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Search Results (5)

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Keywords = port-starboard ambiguity

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23 pages, 6193 KiB  
Article
Multi-Target Tracking in Multi-Static Networks with Autonomous Underwater Vehicles Using a Robust Multi-Sensor Labeled Multi-Bernoulli Filter
by Yuexing Zhang, Yiping Li, Shuo Li, Junbao Zeng, Yiqun Wang and Shuxue Yan
J. Mar. Sci. Eng. 2023, 11(4), 875; https://doi.org/10.3390/jmse11040875 - 20 Apr 2023
Cited by 6 | Viewed by 2514
Abstract
This paper proposes a centralized MTT method based on a state-of-the-art multi-sensor labeled multi-Bernoulli (LMB) filter in underwater multi-static networks with autonomous underwater vehicles (AUVs). The LMB filter can accurately extract the number of targets and trajectories from measurements affected by noise, missed [...] Read more.
This paper proposes a centralized MTT method based on a state-of-the-art multi-sensor labeled multi-Bernoulli (LMB) filter in underwater multi-static networks with autonomous underwater vehicles (AUVs). The LMB filter can accurately extract the number of targets and trajectories from measurements affected by noise, missed detections, false alarms and port–starboard ambiguity. However, its complexity increases as the number of sensors increases. In addition, due to the time-varying underwater environment, AUV detection probabilities are time-varying, and their mismatches often lead to poor MTT performance. Consequently, we detail a robust multi-sensor LMB filter that estimates detection probabilities and multi-target states simultaneously in real time. Moreover, we derive an effective approximate form of the multi-sensor LMB filter using Kullback–Leibler divergence and develop an efficient belief propagation (BP) implementation of the multi-sensor LMB filter. Our method scales linearly with the number of AUVs, providing good scalability and low computational complexity. The proposed method demonstrates superior performance in underwater multi-AUV network MTT simulations. Full article
(This article belongs to the Section Ocean Engineering)
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13 pages, 5249 KiB  
Article
Dimension Reduction Localization Algorithm of Mixed Sources Based on MEMS Vector Hydrophone Array
by Zhenzhen Shang, Libo Yang, Wendong Zhang, Guojun Zhang, Xiaoyong Zhang and Hairong Kou
Micromachines 2022, 13(4), 626; https://doi.org/10.3390/mi13040626 - 15 Apr 2022
Cited by 2 | Viewed by 2336
Abstract
In this paper, a mixed sources dimension reduction Multiple Signal Classification (MUSIC) localization algorithm suitable for Micro-Electro-Mechanical System (MEMS) vector hydrophone linear arrays is proposed, which reduces the two-dimensional search to one-dimensional local search. Firstly, the Lagrangian function is constructed by quadratic optimization [...] Read more.
In this paper, a mixed sources dimension reduction Multiple Signal Classification (MUSIC) localization algorithm suitable for Micro-Electro-Mechanical System (MEMS) vector hydrophone linear arrays is proposed, which reduces the two-dimensional search to one-dimensional local search. Firstly, the Lagrangian function is constructed by quadratic optimization idea to obtain the estimates of azimuth angles. Secondly, the least square method is utilized for optimal match to obtain the direction-of-arrivals (DOAs) and ranges, and the range parameters are judged in Fresnel zone to obtain the azimuth information of all near-field sources. Finally, find the common DOAs and achieve high-resolution separation of far-field and near-field sources. Simulation and field experiments prove that the proposed algorithm only needs a small number of elements can solve the problem of port and starboard ambiguity, does not need to construct high-order cumulants or multi-dimensional search while the parameters are automatically matched with low computational complexity. This study provides an idea of the engineering application of vector hydrophone. Full article
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16 pages, 8861 KiB  
Article
Research on Direction of Arrival Estimation Based on Self-Contained MEMS Vector Hydrophone
by Shan Zhu, Guojun Zhang, Daiyue Wu, Xiaoqi Liang, Yifan Zhang, Ting Lv, Yan Liu, Peng Chen and Wendong Zhang
Micromachines 2022, 13(2), 236; https://doi.org/10.3390/mi13020236 - 30 Jan 2022
Cited by 18 | Viewed by 3345
Abstract
A self-contained MEMS vector hydrophone with a scalar–vector integrated design is proposed in this paper. Compared with traditional MEMS vector hydrophones, this design solves the problem of ambiguity in the port and starboard during orientation, and also realizes the self-contained storage of acoustic [...] Read more.
A self-contained MEMS vector hydrophone with a scalar–vector integrated design is proposed in this paper. Compared with traditional MEMS vector hydrophones, this design solves the problem of ambiguity in the port and starboard during orientation, and also realizes the self-contained storage of acoustic signals. First, the sensor principle and structural design of the self-contained MEMS hydrophone are introduced, and then the principle of the combined beamforming algorithm is given. In addition to this, the amplitude and phase calibration method based on the self-contained MEMS vector hydrophone is proposed. Then, the sensitivity and phase calibrations of the sensor are carried out in the standing wave tube. The sensitivity of the vector channel is −182.7 dB (0 dB@1 V/μPa) and the sensitivity of the scalar channel is −181.8 dB (0 dB@1 V/μPa). Finally, an outdoor water experiment was carried out. The experimental results show that the self-contained MEMS vector hydrophone can accurately pick up and record underwater acoustics information. It realizes the precise orientation of the target by combining beamforming algorithms. The direction of arrival (DOA) error is within 5° under the outdoor experimental conditions with an SNR of 13.67 dB. Full article
(This article belongs to the Special Issue Advances in Piezoelectric Sensors, Transducers and Harvesters)
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19 pages, 3392 KiB  
Article
Passive Underwater Target Tracking: Conditionally Minimax Nonlinear Filtering with Bearing-Doppler Observations
by Andrey Borisov, Alexey Bosov, Boris Miller and Gregory Miller
Sensors 2020, 20(8), 2257; https://doi.org/10.3390/s20082257 - 16 Apr 2020
Cited by 17 | Viewed by 3843
Abstract
The paper presents an application of the Conditionally-Minimax Nonlinear Filtering (CMNF) algorithm to the online estimation of underwater vehicle movement given a combination of sonar and Doppler discrete-time noisy sensor observations. The proposed filter postulates recurrent “prediction–correction” form with some predefined basic prediction [...] Read more.
The paper presents an application of the Conditionally-Minimax Nonlinear Filtering (CMNF) algorithm to the online estimation of underwater vehicle movement given a combination of sonar and Doppler discrete-time noisy sensor observations. The proposed filter postulates recurrent “prediction–correction” form with some predefined basic prediction and correction terms, and then they are optimally fused. The CMNF estimates have the following advantageous features. First, the obtained estimates are unbiased. Second, the theoretical covariance matrix of CMNF errors meets the real values. Third, the CMNF algorithm gives a possibility to choose the preliminary observation transform, basic prediction, and correction functions in any specific case of the observation system to improve the estimate accuracy significantly. All the features of conditionally-minimax estimates are demonstrated by the regression example of random position estimate given the noisy bearing observations. The contribution of the paper is the numerical study of the CMNF algorithm applied to the underwater target tracking given bearing-only and bearing-Doppler observations. Full article
(This article belongs to the Special Issue Autonomous Underwater Vehicle Navigation)
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14 pages, 7012 KiB  
Article
The Use of Underwater Gliders as Acoustic Sensing Platforms
by Cheng Jiang, JianLong Li and Wen Xu
Appl. Sci. 2019, 9(22), 4839; https://doi.org/10.3390/app9224839 - 12 Nov 2019
Cited by 24 | Viewed by 5927
Abstract
Underwater gliders travel through the ocean by buoyancy control, which makes their motion silent and involves low energy consumption. Due to those advantages, numerous studies on underwater acoustics have been carried out using gliders and different acoustic payloads have been developed. This paper [...] Read more.
Underwater gliders travel through the ocean by buoyancy control, which makes their motion silent and involves low energy consumption. Due to those advantages, numerous studies on underwater acoustics have been carried out using gliders and different acoustic payloads have been developed. This paper aims to illustrate the use of gliders in underwater acoustic observation and target detection through experimental data from two sea trials. Firstly, the self-noise of the glider is analyzed to illustrate its feasibility as an underwater acoustic sensing platform. Then, the ambient noises collected by the glider from different depths are presented. By estimating the transmission loss, the signal receiving ability of the glider is assessed, and a simulation of target detection probability is performed to show the advantages of the glider over other underwater vehicles. Moreover, an adaptive line enhancement is presented to further reduce the influence of self-noise. Meanwhile, two hydrophones are mounted at both ends of the glider to form a simple array with a large aperture and low energy consumption. Thus, the target azimuth estimation is verified using broadband signals, and a simple scheme to distinguish the true angle from the port‒starboard ambiguity is presented. The results indicate that the glider does have advantages in long-term and large-scale underwater passive sensing. Full article
(This article belongs to the Special Issue Underwater Robots in Ocean and Coastal Applications)
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