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Underwater Target Localization and Depth Estimation Using the Nonlinear Acoustic Signals

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 13866

Special Issue Editors

Dr. Sufyan Ali Memon

E-Mail Website
Guest Editor
Department of Defense Systems Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: artificial intelligence; autonomous vehicles; wireless networks
Special Issues, Collections and Topics in MDPI journals
Dr. Wan-Gu Kim

E-Mail Website
Guest Editor
Department of Defense System Engineering, Sejong University, Seoul 05006, Korea
Interests: Signal processing and transmission line modeling for target detection
Dr. Muhammad Attique

E-Mail Website
Guest Editor
Department of Software, Sejong University, Seoul 05006, Korea
Interests: Big data analysis; spatial data management; information retrieval; social network analysis; internet of things
Dr. Ihsan Ullah

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan
Interests: Tracking; estimation; guidance; navigation and control

Special Issue Information

Dear Colleagues,

Active sonar systems are widely used for detecting and estimating a submarine's location through high-frequency sound signal processing. The use of high-frequency sound signals enables the miniaturization of operating equipment and high-resolution position estimation for short-range targets. It has the disadvantage of being easily distorted by environmental mismatch factors, and the detection distance is short.

This study aims to develop a nonlinear acoustic active detection method using the high-frequency band-based active sonar system. The nonlinear acoustic active detection technique is a technique that enables signal processing in a low frequency band that is strong against environmental mismatch factors and distance compared with a high frequency signal. When the difference in frequency bands of the acoustic signals received in the high frequency band is induced in a nonlinear acoustic manner (low-frequency signal processing) corresponding to the difference frequency, the differential frequency-matching field model would be applicable. This makes it possible to develop an active detection technique that can minimize the impact of environmental mismatch.

This Special Issue deals with developing novel architectures, strategies, control, optimization and models that are robust to ocean fluctuations, and the novel active detection technique that can accurately identify underwater targets and estimate depth based on the developed model.

Dr. Sufyan Ali Memon
Dr. Wan-Gu Kim
Dr. Muhammad Attique
Dr. Ihsan Ullah
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • active sonar
  • bistatic
  • ocean parameters
  • environmental mismatch
  • submarine
  • target detection
  • target depth estimation

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Published Papers (4 papers)

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Research

18 pages, 1299 KiB  
Article
Improvement in the Tracking Performance of a Maneuvering Target in the Presence of Clutter
by Ghawas Ali Shah, Sumair Khan, Sufyan Ali Memon, Mohsin Shahzad, Zahid Mahmood and Uzair Khan
Sensors 2022, 22(20), 7848; https://doi.org/10.3390/s22207848 - 16 Oct 2022
Cited by 7 | Viewed by 1967
Abstract
The proposed work uses fixed lag smoothing on the interactive multiple model-integrated probabilistic data association algorithm (IMM-IPDA) to enhance its performance. This approach makes use of the advantages of the fixed lag smoothing algorithm to track the motion of a maneuvering target while [...] Read more.
The proposed work uses fixed lag smoothing on the interactive multiple model-integrated probabilistic data association algorithm (IMM-IPDA) to enhance its performance. This approach makes use of the advantages of the fixed lag smoothing algorithm to track the motion of a maneuvering target while it is surrounded by clutter. The suggested method provides a new mathematical foundation in terms of smoothing for mode probabilities in addition to the target trajectory state and target existence state by including the smoothing advantages. The suggested fixed lag smoothing IMM-IPDA (FLs IMM-IPDA) method’s root mean square error (RMSE), true track rate (TTR), and mode probabilities are compared to those of other recent algorithms in the literature in this study. The results clearly show that the proposed algorithm outperformed the already-known methods in the literature in terms of these above parameters of interest. Full article
(This article belongs to the Special Issue Underwater Target Localization and Depth Estimation Using the Nonlinear Acoustic Signals)
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16 pages, 2810 KiB  
Article
Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter
by Sufyan Ali Memon, Min-Seuk Park, Imran Memon, Wan-Gu Kim, Sajid Khan and Yifang Shi
Sensors 2022, 22(13), 4759; https://doi.org/10.3390/s22134759 - 23 Jun 2022
Cited by 4 | Viewed by 1945
Abstract
This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to [...] Read more.
This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms. Full article
(This article belongs to the Special Issue Underwater Target Localization and Depth Estimation Using the Nonlinear Acoustic Signals)
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21 pages, 3620 KiB  
Article
Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment
by Roujie Chen, Tingting Li, Imran Memon, Yifang Shi, Ihsan Ullah and Sufyan Ali Memon
Sensors 2022, 22(9), 3335; https://doi.org/10.3390/s22093335 - 27 Apr 2022
Cited by 4 | Viewed by 2804
Abstract
The multi-sonar distributed fusion system has been pervasively deployed to jointly detect and track marine targets. In the realistic scenario, the origin of locally transmitted tracks is uncertain due to clutter disturbance and the presence of multi-target. Moreover, attributed to the different sonar [...] Read more.
The multi-sonar distributed fusion system has been pervasively deployed to jointly detect and track marine targets. In the realistic scenario, the origin of locally transmitted tracks is uncertain due to clutter disturbance and the presence of multi-target. Moreover, attributed to the different sonar internal processing times and diverse communication delays between sonar and the fusion center, tracks unavoidably arrive in the fusion center with temporal out-of-sequence (OOS), both problems pose significant challenges to the fusion system. Under the distributed fusion framework with memory, this paper proposes a novel multiple forward prediction-integrated equivalent measurement fusion (MFP-IEMF) method, it fuses the multi-lag OOST with track origin uncertainty in an optimal manner and is capable to be implemented in both the synchronous and asynchronous multi-sonar tracks fusion system. Furthermore, a random central track initialization technique is also proposed to detect the randomly born marine target in time via quickly initiating and confirming true tracks. The numerical results show that the proposed algorithm achieves the same optimality as the existing OOS reprocessing method, and delivers substantially improved detection and tracking performance in terms of both ANCTT and estimation accuracy compared to the existing OOST discarding fusion method and the ANF-IFPFD method. Full article
(This article belongs to the Special Issue Underwater Target Localization and Depth Estimation Using the Nonlinear Acoustic Signals)
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19 pages, 2888 KiB  
Article
Towards Automatic License Plate Detection
by Zahid Mahmood, Khurram Khan, Uzair Khan, Syed Hasan Adil, Syed Saad Azhar Ali and Mohsin Shahzad
Sensors 2022, 22(3), 1245; https://doi.org/10.3390/s22031245 - 7 Feb 2022
Cited by 33 | Viewed by 5730
Abstract
Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles’ license plates in images is a critical step that has a substantial impact on any ALPD system’s recognition rate. [...] Read more.
Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles’ license plates in images is a critical step that has a substantial impact on any ALPD system’s recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques. The proposed algorithm initially detects vehicle(s) in the input image through Faster R-CNN. Later, the located vehicle is analyzed by a robust License Plate Localization Module (LPLM). The LPLM module primarily uses color segmentation and processes the HSV image to detect the license plate in the input image. Moreover, the LPLM module employs morphological filtering and dimension analysis to find the license plate. Detailed trials on challenging PKU datasets demonstrate that the proposed method outperforms few recently developed methods by producing high license plates detection accuracy in much less execution time. The proposed work demonstrates a great feasibility for security and target detection applications. Full article
(This article belongs to the Special Issue Underwater Target Localization and Depth Estimation Using the Nonlinear Acoustic Signals)
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