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Abstract Bearing‐only tracking of an underwater uncooperative target is essential to defend the sea territory. Considering the influences by uncertain underwater environment, the purpose of this work is to estimate 2‐D locations and velocities of an interested underwater target for non‐Gaussian environment. In this work, a fast particle filter (FPF) based on the traditional particle filter (PF) with novel jet transport (JT) technique is proposed to deal with this problem. Aiming to overcome the heavy computation burden of the traditional PF that limits most of its practical applications, the JT technique can dramatically reduce the computation time and complexity in the particle evolution process, which contributes huge computational complexities to the traditional PF. Then, the proposed FPF is tested through simulations in the 2‐D underwater uncooperative target tracking scenario. Finally, the Monte Carlo simulation results demonstrate that the proposed FPF can track the underwater uncooperative target with the similar accuracies as the traditional PF but only occupies less than 20% of the computational resources.

Bearing‐only tracking of an underwater uncooperative target is essential to defend the sea territory. Considering the influences by uncertain underwater environment, the purpose of this work is to estimate 2‐D locations and velocities of an interested underwater target for non‐Gaussian environment. In this work, a fast particle filter (FPF) based on the traditional particle filter (PF) with novel jet transport (JT) technique is proposed to deal with this problem. Aiming to overcome the heavy computation burden of the traditional PF that limits most of its practical applications, the JT technique can dramatically reduce the computation time and complexity in the particle evolution process, which contributes huge computational complexities to the traditional PF. Then, the proposed FPF is tested through simulations in the 2‐D underwater uncooperative target tracking scenario. Finally, the Monte Carlo simulation results demonstrate that the proposed FPF can track the underwater uncooperative target with the similar accuracies as the traditional PF but only occupies less than 20% of the computational resources. [ABSTRACT FROM AUTHOR]

Successful synthetic aperture sonar target classification depends on the “shape” of the scatterers within a target signature. This article presents a workflow that computes a target-to-target distance from persistence diagrams, since the “shape” of a signature informs its persistence diagram in a structure-preserving way. The target-to-target distances derived from persistence diagrams compare favorably against those derived from spectral features and have the advantage of being substantially more compact. While spectral features produce clusters associated to each target type that are reasonably dense and well formed, the clusters are not well-separated from one another. In rather dramatic contrast, a distance derived from persistence diagrams results in highly separated clusters at the expense of some misclassification of outliers. [ABSTRACT FROM AUTHOR]

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]

Underwater multi-targets tracking has always been a difficult problem in active sonar tracking systems. In order to estimate the parameters of time-varying multi-targets moving in underwater environments, based on the Bayesian filtering framework, the Random Finite Set (RFS) is introduced to multi-targets tracking, which not only avoids the problem of data association in multi-targets tracking, but also realizes the estimation of the target number and their states simultaneously. Usually, the conventional Probability Hypothesis Density (PHD) and Cardinalized Probability Hypothesis Density (CPHD) algorithms assume that the detection probability is known as a priori, which is not suitable in many applications. Some methods have been proposed to estimate the detection probability, but most assume that it is constant both in time and surveillance region. In this paper, we model the detection probability through the active sonar equation. When fixed the false detection probability, we can get the analytic expression for the detection probability, which is related to target position. In addition, this novel detection probability is used in PHD and CPHD algorithms and applied to underwater active sonar tracking systems. Also, we use the adaptive ellipse gate strategy to reduce the computational load in PHD and CPHD algorithms. Under the linear Gaussian assumption, the proposed detection probability is illustrated in both Gaussian Mixture Probability Hypothesis Density (GM-PHD) and Gaussian Mixture Cardinalized Probability Hypothesis Density (GM-CPHD), respectively. Simulation results show that the proposed Pd-GM-PHD and Pd-GM-CPHD algorithms are more realistic and accuratein underwater active sonar tracking systems. [ABSTRACT FROM AUTHOR]

Hydroacoustic surveys are common tools for habitat investigation and monitoring that aid in the realisation of the aims of the EU Marine Directives. However, the creation of habitat maps is difficult, especially when benthic organisms densely populate the seafloor. This study assesses the sensitivity of entropy and homogeneity image texture parameters derived from backscatter strength data to benthic habitats dominated by the tubeworm Lanice conchilega. Side scan sonar backscatter surveys were carried out in 2010 and 2011 in the German Bight (southern North Sea) at two sites approx. 20 km offshore of the island of Sylt. Abiotic and biotic seabed facies, such as sorted bedforms, areas of fine to medium sand and L. conchilega beds with different tube densities, were identified and characterised based on manual expert analysis and image texture analysis. Ground truthing was performed by grab sampling and underwater video observations. Compared to the manual expert analysis, the k- means classification of image textures proves to be a semi-automated method to investigate small-scale differences in a biologically altered seabed from backscatter data. The texture parameters entropy and homogeneity appear linearly interrelated with tube density, the former positively and the latter negatively. Reinvestigation of one site after 1 year showed an extensive change in the distribution of the L. conchilega-altered seabed. Such marked annual fluctuations in L. conchilega tube cover demonstrate the need for dense time series and high spatial coverage to meaningfully monitor ecological patterns on the seafloor with acoustic backscatter methods in the study region and similar settings worldwide, particularly because the sand mason plays a pivotal role in promoting biodiversity. In this context, image texture analysis provides a cost-effective and reproducible method to track biologically altered seabeds from side scan sonar backscatter signatures. [ABSTRACT FROM AUTHOR]

This paper focuses on underwater target tracking based on a multi-static sonar network composed of passive sonobuoys and an active ping. In the multi-static sonar network, the location of the target can be estimated using TDOA (Time Difference of Arrival) measurements. However, since the sensor network may obtain insufficient and inaccurate TDOA measurements due to ambient noise and other harsh underwater conditions, target tracking performance can be significantly degraded. We propose a robust target tracking algorithm designed to operate in such a scenario. First, track management with track splitting is applied to reduce performance degradation caused by insufficient measurements. Second, a target location is estimated by a fusion of multiple TDOA measurements using a Gaussian Mixture Model (GMM). In addition, the target trajectory is refined by conducting a stack-based data association method based on multiple-frames measurements in order to more accurately estimate target trajectory. The effectiveness of the proposed method is verified through simulations.

In this paper, frequency estimation of sinusoidal signals in multiplicative and additive noise is addressed. Based on the parametric localization of distributed sources and the minimax theorem, an eigenanalysis-based frequency estimator is proposed. Furthermore, we present processing and analysis for pseudofrequency estimates in the proposed method. Especially, a priori knowledge of multiplicative noise is not required as compared with the distributed signal parameter estimator (DSPE). Monte Carlo experiments are carried out to evaluate performance. Simulation results confirm that the proposed method provides better performance than the nonlinear least squares (NLS) approach and the conventional MUSIC algorithm for separating closely spaced sinusoidal signals in multiplicative and additive noise. [ABSTRACT FROM PUBLISHER]

The article discusses the use of simulation in the real world and focuses on the research & development of the maritime simulation including the sonar images for detecting and tracking of moving objects.

Multi-sensor sonar tracking has many advantages, such as the potential to reduce the overall measurement uncertainty and the possibility to hide the receiver. However, the use of multi-target multi-sensor sonar tracking is challenging because of the complexity of the underwater environment, especially the low target detection probability and extremely large number of false alarms caused by reverberation. In this work, to solve the problem of multi-target multi-sensor sonar tracking in the presence of clutter, a novel probabilistic multi-hypothesis tracker (PMHT) approach based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF) is proposed. The PMHT can efficiently handle the unknown measurements-to-targets and measurements-to-transmitters data association ambiguity. The EKF and UKF are used to deal with the high degree of nonlinearity in the measurement model. The simulation results show that the proposed algorithm can improve the target tracking performance in a cluttered environment greatly, and its computational load is low. [ABSTRACT FROM AUTHOR]

Background: Developments in electronic tagging technologies have provided unprecedented insight into the movements and behavior of marine predators. Concurrent information on the prey of these tracked animals, however, is mostly lacking. We developed and tested a prototype autonomous echosounder (aka the sonar tag) for deployment on large marine animals intended to provide quantification of their prey fields. Results: The resulting fully autonomous, internally recording prototype sonar tag operated at a power of 1 W and a frequency of 200 kHz. A series of test deployments were successfully conducted on four juvenile female elephant seals (Mirounga angustirostris) captured at the Año Nuevo State Reserve, California, and released short distances away. Translocated seals were instrumented with a sonar tag and a Fastloc GPS tag with an integrated time-depth recorder (TDR). All four animals returned to land after 3-18 days, making dives to depths of up to 778 m. Strong backscattering from the bottom was observed during many dives, indicating an often close association with the seafloor. Numerous observations of strongly scattering targets, potentially indicative of prey, were also made in the water column, often associated with particular dive and movement behaviors. During dives identified as foraging-type and also travel-type, one animal with the acoustic transducer on its head showed successive targets getting increasingly closer to the animal, possibly consistent with prey pursuit. Conclusion: These results demonstrate the value of active acoustic backscattering measurements made from free-ranging animals, complementing the ecological insight afforded by traditional depth- and position-logging tags. Future refinements will include further miniaturization, performance optimization, and extensions in the deployment duration. [ABSTRACT FROM AUTHOR]

This paper suggests using a wideband sonar system to detect and classify human activity in indoor environment. The high bandwidth enables precise tracking of body parts, and its enhanced correlation properties can be used to distinguish between human and nonhuman objects. Maximal Likelihood (ML) criterions to derive kinematic features and processing methods to estimate the subject activity level and activity type were derived and tailored to the wideband sonar. For tracking and association of the echoes reflected from the different body part, an efficient approximation of the sequential ML estimator was derived in the natural time-space domain, which eases the exploitation of the a priori knowledge about the human subject target. For classification of the activity, a weighted two level nested k-nearest neighbor classifier was applied on only four kinematic features. A set of experiments with five subjects, performing three different activity types of standing, walking, and swinging upper limbs, was carried out in a typical indoor environment. The proposed technology has managed to classify well the different activity types and demonstrated the potential of this technology for continuous assessment of various kinematic features of humans in indoor environment with reduced costs, under any light, smoke, or humidity conditions. This can be useful for instance for monitoring patients at home, and for detecting intruders. [ABSTRACT FROM PUBLISHER]

In this paper, we address the problem of building a grid map as accurately as possible using inexpensive and error-prone sonar sensors. In this research area, incorrect sonar measurements, which fail to detect the nearest obstacle in their beamwidth, generally have been dealt with in the same manner as correct measurements or have been excluded from the mapping. In the former case, the map quality may be severely degraded. In the latter case, the resulting map may have insufficient information after the incorrect measurements are removed because only correct measurements are frequently insufficient to cover the whole environment. We propose an efficient grid-mapping approach that incorporates incorrect measurements in a specialized manner to build a better map; we call this the enhanced maximum likelihood (eML) approach. The eML approach fuses the correct and incorrect measurements into a map based on sub-maps generated from each set of measurements. We also propose the maximal sound pressure (mSP) method to detect incorrect sonar readings using the sound pressure of the waves from sonar sensors. In several indoor experiments, integrating the eML approach with the mSP method achieved the best results in terms of map quality among various mapping approaches. We call this the maximum likelihood based on sub-maps (MLS) approach. The MLS map created using only two sonar sensors exhibited similar accuracy to the reference map, which was an accurate representation of the environment. [ABSTRACT FROM AUTHOR]

Helicopter Long Range Active Sonar (HELRAS) is deployed from navy helicopters to detect submarines. Its worldwide use by NATO navies is expected to increase due to the increasing need to detect sub-marines at greater distances. The 1.33 to 1.43 kHz signals vary in spectrum and duration depending on the circumstances. HELRAS sonar may affect the behavior of harbor porpoises (Phocoena phocoena) within a certain range. To investigate this, a harbor porpoise in a large pool was exposed to five HELRAS signals with different spectra, each of 1.25 s duration, at multiple sound pressure levels (SPLs), which had been determined in a pretest. During each transmission, the presence or absence of a brief behavioral response (defined as a sudden change in swimming speed or swimming direction during sound emission) by the harbor porpoise was recorded. A 50% response rate was observed at mean received SPLs (averaged over all measurement points in the pool) of between 124 and 140 dB re 1 μPa, depending on the signal. A single emission caused no response at received SPLs below around 102 dB re 1 μPa (sound exposure level: 103 dB re 1 μPa2s). The highest level needed to induce a brief response in the harbor porpoise occurred when it was exposed to a down-sweep without harmonics. Therefore, of the five tested signals, this signal will presumably have the least effect on harbor porpoises, and its use could help to minimize the potential effects of HELRAS sounds during peace time exercises. [ABSTRACT FROM AUTHOR]

One method of isolating the useful signal from the noise is by using a soft- processor Kalman filter. There are many applications of how one could use a Kalman filter: using navigation systems, such as satellite or radio, and sonar tracking systems. The guidance systems in military applications is another use of Kalman filter. In the recent years, they form the basis of GPS, which is used for car guidance. [ABSTRACT FROM AUTHOR]

Potential physical effects of sonar transmissions on marine mammals were investigated by measuring pressure fields induced in a 119-kg, 211-cm-long, young adult male common dolphin (Delphinus delphis) cadaver. The specimen was instrumented with tourmaline acoustic pressure gauges used as receiving sensors. Gauge implantation near critical tissues was guided by intraoperative, high-resolution, computerized tomography (CT) scanning. Instrumented structures included the melon, nares, ear, thoracic wall, lungs, epaxial muscle, and lower abdomen. The specimen was suspended from a frame equipped with a standard 50.8-mm-diameter spherical transducer used as the acoustic source and additional receiving sensors to monitor the transmitted and external, scattered field. Following immersion, the transducer transmitted pulsed sinusoidal signals at 5, 7, and 10 kHz. Quantitative internal pressure fields are reported for all cases except those in which the gauge failed or no received signal was detected. A full necropsy was performed immediately after the experiment to examine instrumented areas and all major organs. No lesions attributable to acoustic transmissions were found, consistent with the low source level and source-receiver distances. [ABSTRACT FROM AUTHOR]

Harbor surveillance above and below the sea surface depends on sensors such as surveillance radar and multibeam sonar. These sensors attempt to detect and track moderately observable targets such as small boats or human divers in environments which often are characterized by heavy-tailed backgrounds. Target tracking in heavy-tailed environments is challenging even for moderate signal-to-noise ratios (SNRs) due to the increased frequency of target-like outliers. A tracking method operating in such an environment should exploit as much of the data as possible to ensure robustness. Still, conventional tracking methods rely on kinematic measurements such as range, bearing, and Doppler only. The performance of the tracking method can be improved by using the backscattered signal strengths together with the kinematic measurements. This is done in the probabilistic data association filter with amplitude information (PDAFAI). We propose new conservative amplitude likelihoods for the PDAFAI with improved robustness compared to existing methods. The first likelihood works by incorporating the uncertainty of the background estimate. The second likelihood explicitly treats the background as heavy tailed using the K-distribution. Extensive and realistic Monte Carlo simulations show that both our conservative likelihoods give significant reductions in track loss. Furthermore, we provide a quantitative evaluation of the difficulties encountered by tracking methods in heavy-tailed clutter. To the best of our knowledge, such an analysis does not yet exist in the open literature. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a sensor fusion scheme along with the geometrical modeling of mobile robot navigation path in an unknown environment. In this scheme, the physical placement of sonars, their ranging limits and beam opening angles are considered. A simple 2-D axis transformation is proposed to relate local robot frame with the actual navigation environment. forward safe path (FSP) and target switching approach (TSA) are proposed for efficient obstacle avoidance and target tracking of mobile robot. FSP greatly simplifies the environment conditions as sensed by the robot and also provides minimum turning path during avoidance of obstacles. This method also removes the ldquooscillationrdquo in the mobile robot navigation path. TSA technique gives highest priority on the target tracking during the obstacle avoidance and seeks minimum distance path towards the target. These methods remove unnecessary turning of mobile robot during navigation. A scheme for target directional motion is also proposed. So, mobile robot takes the minimum turning path required towards the target. These methods also ensure the avoidance of ldquodead cycle problemrdquo. These schemes are successfully implemented on a model of PatrolBot mobile robot from ActivMedia Robotics. The overview of current research work on multi-domain robotic system namely system-of-systems is also presented. This paper also describes the Global Positioning System-based navigation of rovers. Results of real-time experiments with Pioneer II P2AT-8 from ActivMedia are included in this paper to show the future aspect of this research work. [ABSTRACT FROM PUBLISHER]

We present two procedures for validating track estimates obtained using the maximum-likelihood probabilistic data association (ML-PDA) algorithm. The ML-PDA, developed for very low observable (VLO) target tracking, always provides a track estimate that must then be tested for target existence by comparing the value of the log likelihood ratio (LLR) at the track estimate to a threshold. Using extreme value theory, we show that in the absence of a target the LLR at the track estimate obeys approximately a Gumbel distribution rather than the Gaussian distribution previously ascribed to it in the literature. The offline track validation procedure relies on extensive offline simulations to obtain a set of track validation thresholds that are then used by the tracking system. The real-time procedure uses the data set that produced the track estimate to also determine the track validation threshold. The performance of these two procedures is investigated through simulation of two active sonar tracking scenarios by comparing the false and true track acceptance probabilities. These techniques have potential for use in a broader class of maximum likelihood estimation problems with similar structure [ABSTRACT FROM PUBLISHER]

The fine-scale swimming behavior of fish can now be studied because of the development of sophisticated measurement devices such as multibeam sonar and stereo video systems. However, even with these sensors, improved methods are still required to generate quality estimates of swimming speeds and turn rates. Biologists have commonly relied on pointwise differentiation of noisy position measurements while engineers have focused on Bayesian algorithms to track underwater vehicles. A comparative evaluation of the performance of these tracking algorithms for the analysis of fine-scale behavior of fish was performed using a data set of 100 fish tracks recorded simultaneously with a multibeam sonar and a stereo video camera system. The segmenting track identifier, a non-Bayesian curve fitting and segmenting tracker, is shown to be most effective for tracking the unpredictable and complex horizontal motion of fish while a Kalman smoother using a constant-velocity model is shown to be most effective for tracking the more predictable and piecewise linear vertical motion of fish. Both are shown to be more effective than pointwise differentiation. Criteria for selecting an appropriate algorithm for a given motion study are provided. [ABSTRACT FROM AUTHOR]

We present an overview of the probabilistic data association (PDA) technique and its application for different target tracking scenarios, in particular for low observable (LO) (low SNR) targets. A summary of the PDA technique is presented. The use of the PDA technique for tracking low observable targets with passive sonar measurements is presented. This "target motion analysis" is an application of the PDA technique, in conjunction with the maximum likelihood (ML) approach, for target motion parameter estimation via a batch procedure. The use of the PDA technique for tracking highly maneuvering targets combined radar resource management is described. This illustrates the application of the PDA technique for recursive state estimation using the interacting multiple model (IMM) estimator with probabilistic data association filter (PDAF) (IMMPDAF). Then we present a flexible (expanding and contracting) sliding-window parameter estimator using the PDA approach for tracking the state of a maneuvering target using measurements from an electro-optical (EO) sensor. This, while still a batch procedure, has the flexibility of varying the batches depending on the estimation results in order to make the estimation robust to target maneuvers as well as target appearance or disappearance. [ABSTRACT FROM PUBLISHER]

Rate-distortion analysis is applied to the problem of joint compression and classification. A Lagrangian distortion measure is used to consider both the Euclidean error in reconstructing the original data as well as the classification performance. The bound is calculated based on an alternating-minimization procedure, representing an extension of the Blahut-Arimoto algorithm. This rate-distortion framework is then applied to a joint compression and target-orientation estimation problem, based on a sequence of scattered waveforms measured at multiple target-sensor orientations. A hidden Markov model-Markov model (HMM-MM) is used as the statistical description for the source, here representative of multiaspect scattering data. Target-orientation estimation reduces to assessing the underlying HMM states from a sequence of observations. After deriving the rate-distortion function, we demonstrate that discrete HMM performance based on Lloyd encoding is far from this bound. Performance is improved via block coding, based on Bayes vector quantization. Results are presented for multiaspect acoustic scattering from an underwater elastic target, using measured and synthesized data. [ABSTRACT FROM PUBLISHER]

The most important sensors for gathering target information onboard a submarine are passive sonars. Problems concerning fusion of these passive sonars are discussed. Three typical passive sonars-passive noise sonar, passive ranging sonar and acoustic pulse surveillance sonar-constitute a passive sonar system for data fusion. This paper is concerned mainly with problems of significance in system development, such as tactical application background, special fusion techniques and own-ship maneuver considerations [ABSTRACT FROM PUBLISHER]

This paper focuses on underwater target tracking based on a multi-static sonar network composed of passive sonobuoys and an active ping. In the multi-static sonar network, the location of the target can be estimated using TDOA (Time Difference of Arrival) measurements. However, since the sensor network may obtain insufficient and inaccurate TDOA measurements due to ambient noise and other harsh underwater conditions, target tracking performance can be significantly degraded. We propose a robust target tracking algorithm designed to operate in such a scenario. First, track management with track splitting is applied to reduce performance degradation caused by insufficient measurements. Second, a target location is estimated by a fusion of multiple TDOA measurements using a Gaussian Mixture Model (GMM). In addition, the target trajectory is refined by conducting a stack-based data association method based on multiple-frames measurements in order to more accurately estimate target trajectory. The effectiveness of the proposed method is verified through simulations. [ABSTRACT FROM AUTHOR]

This article proposes a robust localization algorithm by improving the accuracy of the source position, receiver position, and sound speed using the known position of an object in the sea. The accuracy of those three types of information greatly influences the performance of the target localization in bistatic sonar. Although the locations of the source and receiver can be obtained using the global positioning system, there are still some position errors due to the limitations of global positioning system–based positioning systems and tidal current. In addition, the speed of the sound in the sea changes with temperature. The target estimation error was analyzed mathematically in terms of the mentioned errors. An improved localization is proposed using the accurate positions of objects in the sea, which can compensate for the errors in the source position, receiver position, and sound speed. The proposed algorithm is compared with the conventional algorithm through computer simulations. [ABSTRACT FROM AUTHOR]