and associated challenges. Although passive acoustic target recognition is an exceptionally challenging endeavor especially in shallow water scenarios, it is being used by naval forces of the world by virtue of its inherent advantages compared to the alternatives. In order to address these challenges as well as to exploit the latent and subtle features in the signal stream from the hydrophones, an end-to-end differentiable architecture is proposed in this paper. Here the key strategy is to rely on the data, instead of relying on the prior knowledge about the data. The raw acoustic signals from the hydrophones are directly fed to a pre-initialized 1-dimensional convolutional layer followed by a cascade of 2-dimensional convolutional spectro-temporal feature learners. Various auditory scales are used for pre-initializing, so as to emphasize the frequencies of interest. In order to better capture the temporal relations, a Bidirectional-LSTM layer with a trainable attention module is employed. The best configuration of the proposed classifier system yields an accuracy of 95.2% on a large acoustic dataset, collected from the shallows of the Indian ocean.

In submarine warfare systems, passive SONAR is commonly used to detect enemy targets while concealing one’s own submarine. The bearing information of a target obtained from passive SONAR can be accumulated over time and visually represented as a two-dimensional image known as a BTR image. Accurate measurement of bearing–time information is crucial in obtaining precise information on enemy targets. However, due to various underwater environmental noises, signal reception rates are low, which makes it challenging to detect the directional angle of enemy targets from noisy BTR images. In this paper, we propose a deep-learning-based segmentation network for BTR images to improve the accuracy of enemy detection in underwater environments. Specifically, we utilized the spatial convolutional layer to effectively extract target objects. Additionally, we propose novel loss functions for network training to resolve a strong class imbalance problem observed in BTR images. In addition, due to the difficulty of obtaining actual target bearing data as military information, we created a synthesized BTR dataset that simulates various underwater scenarios. We conducted comprehensive experiments and related discussions using our synthesized BTR dataset, which demonstrate that the proposed network provides superior target segmentation performance compared to state-of-the-art methods.

• The method based on HMM uses features such as spectral line energy and stability. • The algorithm improves the state transition and can detect spectral line length. • The algorithm is fast at extracting weak and adjacent spectral lines. To address the difficulty of extracting weak spectral lines from signals received by passive sonar, we propose a spectral line extraction scheme based on an improved hidden Markov model (HMM). A new state transition probability based on spectral line features is proposed that solves the problem of state transition probability relying on prior information in traditional HMMs. Using a peak detection algorithm and a parallel processing framework reduces computation. We employ the boxplot method to remove the outliers from the spectral lines caused by strong noise and compensate for them. By improving the forward–backward probability calculation method through a peak penalty factor, we manage adjacent spectral lines prone to be missed by traditional HMMs. Lastly, we use dynamic sliding windows to determine a spectral line's birth and death. Data verification by simulations and sea tests show that our algorithm extracts spectral lines better and with a smaller error, accurately detects the birth and death of spectral lines, and is faster than traditional HMM algorithms. [ABSTRACT FROM AUTHOR]

In the realms of the ocean, it becomes a formidable task to detect and classify the passive acoustic targets from the convoluted acoustic mixture confronted by the sonar frontend. Though the advances in deep learning driven by enormity of data and computational infrastructure have resulted in a tremendous leap in performance across various domains, passive sonar target recognition still remains an elusive task for the acoustic as well as signal processing communities. Various channel related artifacts together with the inherent difficulty in obtaining annotated data limit the target records required for training these massive supervised networks so as to yield an optimal performance. This demands models that can generalize well beyond the often sparse training instances. In order to address this issue, generative frameworks can be utilized to model the causal attributes of the target signature so that the network becomes tolerant to the distortions induced by the ambient noise and channel artifacts. This paper exploits the generative modelling capability of an Auxiliary Classifier Generative Adversarial Network (ACGAN) to construct a data-efficient underwater target classifier. These class-conditioned frameworks based on unsupervised representation learning can model the true data distribution using the latent attributes of the training data. In order to make the causal factors of variation more explicit, the raw time domain samples are transformed into joint time–frequency representations using filterbanks initialized at different perceptual scales. Experimental evaluation of the proposed system on target instances collected from diverse locations of the Indian Ocean yields promising results in terms of data efficiency, class confidence and classification accuracy.

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents the results of an innovative approach in the underwater domain of research related to the identification, classification and recognition of maritime targets using acoustic data processed. The "Acoustic Signature" is specific to each target type; it is usually produced by the vibration of the propulsion system of surface vessels caused by their radiation into the water. Therefore, the processing of the frequencies generated by these vibrations is essential for the analysis and the classification of different target type. The purpose of this study is to build an alternative method to identify and classify targets with passive sonars using the TPWS (Two - Pass Split - Windows) filter. In this process, the signal generated by the target is processed in time frequency domain. Then a TPSW algorithm is applied in the frequency domain to reduce the background noise and enhance the frequency lines of the target noise. Finally, an artificial intelligence model is applied to classify targets, taking as inputs the narrowband and the broadband analysis. This classification is based on deep learning process, relied on the training, validation, and test phases in order to enhance the accuracy and reduce the loss. Our results showed that the suggested method is accurate (appx 83.5% SNR = 0db), depending essentially on the signal/noise ratio. [ABSTRACT FROM AUTHOR]

This article describes the development and testing of a passive sonar, multitarget tracker, and adaptive behavior that enable an autonomous underwater vehicle (AUV) to detect and actively track nearby surface vessels. A planar hull-mounted hydrophone array, originally designed for active sonar, is repurposed for passive sonar use and provides acoustic data to a time-delay-and-sum beamformer that generates multiple angle-only contacts. A particle filter tracker assimilates these contacts with a single-hypothesis data association strategy to estimate the position and velocity of targets. Summary statistics of each track are periodically reported to an onboard database along with qualitative labels. To improve tracking performance, detections trigger an adaptive behavior that maneuvers the AUV to maintain multiple targets in the field of view by minimizing the worst case aspect angle deviation from broadside (across all targets). The tracking system is demonstrated through at-sea experiments in which a Bluefin-21 AUV adaptively tracks multiple surface vessels, including another autonomous platform, in the approaches to Boston Harbor. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• A new measurement model with multi-frame coherent integration is established. • An analytical expression of the likelihood function is derived to match the statistical characteristics of multi-frame measurements accurately. • The effects of the newly established likelihood ratio and the batch length on the algorithm performance is analyzed in detail through theoretical derivations. • Comparative experimental results demonstrate the remarkable performance of the proposed method in improving the detection performance. To address the performance degradation caused by power fluctuations of tones, the multi-frame coherent integration-based track-before-detect (TBD) method is proposed. In the proposed method, the high gain advantage of coherent integration and the information accumulation ability of multi-frame TBD method based on batch processing technique are combined to improve the detection and tracking performance of fluctuating tones. Firstly, a new measurement model is established with batch processing technology and coherent integration across multiple data frames is achieved utilizing the tonal coherence, thus the SNR in measurement is greatly improved. Then, a new likelihood ratio function is derived to match the established measurement model, ensuring that TBD processing can be performed correctly. Finally, the effects of the newly established likelihood ratio and the batch length on the detection performance are analyzed in detail through theoretical derivations. The superiorities and robustness of the proposed method are demonstrated through simulation analysis and processing results for sea experimental data. [ABSTRACT FROM AUTHOR]

Abstract Frequency estimation of a tonal signal in passive sonar systems is crucial to the identification of the marine object. In the conventional techniques, a basis mismatch error caused by the discretization of the frequency domain is unavoidable, resulting in a severe degradation of the object detection quality. To overcome the basis mismatch error, we propose a tonal frequency estimation technique in the continuous frequency domain. Towards this end, we formulate the frequency estimation problem as an atomic norm minimization problem. From the numerical experiments, we show that the proposed technique is effective in identifying the tonal frequency components of marine objects.

The method of measuring bearing (azimuth) of a moving surface (underwater) object in the passive sonar is described. This method is considered to be a modification of signals phase comparing method. It is based on the measurement of the delay time between signals received at different signal reception point. A distinctive feature of this method is the use for signal processing method of coherent accumulation of the spectral components of the received signal, developed by the authors. This method provides increase signal/noise ratio in the reception channels, therefore decrease errors of measuring bearing in the passive sonar.

The detection of tonal signals with unknown frequencies is an important area of study in underwater signal processing. A common approach to address this issue is to use the Discrete Fourier Transform (DFT) for observations. When a tone does not lie precisely at the discrete DFT frequency point, its energy will leak to adjacent frequency point. This phenomenon is known as scalloping loss or Picket Fence Effect (PFE). PFE leads to the degradation of detection performance based on DFT. This paper studies the problem of robust detection in the case of PFE. A coherently-averaged power processor utilizing the information of adjacent frequency bins is designed. The results of simulations and experiments show that the proposed method is robust against PFE, and is highly suitable for tone detection in practical circumstances.

The extremely challenging nature of passive acoustic surveillance makes it a key area of research in Naval Non-Co-operative Target Recognition especially in Anti-Submarine Warfare systems. In shallow waters, the complex acoustics due to the highly varying ambient background noise as well as the multi-modal propagation in the surface-bottom bounded channel makes surveillance even difficult. In this work, an ensemble of Convolutional Neural Networks and Bidirectional Long Short Term Memory stages employing soft attention is used to effectively capture the spectro-temporal dynamics of the target signature. In order to alleviate the overall computational cost associated with the optimal model search in the extensive hyperparameter space, a recursive model elimination scheme, making frugal use of the available resources, is also proposed. Experimental analysis on acoustic target records, collected from the shallows of Arabian Sea, has yielded encouraging results in terms of model accuracy, precision and recall. [ABSTRACT FROM AUTHOR]

At present, the tracking accuracy of underwater passive target tracking is often limited due to models that are overly simple, with low complexity, poor universality, and an inability to learn. In this paper, a cubature Kalman filter (CKF) algorithm based on a gated recurrent unit (GRU) network is proposed. The filter innovation, prediction error, and filter gain obtained from the CKF are used as the input to the GRU network, and the filter error value is used as the output to train the network. End-to-end online learning is carried out using the designed fully connected network, and the current state of the target is predicted. In this paper, a deep neural network based on the GRU architecture is used to convert the tracking prediction problem into a time series prediction problem in the field of artificial intelligence, and its strong fitting ability is used to resolve the uncertainty of the target motion. Simulation results show that an unmanned underwater vehicle (UUV) state estimation method based on the GRU filter proposed in this paper offers better accuracy and stability than the traditional state estimation method.

Abstract For almost 20 years, the University of Florida's Machine Intelligence Laboratory (MIL) at the University of Florida (UF) has been creating SONAR systems for locating underwater transmitters. These systems have helped UF teams achieve significant success in international robotic competitions. However, when students graduate, their knowledge is often lost. This paper documents the process of developing a highly accurate, inexpensive SONAR system for future UF projects. A four-hydrophone array SONAR simulator was created to test a time difference of arrival approach used to estimate the location of a sound sources. The software was then applied to an actual SONAR system built in the laboratory. Two-dimensional graphic representations of the SONAR's accuracy were created to visualize the system's performance. This article summarizes the results. [ABSTRACT FROM AUTHOR]

In this paper, we describe an unmanned underwater vehicle (UUV) behavior designed to track nearby vessels using bearing-only measurements obtained from a rigidly mounted planar hydrophone array—one that was originally designed for active sonar use but is repurposed for passive sonar use. Upon detecting a target, a maneuver is executed to resolve the port/starboard bearing ambiguity. The maneuver is heuristically designed with the aim of reducing the effects of end-fire, self-noise during maneuvers, and other noise/spurious measurements on the bearing ambiguity decision. After resolving the bearing ambiguity, the vehicle continuously adjusts its heading to track the target by keeping it broadside to the array. The performance of this behavior, as well as that of the passive sonar itself, is evaluated through field trials in the approaches to Boston Harbor using a Bluefin-21 UUV. In total, 19 successful behavior tests are described where the UUV resolves the bearing ambiguity and tracks either a static source emitting a predefined waveform or the platform noise of a medium-sized catamaran underway. [ABSTRACT FROM AUTHOR]

This paper proposes a method to enhance the quality of detecting and classifying surface vehicle propeller cavitation noise (VPCN) in shallow water by using the improved Detection Envelope Modulation On Noise (DEMON) algorithm in combination with the modified Convolution Neural Network (CNN). To improve the quality of the VPCN spectrogram signal, we apply the DEMON algorithm while analyzing the amplitude variation (AV) to detect the fundamental frequencies of the VPCN signal. To enhance the performance of the traditional CNN, we adapt the size of the sliding window in accordance with the properties of the VPCN spectrogram data, and also reconstruct the CNN layer structure. As for the results, the fundamental frequencies contented in the VPCN spectrogram data can be detected. The analytical results based on the measured data show that the accuracy of the VPCN classification obtained by the proposed method is above 90%, which is higher than those obtained by traditional methods.

The SPICE (Sparse Iterative Covariance-based Estimation) algorithm estimates the azimuth angle by applying a sparse recovery method to the covariance matrix in the time domain. In this paper, we show how the SPICE algorithm, which was originally formulated in the time domain, can be extended to the frequency domain. Furthermore, we demonstrate, through numerical results, that the performance of the proposed algorithm is superior to that of the conventional frequency domain algorithm. [ABSTRACT FROM AUTHOR]

The traditional passive azimuth estimation algorithm using two hydrophones, such as cross-correlation time-delay estimation and cross-spectral phase estimation, requires a high signal-to-noise ratio (SNR) to ensure the clarity of the estimated target trajectory. This paper proposes an algorithm to apply the frequency diversity technique to passive azimuth estimation. The algorithm also uses two hydrophones but can obtain clear trajectories at a lower SNR. Firstly, the initial phase of the signal at different frequencies is removed by calculating the cross-spectral density matrix. Then, phase information between frequencies is used for beamforming. In this way, the frequency dimension information is used to improve the signal processing gain. This paper theoretically analyzes the resolution and processing gain of the algorithm. The simulation results show that the proposed algorithm can estimate the target azimuth robustly under the conditions of a single target (SNR = −16 dB) and multiple targets (SNR = −10 dB), while the cross-correlation algorithm cannot. Finally, the algorithm is tested by the swell96 data and the South Sea experimental data. When dealing with rich frequency signals, the performance of the algorithm using two hydrophones is even better than that of the conventional broadband beamforming of the 64-element array. This further validates the effectiveness and advantages of the algorithm.

Capon beamforming is often applied in passive sonar to improve the detectability of weak underwater targets. However, we often have no accurate prior information of the direction-of-arrival (DOA) of the target in the practical applications of passive sonar. In this case, Capon beamformer will suffer from performance degradation due to the steering vector error dominated by large DOA mismatch. To solve this, a new robust Capon beamforming approach is proposed. The essence of the proposed method is to decompose the actual steering vector into two components by oblique projection onto a subspace and then estimate the actual steering vector in two steps. First, we estimate the oblique projection steering vector within the subspace by maximizing the output power while controlling the power from the sidelobe region. Subsequently, we search for the actual steering vector within the neighborhood of the estimated oblique projection steering vector by maximizing the output signal-to-interference-plus-noise ratio (SINR). Semidefinite relaxation and Charnes-Cooper transformation are utilized to derive convex formulations of the estimation problems, and the optimal solutions are obtained by the rank-one decomposition theorem. Numerical simulations demonstrate that the proposed method can provide superior performance, as compared with several previously proposed robust Capon beamformers in the presence of large DOA mismatch and other array imperfections.

This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf) and noise less vessels. Generally, in passive sonar, the targets are detected by sonar equation (with constant threshold) that increases the detection error in shallow water. The purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound) is processed in time and frequency domain. For classifying, Bayesian classification is used and posterior distribution is estimated by Maximum Likelihood Estimation algorithm. Finally, target was detected by combining the detection points in both domains using Least Mean Square (LMS) adaptive filter. Results of this paper has showed that the proposed method has improved true detection rate by about 24% when compared other the best detection method. [ABSTRACT FROM AUTHOR]

This paper presents an effective solution to the problems of multi-target tracking in passive sonar. Since bearing information alone is used, target crossing problems arises in passive sonar. Nearest Neighbor Joint Probabilistic Data Association (NNJPDA) is employed for the information processing. Further a prediction mechanism is used, where the track bearing depends only on the predicted value not on the measurements. This leads to the correct assignment of measurements with the track in the clutter and thereby avoids the track loss. The state estimation is then refined by Kalman filtering based on the corrected measurements from the NNJPDA technique. Thus accurate and continuous track is maintained. [ABSTRACT FROM AUTHOR]

A novel stochastic algorithm using pre-processing technique is proposed in this paper to deal with the problem of underwater target tracking using passive Sonar. Pre-processing is a concept of reducing the variance of noise present in the measurements given by sensors. This key step is performed ahead of conventional estimation algorithms. Pre-processed measurements are obtained by taking weighted average of present measurements and projected previous measurements. The method is expected to bring down the variance of noise to a great deal based on the fact that the sensor errors are unbiased by nature. The most attractive feature of this algorithm is the capability to track long range targets in heavy noise environments. The algorithm is tested by running Monte Carlo simulations in Matlab R2009a environment. There, it is shown that the estimation error and the time of convergence of the pre-processing technique based algorithms like pre-processed Unscented Kalman Filter (PP-UKF) and Integrated Unscented Kalman filter (PP-IUKF) are much less compared to their non-pre-processing counterparts namely UKF and IUKF, thus indicating the importance of the proposed novel method. [ABSTRACT FROM AUTHOR]