High Resolution Wideband Acoustic Beamforming and Underwater Target Localization using 64-Element Linear Hydrophone Array

2018 ◽  
Author(s):  
Rashida K. ◽  
P. G. Thulasi Devi ◽  
Arun A. Balakrishnan ◽  
M. H. Supriya
Keyword(s):  
High Resolution ◽  
Target Localization ◽  
Underwater Target ◽  
Hydrophone Array

High-Resolution Imaging Sonar and Video Technologies for Detection and Classification of Underwater Munitions

2011 ◽  
Vol 45 (6) ◽  
pp. 62-74 ◽  
Author(s):  
Pierre-Philippe J. Beaujean ◽  
Lisa N. Brisson ◽  
Shahriar Negahdaripour
Keyword(s):  
High Resolution ◽  
Template Matching ◽  
Metal Alloy ◽  
Acoustic Energy ◽  
Target Localization ◽  
Imaging Systems ◽  
Optical Systems ◽  
Test Tank ◽  
Range Limitation

AbstractThe detection of and response to underwater munitions will undoubtedly require the appropriate combinations of fully integrated sensors and imaging systems and platforms, as well as navigation and positioning technologies, to handle the variability in bottom conditions, water clarity and depth, size and type of munitions of interest, whether they are buried or proud. Where visibility allows, practically no sensing modality matches the details and information content from optical imaging systems for target localization, discrimination and identification. The significant disadvantage of optical systems for underwater applications is the range limitation. Sonar imaging systems are of limited resolution but do not have such a severe range limitation, as acoustic energy propagates well through turbid waters.In this study, we have explored two aspects of the munitions detection and classification process: (1) high-resolution mapping of an environment using a high-frequency sonar system to determine footprints of areas with munitions present and target localization in a wide-area survey and to perform detailed surveys for individual detected items during a re-acquisition process and (2) Multiple-Aspect Fixed-Range Template Matching (MAFR-TM) for detection and classification of the potential target.The MAFR-TM approach was tested using (1) a singular target scene collected in a test tank, (2) a cluttered scene acquired in the same test tank, and (3) a cluttered scene obtained in a realistic field environment (a marina). The munitions-like targets were cylinders made of steel or aluminum. The clutter was a collection of PVC tubes. Biological growth surrounded the target and artificial clutter in the marina. The experimental results indicate that the detection algorithm performs fairly well with the tank data (100% of the targets are detected) and cluttered tank data (94.44%). The classification between metals and plastics, proper orientation and target localization is also of good quality: 94.4% of the detected targets are properly classified as metal alloy if no clutter is present versus 82.35% in the presence of clutter. The algorithm performance in the marina is reasonably good, even though the overall performance drops: 61.11% of the targets are detected, and 68.18% of the detected targets are properly classified as metal alloy.

scholarly journals A Fast and High-Resolution Multi-Target Localization Approach in MIMO Radar

10.5772/56651 ◽  
2013 ◽  
Vol 10 (9) ◽  
pp. 322
Author(s):  
Yu Zhang ◽  
Hong Jiang ◽  
Ying-Chun Wei ◽  
Hai-Jing Cui
Keyword(s):  
High Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Localization Approach

scholarly journals Range-only underwater target localization: Path characterization

2016 ◽  
Author(s):  
I. Masmitja ◽  
S. Gomariz ◽  
J. Del Rio ◽  
B. Kieft ◽  
T. O'Reilly
Keyword(s):  
Target Localization ◽  
Underwater Target

scholarly journals Underwater Target Localization Using Opportunistic Ship Noise Recorded on a Compact Hydrophone Array

Acoustics ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 611-629
Author(s):  
Mojgan Mirzaei Hotkani ◽  
Jean-Francois Bousquet ◽  
Seyed Alireza Seyedin ◽  
Bruce Martin ◽  
Ehsan Malekshahi
Keyword(s):  
Normal Modes ◽  
Broadband Noise ◽  
Localization Algorithm ◽  
Matched Field Processing ◽  
Time Frequency ◽  
Noise Data ◽  
Time Frequency Localization ◽  
Underwater Targets ◽  
Underwater Target ◽  
Hydrophone Array

In this research, a new application using broadband ship noise as a source-of-opportunity to estimate the scattering field from the underwater targets is reported. For this purpose, a field trial was conducted in collaboration with JASCO Applied Sciences at Duncan’s Cove, Canada in September 2020. A hydrophone array was deployed in the outbound shipping lane at a depth of approximately 71 m to collect broadband noise data from different ship types and effectively localize the underwater targets. In this experiment, a target was installed at a distance (93 m) from the hydrophone array at a depth of 25 m. In this study, a matched field processing (MFP) algorithm is utilized for localization. Different propagation models are presented using Green’s function to generate the replica signal; this includes normal modes in a shallow water waveguide, the Lloyd-mirror pattern for deep water, as well as the image model. We use the MFP algorithm with different types of underwater environment models and a proposed estimator to find the best match between the received signal and the replica signal. Finally, by applying the scatter function on the proposed multi-channel cross correlation coefficient time-frequency localization algorithm, the location of target is detected.

Underwater Target Localization

2010 ◽  
Vol 17 (1) ◽  
pp. 64-70 ◽  
Author(s):  
Giacomo Marani ◽  
Song Choi
Keyword(s):  
Target Localization ◽  
Underwater Target

scholarly journals Multiple underwater target positioning with optimally placed acoustic surface sensor networks

2018 ◽  
Vol 14 (5) ◽  
pp. 155014771877323 ◽  
Author(s):  
David Moreno-Salinas ◽  
Antonio Manuel Pascoal ◽  
Joaquín Aranda
Keyword(s):  
Autonomous Vehicles ◽  
Information Matrix ◽  
Sensor Placement ◽  
Target Localization ◽  
Sensor Nodes ◽  
Multiple Objective ◽  
Positioning Accuracy ◽  
Trade Offs ◽  
Sensor Configuration ◽  
Underwater Target

In an increasing number of scientific and commercial mission scenarios at sea, it is required to simultaneously localize a group of underwater targets. The latter may include moored systems, autonomous vehicles, and even human divers. For reasons that have to do with the unavailability of Global Positioning System underwater, cost reduction, and simplicity of operation, there is currently a surge of interest in the development of range-based multiple target localization systems that rely on the computation of the distances between the targets and a number of sensor nodes deployed at the ocean surface, equipped with acoustic range measuring devices. In the case of a single target, there is a wealth of literature on the problem of optimal acoustic sensor placement to maximize the information available for target localization using trilateration methods. In the case of multiple targets, however, the literature is scarce. Motivated by these considerations, we address the problem of optimal sensor placement for multiple underwater target positioning. In this setup, we are naturally led to a multiple objective optimization problem, the solution of which allows for the analysis of the trade-offs involved in the localization of the targets simultaneously. To this end, we resort to tools from estimation theory and multi-objective optimization. For each target, the function to be minimized (by proper choice of the sensor configuration) is related to the determinant of the corresponding Fisher information matrix, which yields information on the minimum possible covariance of the error with which the position of the target can be estimated using any non-biased estimator. To deal with the fact that more than one target is involved, we exploit the concept of multiple objective Pareto-optimal solutions to characterize the best possible accuracy with which each of the targets can be positioned, given constraints on the desired positioning accuracy of the other targets. Simulation examples illustrate how, for a three-sensor network and two targets, it is possible to define an optimal sensor configuration that yields large positioning accuracy for both targets simultaneously, using convex optimization tools. When more than two targets are involved, however, more than three sensors are required to exploit an adequate trade-off of the accuracy with which each target can be positioned by resorting to non-convex and Pareto-optimization tools. We show how in this case the optimal sensor configurations depend on the Pareto weights assigned to each of the targets, as well as on the number of sensors, the number of targets, and the uncertainty with which the positions of the targets are known a priori.

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