Description: Tactically maneuvering targets are difficult
to track since acceleration cannot be observed
directly and the accelerations are induced by human
control or an autonomous guidance system therefore
they are not subject to deterministic models. A common
tracking system is the two-state Kalman Filter
with a Singer maneuver model where the second order
statistics of acceleration is the same as a first
order Markov process. The Singer model assumes a
uniform probability distribution on the target s acceleration
which is independent of the x and y direction.
In practice, it is expected that targets have constant
forward speed and an acceleration vector normal to
the velocity vector, a condition not present in the
Singer model. This paper extends the work of Singer
by presenting a maneuver model which assumes constant
forward speed and a probability distribution on
the targets turn-rate-Tactically maneuvering targets are difficult
to track since acceleration cannot be observed
directly and the accelerations are induced by human
control or an autonomous guidance system therefore
they are not subject to deterministic models. A common
tracking system is the two-state Kalman Filter
with a Singer maneuver model where the second order
statistics of acceleration is the same as a first
order Markov process. The Singer model assumes a
uniform probability distribution on the target s acceleration
which is independent of the x and y direction.
In practice, it is expected that targets have constant
forward speed and an acceleration vector normal to
the velocity vector, a condition not present in the
Singer model. This paper extends the work of Singer
by presenting a maneuver model which assumes constant
forward speed and a probability distribution on
the targets turn-rate Platform: |
Size: 265216 |
Author:jorgehas |
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Description: 卡尔曼滤波在目标跟踪中的应用与仿真,对给定飞行方向和飞行速度的飞行器航迹进行了滤波；通过Matlab进行了仿真，并给出了仿真结果。-Kalman filtering in target tracking application and simulation, for a given flight direction and flight speed of the aircraft track is filtered through Matlab simulation, and the simulation results. Platform: |
Size: 2048 |
Author:彩云 |
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Description: 分别使用传统卡尔曼滤波和改进的自适应卡尔曼滤波对运动物体进行跟踪，比较并分析结果，验证自适应卡尔玛滤波的优点。-Respectively using conventional Kalman filter and adaptive Kalman filter for improved tracking of moving objects, compare and analyze the results to verify the advantages of adaptive filtering Kalmar. Platform: |
Size: 5120 |
Author:张军令 |
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Description: 包含大量的卡尔曼滤波，平滑，有小例子来学习，老外编的-Kalman filter toolbox written by Kevin Murphy, 1998.
See http://www.ai.mit.edu/~murphyk/Software/kalman.html for details.
Installation
1. Install KPMtools http://www.ai.mit.edu/~murphyk/Software/KPMtools.html
3. Assuming you installed all these files in your matlab directory, In Matlab type
addpath matlab/KPMtools
addpath matlab/Kalman
Demos
-
See tracking_demo.m for a demo of 2D tracking.
See learning_demo.m for a demo of parameter estimation using EM.
Platform: |
Size: 13312 |
Author:李腾 |
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Description: 一篇参考了大量外文文献的基础上写的人体运动跟踪的综述，供大家参考。-An reference to a large base of foreign literature written a review of human motion tracking, for your reference. Platform: |
Size: 323584 |
Author:郭文浩 |
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Description: 基于OPENCV再结合kalman滤波对监控视频进行目标跟踪。-OPENCV combined kalman filtering based on surveillance video for target tracking. Platform: |
Size: 6144 |
Author:fangqimin |
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Description: Kalman滤波技术结合MATLAB库对监控视频进行目标跟踪。-Kalman filtering techniques combined with MATLAB bank on surveillance video for target tracking. Platform: |
Size: 649216 |
Author:fangqimin |
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Description: This code denotes the implementation of kalman filter. kalman filter is used for tracking vechicles in ITS Platform: |
Size: 1024 |
Author:premaram |
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Description: Sigma point Kalman fi lter for bearing only tracking
S. Sadhua,, S. Mondal
b
, M. Srinivasana
, T.K. Ghoshal
a
a
Department of Electrical Engineering, Jadavpur University, Kolkata-700032, India
b
Department of Mechanical Engineering, IIT Kharagpur 721302, India
Received 10 August 2005 received in revised form 14 December 2005 accepted 12 March 2006
Available online 6 April 2006-Sigma point Kalman fi lter for bearing only tracking
S. Sadhua,, S. Mondal
b
, M. Srinivasana
, T.K. Ghoshal
a
a
Department of Electrical Engineering, Jadavpur University, Kolkata-700032, India
b
Department of Mechanical Engineering, IIT Kharagpur 721302, India
Received 10 August 2005 received in revised form 14 December 2005 accepted 12 March 2006
Available online 6 April 2006 Platform: |
Size: 177152 |
Author:Gomaa Haroun |
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Description: The Unscented Kalman Filter for State Estimation
of 3-Dimension Bearing-Only Tracking
WANG Wan-ping
1,2
1
Institute of Optics and Electronics, Chinese Academy of
Sciences, Chendu, P.R. China
2
Graduate School of the Chinese Academy of Sciences,
Beijing, P.R. China
LIAO Sheng1
, XING Ting-wen1
Institute of Optics and Electronics,
Chinese Academy of Sciences,
Chendu, P.R. China
e-mail: BDBQX_LS@sina.com-The Unscented Kalman Filter for State Estimation
of 3-Dimension Bearing-Only Tracking
WANG Wan-ping
1,2
1
Institute of Optics and Electronics, Chinese Academy of
Sciences, Chendu, P.R. China
2
Graduate School of the Chinese Academy of Sciences,
Beijing, P.R. China
LIAO Sheng1
, XING Ting-wen1
Institute of Optics and Electronics,
Chinese Academy of Sciences,
Chendu, P.R. China
e-mail: BDBQX_LS@sina.com Platform: |
Size: 296960 |
Author:Gomaa Haroun |
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Description: The extended Kalman filter applied to bearings-only target tracking
is theoretically analyzed. Closed-form expressions for the state
vector and its associated covariance matrix are introduced, and subsequently
used to demonstrate how bearing and range estimation
errors can interact to cause filter instability (i.e., premature covariance
collapse and divergence). Further investigation reveals that
conventional initialization techniques often precipitate such anomalous
behavior. These results have important practical implications
and are not presently being exploited to full advantage. Platform: |
Size: 1933312 |
Author:Gomaa Haroun |
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Search - kalman for tracking