Dynamic Target Tracking Based on Particle Filter in Actual Environment

2013 ◽  
Vol 683 ◽  
pp. 824-827
Author(s):  
Tian Ding Chen ◽  
Chao Lu ◽  
Jian Hu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Monitoring System ◽  
Filter Method ◽  
Complex Scene ◽  
Actual Environment ◽  
Dynamic Target ◽  
Gaussian System ◽  
Non Gaussian

With the development of science and technology, target tracking was applied to many aspects of people's life, such as missile navigation, tanks localization, the plot monitoring system, robot field operation. Particle filter method dealing with the nonlinear and non-Gaussian system was widely used due to the complexity of the actual environment. This paper uses the resampling technology to reduce the particle degradation appeared in our test. Meanwhile, it compared particle filter with Kalman filter to observe their accuracy .The experiment results show that particle filter is more suitable for complex scene, so particle filter is more practical and feasible on target tracking.

scholarly journals A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter

2021 ◽  
Vol 11 (21) ◽  
pp. 10270
Author(s):  
Yong Tao ◽  
Fan Ren ◽  
He Gao ◽  
Tianmiao Wang ◽  
Shan Jiang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Particle Filtering ◽  
Servo Control ◽  
Simulation Environment ◽  
Visual Servo ◽  
Affine Groups ◽  
Actual Environment ◽  
Dynamic Target ◽  
Particle Filter Algorithm

Tracking and grasping a moving target is currently a challenging topic in the field of robotics. The current visual servo grasping method is still inadequate, as the real-time performance and robustness of target tracking both need to be improved. A target tracking method is proposed based on improved geometric particle filtering (IGPF). Following the geometric particle filtering (GPF) tracking framework, affine groups are proposed as state particles. Resampling is improved by incorporating an improved conventional Gaussian resampling algorithm. It addresses the problem of particle diversity loss and improves tracking performance. Additionally, the OTB2015 dataset and typical evaluation indicators in target tracking are adopted. Comparative experiments are performed using PF, GPF and the proposed IGPF algorithm. A dynamic target tracking and grasping method for the robot is proposed. It combines an improved Gaussian resampling particle filter algorithm based on affine groups and the positional visual servo control of the robot. Finally, the robot conducts simulation and experiments on capturing dynamic targets in the simulation environment and actual environment. It verifies the effectiveness of the method proposed in this paper.

The Particle Filter Algorithm Research for Target Tracking Based on Information Fusion

2012 ◽  
Vol 628 ◽  
pp. 440-444 ◽  
Author(s):  
Juan Li ◽  
Hui Juan Hao ◽  
Mao Li Wang
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Information Fusion ◽  
Particle Filtering ◽  
Tracking System ◽  
Distributed Sensor ◽  
Multi Target Tracking ◽  
Particle Filter Algorithm ◽  
Gaussian System ◽  
Non Gaussian

This paper researches the particle filters Algorithms for target tracking based on Information Fusion, it combines the traditional Kalman filter with the particle filter. For multi-sensor and multi-target tracking system with complex application background, which is nonlinear and non-gaussian system, the paper proposes an effective particle filtering algorithm based on information fusion for distributed sensor, this algorithm contributes to the solution of particle degradation problems and the phenomenon of particle lack, and achieve high precision for target tracking.

ALGORITHM OF IMM COMBINING KALMAN AND PARTICLE FILTER FOR MANEUVERING TARGET TRACKING

2006 ◽  
Vol 03 (04) ◽  
pp. 321-328
Author(s):  
GUIXI LIU ◽  
ENKE GAO ◽  
CHUNYU FAN
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Nonlinear Problems ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target ◽  
Computation Efficiency ◽  
Non Gaussian

The particle filter can deal with nonlinear/non-Gaussian problems and it has been introduced to the algorithm of the interacting multiple model (IMM) for higher precision. The general IMM based on Kalman filter or extended Kalman filter (IMMEKF) cannot deal with non-Gaussian problems and also does not work as well as the IMM based on the particle filter (IMMPF) for the nonlinear problems. However the problem of the particle filter is its expensive computation, because a particle filter usually has a lot of particles, which will increase the computation load greatly. Here an algorithm of IMM combining the Kalman filter and the particle filter (IMMK-PF) for maneuvering target tracking is proposed to improve the computation efficiency as compared to the IMMPF. For nonlinear/Gaussian problems the new algorithm is expected to have a good performance as the IMMPF, while for linear problems it will perform similarly to the IMMEKF and will work better than the IMMPF.

An Implicit Algorithm of Solving Nonlinear Filtering Problems

2014 ◽  
Vol 16 (2) ◽  
pp. 382-402
Author(s):  
Feng Bao ◽  
Yanzhao Cao ◽  
Xiaoying Han
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Nonlinear Filtering ◽  
State Equation ◽  
Nonlinear Filter ◽  
Filter Method ◽  
Simulation Methods ◽  
Current State ◽  
Numerical Simulation Methods ◽  
Kalman Filter Method

AbstractNonlinear filter problems arise in many applications such as communications and signal processing. Commonly used numerical simulation methods include Kalman filter method, particle filter method, etc. In this paper a novel numerical algorithm is constructed based on samples of the current state obtained by solving the state equation implicitly. Numerical experiments demonstrate that our algorithm is more accurate than the Kalman filter and more stable than the particle filter.

scholarly journals Iterative Truncated Unscented Particle Filter

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 214
Author(s):  
Yanbo Wang ◽  
Fasheng Wang ◽  
Jianjun He ◽  
Fuming Sun
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Process Models ◽  
Filter Method ◽  
Proposal Distribution ◽  
Truncation Method ◽  
Unscented Particle Filter

The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.

Particle filter initialization in non-linear non-Gaussian radar target tracking

2007 ◽  
Vol 18 (3) ◽  
pp. 491-496 ◽  
Author(s):  
Wang Lian ◽  
Jin Yonggao ◽  
Dai Dingzhang ◽  
Dong Huachun ◽  
Quan Taifan
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Radar Target ◽  
Non Linear ◽  
Non Gaussian

scholarly journals Improved Interacting Multiple Model Particle Filter Algorithm

2018 ◽  
Vol 36 (1) ◽  
pp. 169-175 ◽  
Author(s):  
Qiaoran Liu ◽  
Xun Yang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Proposal Distribution ◽  
Model Particle ◽  
Interactive Multiple Model ◽  
Particle Filter Algorithm

For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.

scholarly journals A Zero-Velocity Update Method for Adaptive Particle Filtering

2020 ◽  
Vol 38 (2) ◽  
pp. 427-433
Author(s):  
Yuyang Guo ◽  
Xiangbo Xu ◽  
Miaoxin Ji
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Particle Filtering ◽  
State Space Model ◽  
Classical Particle ◽  
Measurement Unit ◽  
Correction Algorithm ◽  
Zero Velocity ◽  
Non Gaussian ◽  
Navigation Errors

Aiming at the low precision of Kalman filter in dealing with non-linear and non-Gaussian models and the serious particle degradation in standard particle filter, a zero-velocity correction algorithm of adaptive particle filter is proposed in this paper. In order to improve the efficiency of resampling, the adaptive threshold is combined with particle filter. In the process of resampling, the degradation co-efficient is introduced to judge the degree of particle degradation, and the particles are re-sampled to ensure the diversity of particles. In order to verify the effectiveness and feasibility of the proposed algorithm, a hardware platform based on the inertial measurement unit (IMU) is built, and the state space model of the system is established by using the data collected by IMU, and experiments are carried out. The experimental results show that, compared with Kalman filter and classical particle filter, the positioning accuracy of adaptive particle filter in zero-velocity range is improved by 40.6% and 19.4% respectively. The adaptive particle filter (APF) can correct navigation errors better and improve pedestrian trajectory accuracy.

Robust filtering with randomly delayed measurements and its application to ballistic target tracking in boost phase

2019 ◽  
Vol 41 (7) ◽  
pp. 2077-2088 ◽  
Author(s):  
Wutao Qin ◽  
Xiaogang Wang ◽  
Naigang Cui
Keyword(s):  
Target Tracking ◽  
System Model ◽  
Robust Filtering ◽  
Bernoulli Random Variables ◽  
System A ◽  
M Estimator ◽  
Delayed Measurements ◽  
Gaussian System ◽  
Non Gaussian ◽  
High Degree

Motivated by the performance degradation of High-degree Cubature Kalman Filtering (HCKF) in coping with randomly delayed measurements in non-Gaussian system, a novel robust filtering named as Randomly Delayed High-degree Cubature Huber-based Filtering (RD-HCHF) is proposed in this paper. At first, the system model is re-written by the Bernoulli random variables to describe the randomly delayed measurements. Then, the Randomly Delayed HCKF (RD-HCKF) is derived based on the rewritten system model and 5th-degree spherical-radial cubature (SRC) rule. In order to enhance the robustness of the filter in glint noise case, the measurement update of RD-HCKF is modified by the Huber technique, which is essentially an M-estimator. Therefore, the proposed RD-HCHF is not only robust to the randomly delayed measurements, but also robust to the glint noise. In addition, the RD-HCHF is applied to the ballistic target tracking in boost phase, and the Gravity-Turn (GT) model is taken as the target model. Finally, the simulation is conducted and the tracking performance of RD-HCHF is compared with that of HCKF, RD-HCKF and High-degree Cubature Huber-based Filtering (HCHF). The results clearly confirm the superiority of the RD-HCHF.

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