Particle Filter for Ballistic Target Tracking with Glint Noise

2010 ◽  
Vol 33 (6) ◽  
pp. 1918-1921 ◽  
Author(s):  
Jinwhan Kim ◽  
Monish Tandale ◽  
P. K. Menon ◽  
Ernest Ohlmeyer
Keyword(s):  
Particle Filter ◽  
Target Tracking

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.

Rao-Blackwellized particle filter for multiple target tracking

2007 ◽  
Vol 8 (1) ◽  
pp. 2-15 ◽  
Author(s):  
Simo Särkkä ◽  
Aki Vehtari ◽  
Jouko Lampinen
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Multiple Target Tracking ◽  
Multiple Target

scholarly journals Bayesian multiple target tracking

2021 ◽  
Author(s):  
◽  
Praveen Babu Choppala
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Array Signal Processing ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Optimal Method ◽  
Array Data ◽  
Probability Hypothesis Density ◽  
Kolmogorov Smirnov ◽  
Suitable Framework

<p>This thesis addresses several challenges in Bayesian target tracking, particularly for array signal processing applications, and for multiple targets.  The optimal method for multiple target tracking is the Bayes’ joint filter that operates by hypothesising all the targets collectively using a joint state. As a consequence, the computational complexity of the filter increases rapidly with the number of targets. The probability hypothesis density and the multi-Bernoulli filters that overcome this complexity do not possess a suitable framework to operate directly on phased sensor array data. Instead, such data is converted into beamformer images in which close targets may not be effectively resolved and much information is lost. This thesis develops a multiple signal classification (MUSIC) based multi-target particle filter that improves upon the filters mentioned above. A MUSIC based multi-Bernoulli particle filter is also developed, that operates more directly on array data.  The above mentioned particle filters require a resampling step which impedes information accumulation over successive observations, and affects the detection of very covert targets. This thesis develops soft resampling and soft systematic resampling to overcome this problem without affecting the accuracy of approximation. Additionally, modified Kolmogorov-Smirnov testing is proposed, to numerically evaluate the accuracy of the particle filter approximation.</p>

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