scholarly journals Non-overlapping Distributed Tracking System Utilizing Particle Filter

2007 ◽  
Vol 49 (3) ◽  
pp. 343-362 ◽  
Author(s):  
F. L. Lim ◽  
W. Leoputra ◽  
T. Tan
Keyword(s):  
Particle Filter ◽  
Tracking System ◽  
Distributed Tracking

scholarly journals An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 135972-135981
Author(s):  
Mousa Nazari ◽  
Saeid Pashazadeh ◽  
Leyli Mohammad-Khanli
Keyword(s):  
Fuzzy Clustering ◽  
Tracking System ◽  
Track Association ◽  
Distributed Tracking

scholarly journals Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2671
Author(s):  
Yifang Shi ◽  
Jee Woong Choi ◽  
Lei Xu ◽  
Hyung June Kim ◽  
Ihsan Ullah ◽  
...  
Keyword(s):  
Measurement Accuracy ◽  
Tracking System ◽  
Sampling Interval ◽  
Fusion Center ◽  
Forward Prediction ◽  
Distributed Target ◽  
Calculated Probability ◽  
Track Management ◽  
Internal Processing ◽  
Distributed Tracking

In the multiple asynchronous bearing-only (BO) sensors tracking system, there usually exist two main challenges: (1) the presence of clutter measurements and the target misdetection due to imperfect sensing; (2) the out-of-sequence (OOS) arrival of locally transmitted information due to diverse sensor sampling interval or internal processing time or uncertain communication delay. This paper simultaneously addresses the two problems by proposing a novel distributed tracking architecture consisting of the local tracking and central fusion. To get rid of the kinematic state unobservability problem in local tracking for a single BO sensor scenario, we propose a novel local integrated probabilistic data association (LIPDA) method for target measurement state tracking. The proposed approach enables eliminating most of the clutter measurement disturbance with increased target measurement accuracy. In the central tracking, the fusion center uses the proposed distributed IPDA-forward prediction fusion and decorrelation (DIPDA-FPFD) approach to sequentially fuse the OOS information transmitted by each BO sensor. The track management is carried out at local sensor level and also at the fusion center by using the recursively calculated probability of target existence as a track quality measure. The efficiency of the proposed methodology was validated by intensive numerical experiments.

Using Cumulative Histogram Maps in an Adaptive Color-Based Particle Filter for Real-Time Object Tracking

2010 ◽  
Vol 121-122 ◽  
pp. 585-590 ◽  
Author(s):  
San Lung Zhao ◽  
Shen Zheng Wang ◽  
Hsi Jian Lee ◽  
Hung I Pai
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Tracking System ◽  
Target Object ◽  
Color Histogram ◽  
Human Tracking ◽  
Speed Up ◽  
Non Gaussian ◽  
The Cost ◽  
Gaussian Estimation

The study presents a human tracking system. To tracking a person, we adopt a particle filter as tracking kernel, since the method has proven successful for tracking in non-linear and non-Gaussian estimation. In a particle filter, a set of weighted particles represents the possible target sates. In this study, we measure the weight according to both the appearances of the target object and background scene to improve the discriminability between them. In our tracker, the appearances are modeled as color histogram, since it is scale and rotation invariant. However, the color histogram extraction for a large number of overlap regions is repeated redundantly and inefficiently. To speed up it, we reduce the cost for calculating overlapped regions by creating a cumulative histogram map for the processing image. The experimental results show that the tracker has the best precision improvement, and the tracking speed is 49.7 fps for 384 × 288 resolution, when we use 600 particles. The results show that the proposed method can be applied to a real-time human tracking system with high precision.

Moving Object Tracking System Based on Particle Filter on FPGA Design and Implementation

2012 ◽  
Vol 203 ◽  
pp. 31-35
Author(s):  
Jun Song ◽  
Han Wen Wu
Keyword(s):  
Particle Filter ◽  
Processing Speed ◽  
Tracking System ◽  
Tracking Accuracy ◽  
Fpga Design ◽  
Fast Calculation ◽  
And Performance ◽  
Operation Cycle ◽  
Long Operation ◽  
Processing Mechanism

In view of the large amount of calculation and long operation cycle in the particle filter tracking algorithm. This paper puts forward a particle filter designed in the FPGA platform, using fast calculation speed and parallel processing mechanism of the FPGA to improve the processing speed and performance of the system. The experimental results show that the system has a good real-time and tracking accuracy. Its portability is strong, and having a broad prospect of application.

scholarly journals Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Anna Elena Tirri ◽  
Giancarmine Fasano ◽  
Domenico Accardo ◽  
Antonio Moccia
Keyword(s):  
Particle Filter ◽  
Particle Filtering ◽  
Risk Estimation ◽  
Tracking System ◽  
Nonlinear Problems ◽  
Obstacle Detection ◽  
Collision Risk ◽  
Sense And Avoid ◽  
Particle Filter Algorithm ◽  
The Impact

Obstacle detection and tracking is a key function for UAS sense and avoid applications. In fact, obstacles in the flight path must be detected and tracked in an accurate and timely manner in order to execute a collision avoidance maneuver in case of collision threat. The most important parameter for the assessment of a collision risk is the Distance at Closest Point of Approach, that is, the predicted minimum distance between own aircraft and intruder for assigned current position and speed. Since assessed methodologies can cause some loss of accuracy due to nonlinearities, advanced filtering methodologies, such as particle filters, can provide more accurate estimates of the target state in case of nonlinear problems, thus improving system performance in terms of collision risk estimation. The paper focuses on algorithm development and performance evaluation for an obstacle tracking system based on a particle filter. The particle filter algorithm was tested in off-line simulations based on data gathered during flight tests. In particular, radar-based tracking was considered in order to evaluate the impact of particle filtering in a single sensor framework. The analysis shows some accuracy improvements in the estimation of Distance at Closest Point of Approach, thus reducing the delay in collision detection.

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