Target Tracking Using a Hybrid KF-PSO Tracking Model in WSN

2020 ◽  
pp. 83-98
Author(s):  
Dhiren P. Bhagat ◽  
Himanshukumar Soni
Keyword(s):  
Target Tracking ◽  
Tracking Model

A novel improved CKF with adaptive generation of the cubature points and weights for target tracking

2021 ◽  
Author(s):  
Hongpo Fu ◽  
Yongmei Cheng ◽  
Cheng Cheng
Keyword(s):  
Target Tracking ◽  
Superior Performance ◽  
Inner Product ◽  
Estimation Accuracy ◽  
Cubature Rule ◽  
Cubature Kalman Filter ◽  
One Step ◽  
Tracking Model ◽  
Dynamic Filtering ◽  
High Degree

Abstract In the nonlinear state estimation, the generation method of cubature points and weights of the classical cubature Kalman filter (CKF) limits its estimation accuracy. Inspired by that, in this paper, a novel improved CKF with adaptive generation of the cubature points and weights is proposed. Firstly, to improve the accuracy of classical CKF while considering the calculation efficiency, we introduce a new high-degree cubature rule combining third-order spherical rule and sixth-degree radial rule. Next, in the new cubature rule, a novel method that can generate adaptively cubature points and weights based on the distance between the points and center point in the sense of the inner product is designed. We use the cosine similarity to quantify the distance. Then, based on that, a novel high-degree CKF is derived that use much fewer points than other high-degree CKF. In the proposed filter, based on the actual dynamic filtering process, the simultaneously adaptive generation of cubature points and weight can make the filter reasonably distribute the cubature points and allocate the corresponding weights, which can obviously improve the approximate accuracy of one-step state and measurement prediction. Finally, the superior performance of the proposed filter is demonstrated in a benchmark target tracking model.

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

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.

scholarly journals Research on Moving Target Tracking Based on FDRIG Optical Flow

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1122
Author(s):  
Gong ◽  
Wang
Keyword(s):  
Optical Flow ◽  
Target Tracking ◽  
Research Work ◽  
Flow Equation ◽  
Superior Performance ◽  
Moving Target ◽  
Moving Target Tracking ◽  
Motion Sequence ◽  
Tracking Model ◽  
Vehicle Motion

Aiming at the problem of moving target recognition, a moving target tracking model based on FDRIG optical flow is proposed. First, the optical flow equation was analyzed from the theory of optical flow. Then, with the energy functional minimization, the FDRIG optical flow technique was proposed. Taking a road section of a university campus as an experimental section, 30 vehicle motion sequence images were considered as objects to form a vehicle motion sequence image with a complex background. The proposed FDRIG optical flow was used to calculate the vehicle motion optical flow field by the Halcon software. Comparable with the classic Horn and Schunck (HS) and Lucas and Kande (LK) optical flow algorithm, the monitoring results proved that the FDRIG optical flow was highly precise and fast when tracking a moving target. The Ettlinger Tor traffic scene was then taken as the second experimental object; FDRIG optical flow was used to analyze vehicle motion. The superior performance of the FDRIG optical flow was further verified. The whole research work shows that FDRIG optical flow has good performance and speed in tracking moving targets and can be used to monitor complex target motion information in real-time.

Artificial intelligence based tracking model for functional sports training goals in competitive sports

2020 ◽  
pp. 1-13
Author(s):  
Zhe Zhao ◽  
Xingyu Liu ◽  
Xi She
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Physical Training ◽  
Particle Filtering ◽  
Binary Classification ◽  
Moving Target ◽  
Competitive Sports ◽  
Balance Test ◽  
Moving Target Tracking ◽  
Tracking Model

As an advanced training concept, functional physical training is gradually recognized by top athletes for its high training effect and low sports injury. Functional physical training should gradually develop from elite athletes to grassroots athletes, so as to lay a solid foundation for the development of competitive sports. Because particle filtering is susceptible to external factors in moving target tracking, this paper designs a method for sparse coding using local image blocks of the target, establishes a static “impression” and dynamic model for the appearance of the target. The tracking problem is regarded as a binary classification problem between the foreground target and the background image. During the tracking process, the dual particle filter is implemented to alleviate the tracking drift, so that the algorithm can adaptively capture the changes in the target appearance At the same time, it can reduce the update caused by wrong positioning. The subjects’ FMS test and Y balance test have improved in varying degrees; the pressure distribution of the forefoot, arch, and heel tends to be rationalized, and the ratio of internal and external splayed feet has decreased. Experiments show that this particle filter moving target tracking scheme can adapt to changes in the environment and overcome the inflexibility of the global template when dealing with local changes in the target.

Switching Multisensor Improved Current Statistical Model for Maneuvering Target Tracking

2013 ◽  
Vol 385-386 ◽  
pp. 585-588
Author(s):  
Qi Fang He ◽  
Yan Bin Li ◽  
Hang Lv ◽  
Guang Jun He
Keyword(s):  
Statistical Model ◽  
Target Tracking ◽  
Tracking System ◽  
Sensor Management ◽  
Maneuvering Target Tracking ◽  
System Parameters ◽  
Maneuvering Target ◽  
Tracking Model ◽  
Transient Error ◽  
Random Change

For an actual maneuvering target tracking system, the random change of system parameters and structure can often occur. So the whole system is dynamic, and there is uncertainty in system parameters and structure. In the paper, theory of system with random changing structures and technology of sensor management is introduced to build a tracking model with random changing structures. For the considering of random changing, the transient error brought by structure random changing is overcome.

scholarly journals Multitarget Real-Time Tracking Algorithm for UAV IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Tao Hong ◽  
Qiye Yang ◽  
Peng Wang ◽  
Jinmeng Zhang ◽  
Wenbo Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Target Tracking ◽  
Real Time ◽  
High Speed ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Urban Life ◽  
Tracking Accuracy ◽  
Tracking Model ◽  
Real Time Tracking

Unmanned aerial vehicles (UAVs) have increased the convenience of urban life. Representing the recent rapid development of drone technology, UAVs have been widely used in fifth-generation (5G) cellular networks and the Internet of Things (IoT), such as drone aerial photography, express drone delivery, and drone traffic supervision. However, owing to low altitude and low speed, drones can only limitedly monitor and detect small target objects, resulting in frequent intrusion and collision. Traditional methods of monitoring the safety of drones are mostly expensive and difficult to implement. In smart city construction, a large number of smart IoT cameras connected to 5G networks are installed in the city. Captured drone images are transmitted to the cloud via a high-speed and low-latency 5G network, and machine learning algorithms are used for target detection and tracking. In this study, we propose a method for real-time tracking of drone targets by using the existing monitoring network to obtain drone images in real time and employing deep learning methods by which drones in urban environments can be guided. To achieve real-time tracking of UAV targets, we employed the tracking-by-detection mode in machine learning, with the network-modified YOLOv3 (you only look once v3) as the target detector and Deep SORT as the target tracking correlation algorithm. We established a drone tracking dataset that contains four types of drones and 2800 pictures in different environments. The tracking model we trained achieved 94.4% tracking accuracy in real-time UAV target tracking and a tracking speed of 54 FPS. These results comprehensively demonstrate that our tracking model achieves high-precision real-time UAV target tracking at a reduced cost.

scholarly journals Long-Term Target Tracking of UAVs Based on Kernelized Correlation Filter

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3006
Author(s):  
Junqiang Yang ◽  
Wenbing Tang ◽  
Zuohua Ding
Keyword(s):  
Target Tracking ◽  
Template Matching ◽  
Control Method ◽  
Target Position ◽  
Correlation Filter ◽  
Background Information ◽  
The Status ◽  
Tracking Model ◽  
Update Strategy

During the target tracking process of unmanned aerial vehicles (UAVs), the target may disappear from view or be fully occluded by other objects, resulting in tracking failure. Therefore, determining how to identify tracking failure and re-detect the target is the key to the long-term target tracking of UAVs. Kernelized correlation filter (KCF) has been very popular for its satisfactory speed and accuracy since it was proposed. It is very suitable for UAV target tracking systems with high real-time requirements. However, it cannot detect tracking failure, so it is not suitable for long-term target tracking. Based on the above research, we propose an improved KCF to match long-term target tracking requirements. Firstly, we introduce a confidence mechanism to evaluate the target tracking results to determine the status of target tracking. Secondly, the tracking model update strategy is designed to make the model suffer from less background information interference, thereby improving the robustness of the algorithm. Finally, the Normalized Cross Correlation (NCC) template matching is used to make a regional proposal first, and then the tracking model is used for target re-detection. Then, we successfully apply the algorithm to the UAV system. The system uses binocular cameras to estimate the target position accurately, and we design a control method to keep the target in the UAV’s field of view. Our algorithm has achieved the best results in both short-term and long-term evaluations of experiments on tracking benchmarks, which proves that the algorithm is superior to the baseline algorithm and has quite good performance. Outdoor experiments show that the developed UAV system can achieve long-term, autonomous target tracking.

Maneuvering Target Tracking Algorithm Based on Improved "Self-Adaptive Turning Rate" Model

2013 ◽  
Vol 278-280 ◽  
pp. 1682-1686
Author(s):  
Xiu Ling He ◽  
Jing Song Yang
Keyword(s):  
Target Tracking ◽  
Rate Model ◽  
Simulation Test ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target ◽  
Modeling Methods ◽  
Actual Application ◽  
Model Set ◽  
Tracking Model ◽  
Self Adaptive

Multi-model (interactive multi-model )tracking is an effective method for maneuvering target tracking, and turning model is widely studied and applied in multi-model tracking modeling and model set establishment. A detailed study is carried out on the self-adaptive turning model modeling method for maneuvering target tracking. Also a summary is made about the method of establishing tracking model by calculating the turning rate. The shortcomings of literature [4、6]'s self-adaptive turning model in actual application is also pointed out, with two modeling methods adopting average turning rate presented accordingly. The simulation test proves the necessity of improvement and the validity of the new model.

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