A New Target Tracking Algorithm for Synchronous Radar Network under Blanket Jamming

In order to improve the tracking accuracy of the synchronous radar network under blanket jamming with less computation, a new target tracking algorithm based on the optimal linearization is proposed. Firstly, the optimal linearization algorithm for the measurement equation is analyzed. Then the optimal estimation of the position is derived in 2D space according to the bearing angle measurements, and then the estimation is expanded to 3D space in accordance with the pitch angle measurements. Finally, the tracking algorithm for the moving target is presented and simulation testing is conducted. The simulation results show the tracking algorithm without iteration proposed by this paper can make it possible for the radar network under blanket jamming to track the target precisely.

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Research on Target Tracking Algorithm Based on Siamese Neural Network

Mobile Information Systems ◽

10.1155/2021/6645629 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Haibo Pang ◽

Qi Xuan ◽

Meiqin Xie ◽

Chengming Liu ◽

Zhanbo Li

Keyword(s):

Neural Network ◽

Target Tracking ◽

Evaluation Criteria ◽

Attention Mechanism ◽

Tracking Algorithm ◽

Temporal Attention ◽

Tracking Accuracy ◽

Current Frame ◽

Tracking Process ◽

Recognition Ability

Target tracking is a significant topic in the field of computer vision. In this paper, the target tracking algorithm based on deep Siamese network is studied. Aiming at the situation that the tracking process is not robust, such as drift or miss the target, the tracking accuracy and robustness of the algorithm are improved by improving the feature extraction part and online update part. This paper adds SE-block and temporal attention mechanism (TAM) to the framework of Siamese neural network. SE-block can refine and extract features; different channels are given different weights according to their importance which can improve the discrimination of the network and the recognition ability of the tracker. Temporal attention mechanism can update the target state by adjusting the weights of samples at current frame and historical frame to solve the model drift caused by the existence of similar background. We use cross-entropy loss to distinguish the targets in different sequences so that their distance in the feature domains is longer and the features are easier to identify. We train and test the network on three benchmarks and compare with several state-of-the-art tracking methods. The experimental results demonstrate that the algorithm proposed is superior to other methods in tracking effect diagram and evaluation criteria. The proposed algorithm can solve the occlusion problem effectively while ensuring the real-time performance in the process of tracking.

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Tracking algorithm of snowboard target in intelligent system

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189350 ◽

2020 ◽

pp. 1-9

Author(s):

Zhipeng Li ◽

Xiaolan Li ◽

Ming Shi ◽

Wenli Song ◽

Guowei Zhao ◽

...

Keyword(s):

Target Tracking ◽

High Speed ◽

Intelligent System ◽

Mean Shift ◽

Target Position ◽

Tracking Algorithm ◽

Tracking Accuracy ◽

Mean Shift Algorithm ◽

Target Track ◽

The Mean

Snowboarding is a kind of sport that takes snowboarding as a tool, swivels and glides rapidly on the specified slope line, and completes all kinds of difficult actions in the air. Because the sport is in the state of high-speed movement, it is difficult to direct guidance during the sport, which is not conducive to athletes to find problems and correct them, so it is necessary to track the target track of snowboarding. The target tracking algorithm is the main solution to this task, but there are many problems in the existing target tracking algorithm that have not been solved, especially the target tracking accuracy in complex scenes is insufficient. Therefore, based on the advantages of the mean shift algorithm and Kalman algorithm, this paper proposes a better tracking algorithm for snowboard moving targets. In the method designed in this paper, in order to solve the problem, a multi-algorithm fusion target tracking algorithm is proposed. Firstly, the SIFT feature algorithm is used for rough matching to determine the fuzzy position of the target. Then, the good performance of the mean shift algorithm is used to further match the target position and determine the exact position of the target. Finally, the Kalman filtering algorithm is used to further improve the target tracking algorithm to solve the template trajectory prediction under occlusion and achieve the target trajectory tracking algorithm design of snowboarding.

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Research on the Particle Filter Single-Station Target Tracking Algorithm Based on Particle Number Optimization

Journal of Electrical and Computer Engineering ◽

10.1155/2021/2838971 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Lieping Zhang ◽

Jinghua Nie ◽

Shenglan Zhang ◽

Yanlin Yu ◽

Yong Liang ◽

...

Keyword(s):

Particle Filter ◽

Target Tracking ◽

Real Time ◽

Particle Number ◽

Tracking Algorithm ◽

Tracking Accuracy ◽

Time Performance ◽

Single Station ◽

Tracking Model ◽

Number Of Particles

Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering time as the evaluation indexes. On this basis, the optimal number of particles, which could meet the accuracy and real-time performance requirements, was determined and taken as the number of particles of the proposed algorithm. The MATLAB simulation results revealed that compared with the unscented Kalman filter (UKF), the single-station PF target tracking algorithm based on particle number optimization not only was of high tracking accuracy but also could meet the real-time performance requirement.

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Efficient Moving Target Tracking Algorithm: Design and Implementation of the Sand-Table Tracking Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.475-476.1032 ◽

2013 ◽

Vol 475-476 ◽

pp. 1032-1039

Author(s):

Jia Qi Li

Keyword(s):

Target Tracking ◽

Algorithm Design ◽

Tracking Algorithm ◽

Moving Target ◽

Tracking Accuracy ◽

Moving Target Tracking ◽

Design And Implementation ◽

Tracking Ability

Working on the design of a new algorithm :sand_table algorithm.The algorithm could work well in recognizing and tracking an single moving target shot by camera or in a video .The algorithm works simple with low operation cost.May used in tracking different object of many kinds.The algorithm imitate the the process of falling sands to Greatly enhance the tracking ability and tracking accuracy.

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Object Detection and Recognition Algorithm of Moving UAV Based on Machine Vision

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2016.5770 ◽

2016 ◽

Vol 13 (10) ◽

pp. 7731-7737

Author(s):

Mao Jun

Keyword(s):

Target Tracking ◽

Optimal Solution ◽

Target Position ◽

Multiple Instance Learning ◽

Tracking Algorithm ◽

Tracking Accuracy ◽

L1 Norm ◽

Original Distribution ◽

Local Optimal Solution ◽

Distribution Field

To conquer disadvantages of slow speed of target tracking algorithm in original distribution field as well as easiness of being caught in local optimal solution, one target tracking algorithm of real-time distribution field based on global matching is presented in the Thesis, thus remarkably improving performance of target tracking algorithm in distribution field. In proposed algorithm, relevant correlation coefficients will be used to substitute the similarity between target distribution filed of original L1 norm measurement and candidate distribution field. As a consequence, the target search process can concert from time domain operation to operation processing of frequency domain among which the latter has lower computation complexity and capability of global search of target position so as to conquer disadvantages such as randomness caused by sparse sampling and that the gradient descent of target tracking algorithm in original distribution field is liable to be caught in local optimal solution. In 12 challenging video sequences, compared with multiple-instance learning and tracking algorithm and tracking algorithm of original distribution field, the method proposed in the Thesis has acquired the optimum performance in tracking accuracy, success rate and speed.

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Underwater Target Tracking Algorithm Based on an Improved Color Matching

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.834-836.1234 ◽

2013 ◽

Vol 834-836 ◽

pp. 1234-1239

Author(s):

Ling Yu Sun ◽

Ming Ming Li ◽

Zhao Wang

Keyword(s):

Target Tracking ◽

Tracking System ◽

Size Variation ◽

Window Size ◽

Tracking Algorithm ◽

Color Matching ◽

Tracking Accuracy ◽

Matching Algorithm ◽

Underwater Image ◽

Underwater Target

Owing to fuzzy detail and distortion of underwater image and complex changes of target, the underwater target tracking system requires accuracy and continuity of tracking, and expects that the size of tracking window can adapt to appearance change of target. According to the requirements mentioned above, the underwater target tracking algorithm based on an improved color matching is proposed, which finds the best location of target through tracking accuracy algorithm and calculates width of window on the basis of tracking window size variation algorithm. The experimental results show that this algorithm can adaptively track the real-time target and has higher accuracy than traditional color matching algorithm.

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Multi-target tracking algorithm in intelligent transportation based on wireless sensor network

Open Physics ◽

10.1515/phys-2018-0121 ◽

2018 ◽

Vol 16 (1) ◽

pp. 1000-1008 ◽

Cited By ~ 1

Author(s):

Yang Lei ◽

Yuan Wu ◽

Ahmad Jalal Khan Chowdhury

Keyword(s):

Target Tracking ◽

Information Exchange ◽

Intelligent Transportation System ◽

Tracking Error ◽

Intelligent Transportation ◽

Tracking Algorithm ◽

Tracking Accuracy ◽

Multiple Targets ◽

Tracking Process ◽

Multi Target Tracking

Abstract The traditional extended Kalman algorithm for multi-target tracking in the field of intelligent transportation does not consider the occlusion problem of the multi-target tracking process, and has the disadvantage of low multi-target tracking accuracy. A multi-target tracking algorithm using wireless sensors in an intelligent transportation system is proposed. Based on the dynamic clustering structure, the measurement results of each sensor are the superimposed results of sound signals and environmental noise from multiple targets. During the tracking process, each target corresponds to a particle filter. When the target spacing is relatively close to each other, each master node realizes distributed multi-target tracking through information exchange. At the same time, it is also necessary to consider the overlap between adjacent frames. Since the moving target speed is too fast, the target occlusion has the least influence on the tracking accuracy, and can accurately track multiple targets. The experimental results show that the proposed algorithm has a target tracking error of 0.5 m to 1 m, and the tracking result has high precision.

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A Real-Time Structured Output Tracker with Scale Adaption for Visual Target Tracking

Mathematical Problems in Engineering ◽

10.1155/2021/4649358 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Kaiyun Yang ◽

Xuedong Wu ◽

Jingxiang Xu

Keyword(s):

Target Tracking ◽

Real Time ◽

Visual Target ◽

Target Position ◽

Tracking Algorithm ◽

Tracking Accuracy ◽

Output Tracking ◽

Real Time Tracking ◽

Structured Output ◽

Visual Target Tracking

The structured output tracking algorithm is a visual target tracking algorithm with excellent comprehensive performance in recent years. However, the algorithm classifier will produce error information and result in target loss or tracking failure when the target is occluded or the scale changes in the process of tracking. In this work, a real-time structured output tracker with scale adaption is proposed: (1) the target position prediction is added in the process of target tracking to improve the real-time tracking performance; (2) the adaptive scheme of target scale discrimination is proposed in the structured support to improve the overall tracking accuracy; and (3) the Kalman filter is used to solve the occlusion problem of continuous tracking. Extensive evaluations on the OTB-2015 benchmark dataset with 100 sequences have shown that the proposed tracking algorithm can run at a highly efficient speed of 84 fps and perform favorably against other tracking algorithms.

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