Vessel Velocity Estimation and Tracking From Doppler Echoes of T/R-R Composite Compact HFSWR

Sun, Weifeng; Pang, Zhenzhen; Huang, Weimin; Ji, Yonggang; Dai, Yongshou

doi:10.1109/jstars.2021.3071625

Cited by 12 publications

(4 citation statements)

References 44 publications

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“…e basic ideas of these methods are similar. e human body is divided into multiple parts with depth data and similar degree label [21,22]. e segmentation process of human body parts uses the contribution value of each pixel in the space model to calculate the position information of the three-dimensional connection point, which can be regarded as the classification of each pixel in the depth data.…”

Section: Human Motion Recognition Methods Based On Skeletonmentioning

confidence: 99%

Human Motion Tracking Algorithm Based on Image Segmentation Algorithm and Kinect Depth Information

Zuo¹

2021

Mathematical Problems in Engineering

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Human motion recognition has an important application value in scenarios such as intelligent monitoring and advanced human-computer interaction, and it is an important research direction in the field of computer vision. Traditional human motion recognition algorithms based on two-dimensional cameras are susceptible to changes in light intensity and texture. The advent of depth sensors, especially the Kinect series with good performance and low price released by Microsoft, enables extensive research based on depth information. However, to a large extent, the depth information has not overcome these problems based on two-dimensional images. This article introduces the research background and significance of human motion recognition technology based on depth information, introduces in detail the research methods of human motion recognition algorithms based on depth information at home and abroad, and analyzes their advantages and disadvantages. The public dataset is introduced. Then, based on the depth information, a method of human motion recognition is proposed and optimized. A moving human body image segmentation method based on an improved two-dimensional Otsu method is proposed to solve the problem of inaccurate and slow segmentation of moving human body images using the two-dimensional Otsu method. In the process of constructing the threshold recognition function, this algorithm not only uses the cohesion of the pixels within the class but also considers the maximum variance between the target class and the background class. Then, the quantum particle swarm algorithm is used to find the optimal threshold solution of the threshold recognition function. Finally, the optimal solution is used to achieve accurate and fast image segmentation, which increases the accuracy of human body motion tracking by more than 30%.

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Section: Human Motion Recognition Methods Based On Skeletonmentioning

confidence: 99%

Human Motion Tracking Algorithm Based on Image Segmentation Algorithm and Kinect Depth Information

Zuo¹

2021

Mathematical Problems in Engineering

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“…Te AIS receiver collects the signal and the data, which can be used to assess the tracking results of HF radars. However, the linear tracks dominate the data for any assessment of the tracking algorithm [6,7]. Te AIS data are sometimes sufered from track fragmentation which can reduce the availability of ground truth data for this study [8].…”

Section: Introductionmentioning

confidence: 99%

An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model

Iswandi

Hidayat

Wibowo

2023

Journal of Engineering

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Thelong-distance coverage of high-frequency surface wave radar (HFSWR) has promoted it as an enormous means for ship monitoring on the country’s maritime territory. Since it is a primaryradar, noncooperative targets can also be detected. However, this radar also has a shortcoming of low spatial and temporal resolutions due to the narrow available bandwidth in the HF band. This limitation can reduce the performance of ship detection and tracking, especially for highly maneuvering ships. This paper proposes a new method to assess the tracking algorithm for a high-maneuvering ship. The absence of a high-maneuvering plot in the AIS data and existing analytical models are replaced by the MMG model run on MANSIM software. The linear, turning, and zigzag motions are generated and used to evaluate the tracking algorithms. The Monte Carlo simulation was conducted regarding the degradation of spatial resolution in the higher radial range. The tracking performance was analyzed by calculating the RMSE of four parameters, i.e., absolute position, radial range, bearing angle, and speed. For a trial case, four tracking algorithms were evaluated, i.e., Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). The evaluation results showed that the EKF tracker had a minor error for the linear track with RMSE of absolute position, radial range, bearing angle, and speed being 1.368 km, 0.526 km, 1.550°, and 0.005 m/s, respectively. Otherwise, the UKF performed slightly better than EKF for the high maneuver targets. The RMSE of absolute position, radial range, bearing angle, and speed were 1.649 km, 0.639 km, 1.919°, and 0.165 m/s, respectively. The results also ensure the applicability of the MMG model to evaluate the tracking algorithm’s performance in HFSWR.

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“…For bistatic T-R radar systems, the receiver is secure when the transmitter is attacked as the receiver is passive and in a state of concealment. Because the radar transmitter and receiver are positioned in separate locations, the receiving antenna or the transmitter can be placed on an island, an offshore platform or even a buoy or other mobile platforms, which can improve the detection range and flexibility of the radar system [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Ship Monitoring With Bistatic Compact HFSWR of Small Aperture

Wang

Sun

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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High-frequency surface wave radar (HFSWR) can detect and continuously track ship targets in real time and beyond the horizon. Compared with transmit/receive (T/R) monostatic HFSWR, the T-R bistatic HFSWR has the advantages of flexibility, receiver concealment and large coverage because of the separation between the radar transmitter and receiver locations. So far, there is little research on marine ship monitoring for bistatic HFSWR. This study examines ship monitoring performance with T-R bistatic compact HFSWR of small aperture based on measured experimental data. The first-order sea clutter characteristics and detection blind zone for T-R bistatic HFSWR were investigated theoretically, and the formulas for the moving ships were derived and its position accuracy were analyzed from simulation results. The experimental results of ship target detection of bistatic compact HFSWR carried out in 2015 were presented, and the spectrum characteristics of the bistatic HFSWR were analyzed with measured experimental data. A method integrating detection and tracking was applied to the target detection data collected by the bistatic compact radar, and two targets examples tracked were given. Finally, the validity of the method and the tracing results were verified by using synchronous automatic identification system (AIS) data, and the ship positioning accuracy of the bistatic compact HFSWR was statistically analyzed. The analysis shows that bistatic radars have larger errors than monostatic T/R radars, with a positioning error as large as 10 km.

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Vessel Velocity Estimation and Tracking From Doppler Echoes of T/R-R Composite Compact HFSWR

Cited by 12 publications

References 44 publications

Human Motion Tracking Algorithm Based on Image Segmentation Algorithm and Kinect Depth Information

Human Motion Tracking Algorithm Based on Image Segmentation Algorithm and Kinect Depth Information

An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model

Ship Monitoring With Bistatic Compact HFSWR of Small Aperture

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