Description: 目标跟踪的扩展卡尔曼滤波算法主函数的文件是：kal_demo.m
近似网格滤波的主函数文件是：bayes_demo.m
近似网格滤波划分网格的方法是：以目标上一个时刻的位置作为中心进行网格的划分，每个网格大小为1，总的区域为5*5
改进后算法的主函数文件是：trackiing_demo.m-Target tracking extended Kalman filter algorithm is the main function of the document: kal_demo.m approximate mesh filter paper is the main function: bayes_demo.m approximate mesh filter into the grid method is: in order to target the location of a moment as the central to carry out the division of the grid, each grid size of 1, with a total area of 5* 5 to improve the algorithm Platform: |
Size: 138240 |
Author:Lin |
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Description: 目标跟踪的扩展卡尔曼滤波算法主函数的文件是：kal_demo.m 近似网格滤波的主函数文件是：bayes_demo.m 近似网格滤波划分网格的方法是：以目标上一个时刻的位置作为中心进行网格的划分，每个网格大小为1，-Target tracking extended Kalman filter algorithm is the main function of the document: kal_demo.m approximate mesh filter paper is the main function: bayes_demo.m approximate mesh filter into the grid method is: in order to target the location of a moment as the central to carry out the division of the grid, each grid size of 1, Platform: |
Size: 94208 |
Author:王明 |
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Description: 基于“当前”统计模型的模糊自适应跟踪算法
我存的一篇论文，拿来与大家共享-Current statistical model needs to pre-define the value of maximum accelerations of maneuvering targets.So it
may be difficult to meet all maneuvering conditions.The Fuzzy inference combined with Current statistical model is
proposed to cope with this problem.Given the error and change of error in the last prediction,fuzzy system on-line
determines the magnitude of maximum acceleration to adapt to different target maneuvers.Furthermore,in tracking problem
many measurement equations are non-linear.Unscented Kalman filter is applied instead of extended Kalman filter.The
Monte Carlo simulation results show that this method outperforms the conventional tracking algorithm based on current
statistical model in both tracking accuracy and convergence rate.
Platform: |
Size: 80896 |
Author:dailu |
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Description: Bearing Only Tracking在多场景下的仿真程序 是本人在国外读书时研究项目中的导师和同事等共同开发 使用EKF(扩展KALMAN Filter)与PDA联合算法对target进行定位追踪-Bearing Only Tracking in a multi-scenario is the simulation program to study abroad when I research projects, such as mentors and colleagues to develop the use of EKF (extended KALMAN Filter) algorithm combined with the PDA to locate target tracking Platform: |
Size: 9216 |
Author:chris |
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Description: ua University, in 2002 publi
this document, including the Mont
A program of curve fitting based
Bayesian Filter. Bayesian (Bayesi
a target tracking system MATLAB s
cubic spline curve fitting This i
book is widely used in engineerin
this study is extended Kalman Fil
particle filter algorithm code, t
use AR model for time series pred
principal component analysis algo
HMM, C language, it is important
spectrum analysis techniques to s
digital watermarking technology p
mean-shift method for the example
chaotic sequence of phase space r
Serializing objects using CArchiv
C compile some of the most optimi
The source code of FFT,is a good
Mailto US | Studio | Copyright Complaints Platform: |
Size: 712704 |
Author:wibisono |
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Description: 程序可以实现三维目标的跟踪监控，利用的是扩展卡尔曼滤波器EKF算法-Program can achieve the three-dimensional target tracking control, the use of the extended Kalman filter EKF algorithm Platform: |
Size: 1024 |
Author:张肖 |
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Description: 本代码主要是势均衡多目标多伯努利滤波器的扩展卡尔曼（EKF）实现，能有效的解决杂波环境下的多目标跟踪，本代码能够正常运行。-
您是不是要找： 本代码主要是食均衡多目标多伯努利滤波器的扩展卡尔曼(EKF)实现,能有效的解决杂波环境下的多目标跟踪,本代码能够正常运行。
This code is mainly balanced multi-objective multi-bonuli potential extended Kalman filter (EKF) to achieve, can effectively solve the multi-target tracking in clutter, the code to run properly. Platform: |
Size: 112640 |
Author:王战 |
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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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Description: 扩展kalman滤波在目标跟踪中的应用。给出了一种基于距离的目标跟踪算法Matlab源程序，并附上详细注释，程序调试通过，可直接运行。-Extended kalman filter in target tracking. Gives a target distance based tracking algorithm Matlab source, along with detailed notes, debugging through, it can be directly run. Platform: |
Size: 1024 |
Author:包军海 |
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Description: 粒子滤波理论是近年来跟踪领域的热门研究课题。在该领域，传统的卡尔曼（Kalman）滤波器是非常经典的运动目标跟踪工具。然而经典亦有其弊端，卡尔曼滤波对于非线性及非高斯环境下的工作能力相当无力。为解决这一问题，本文提出了一种基于粒子滤波的目标跟踪方法。其核心为以粒子(一种随机样本，携带权值)来表示后验概率密度，从而得到基于物理模型的近似最优数值解，其优点在于能在追踪的过程中实现更高的精度和更快的收敛速度等。粒子滤波通过加权计算这些带有权重的随机样本来得到目标的近似的运动状态，因此对于非高斯和非线性的环境有着较强的鲁棒性（对特性或参数扰动的不敏感性）。本文将粒子滤波运用于视频跟踪，与传统卡尔曼滤波器进行对比实验，通过实验结果进一步说明粒子滤波对卡尔曼滤波的优势，因此可以广泛的替代传统卡尔曼滤波器，应用于各个跟踪系统中。-Because in non-linear non-Gaussian environment the performance of traditional Kalman Filter in tracking of moving targets is very poor, the paper uses particle filter to track the moving target. Particle filter does not involve linearization around current estimates but rather represent the desired distributions by discrete random measures, which are composed of weighted particles. It has a high accuracy and a rapid convergence. The theory of target tracking based on particle filter is to use these weighted particles to estimate the states of targets. The simulation results of the target model show that in the non-linear non-Gaussian environment, the performance of the particle filter is better than extended Kalman Filter. Finally, we use the particle filter in video tracking, the experimental results further show that in the non-linear non-Gaussian environment the particle filter has a better tracking performance. Particle filter technology can be widely used for air to air, air-groun Platform: |
Size: 3072 |
Author:黎明 |
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Description: 卡尔曼滤波原理及应用—MATLAB仿真主要介绍数字信号处理中的卡尔曼（Kalman）滤波算法及在相关领域应用。全书共7章。第1章为绪论。第2章介绍MATLAB算法仿真的编程基础。第3章介绍线性Kalman滤波。第4章讨论扩展Kalman滤波，并介绍其在目标跟踪和制导领域的应用和算法仿真。第5章介绍UKF滤波算法，同时也给出其应用领域内的算法仿真实例。第6章介绍了交互多模型Kalman滤波算法。第7章介绍Simulink环境下，如何通过模块库和S函数构建Kalman滤波器，并给出了系统是线性和非线性两种情况的滤波器设计方法(Principle and Application of Kalman Filter - MATLAB simulation mainly introduces Kalman filtering algorithm in digital signal processing and its application in related fields. The book consists of 7 chapters. The first chapter is the introduction. The second chapter introduces the programming foundation of MATLAB algorithm simulation. The third chapter introduces linear Kalman filtering. Chapter 4 discusses the extended Kalman filter, and introduces its application and algorithm simulation in the field of target tracking and guidance. The fifth chapter introduces the UKF filtering algorithm, and gives an example of algorithm simulation in its application area. The sixth chapter introduces the interacting multiple model Kalman filtering algorithm. Chapter 7 introduces how to construct Kalman filter by module library and S-function in Simulink environment, and gives the design method of Kalman filter for both linear and nonlinear systems.) Platform: |
Size: 13403136 |
Author:jianjian健健 |
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Description: Over-the-horizon radar (OTHR) exploits skywave propagation
of high-frequency signals to detect and track targets,
which are different from the conventional radar. It
has received wide attention because of its wide area surveillance,
long detection range, strong anti-stealth ability,
the capability of the long early warning time, and so on.
In OTHR, a significant problem is the effect of multipath
propagation, which causes multiple detections via
different propagation paths for a target with missed detections
and false alarms at the receiver [1–6]. Nevertheless,
the conventional tracking algorithms, such as
probabilistic data association (PDA) [7–9], presume that
a single-measurement per target, it may consider the
other measurements of the same target as clutter, and
multiple tracks are produced when a single target is
present. Therefore, these methods cannot effectively
solve the multipath propagation problem.(Conventional multitarget tracking systems presume that each target can produce at most one measurement
per scan. Due to the multiple ionospheric propagation paths in over-the-horizon radar (OTHR), this assumption is
not valid. To solve this problem, this paper proposes a novel tracking algorithm based on the theory of finite set
statistics (FISST) called the multipath probability hypothesis density (MP-PHD) filter in cluttered environments.
First, the FISST is used to derive the update equation, and then Gaussian mixture (GM) is introduced to derive
the closed-form solution of the MP-PHD filter. Moreover, the extended Kalman filter (EKF) is presented to deal
with the nonlinear problem of the measurement model in OTHR. Eventually, the simulation results are provided
to demonstrate the effectiveness of the proposed filter.) Platform: |
Size: 18432 |
Author:ioeyoyo |
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Description: 本书以 Kalman 滤波器为主要介绍对象，包含基本原理、推导方 法及其在跟踪系统中的应用，同时配套 MATLAB 源程序。具体内容 包括 Kalman 滤波器、扩展 Kalman 滤波器、不敏 Kalman 滤波器及其 在 RFID 系统的跟踪应用研究。 本书凝练了作者二十余年来对 Kalman 滤波器基础理论及在目标 跟踪应用的研究成果，具体内容包括：根据目标运动特征进行自调整 参数的“自适应动力学模型”、不敏变换的性能分析、RFID 跟踪系 统的测量方程及其仿真平台等。 本书可作为自动化、电子信息、计算机应用、控制科学与工程、 信号处理、导航与制导等相关专业高年级本科生和研究生的教材，也 可供相关领域的工程技术人员和研究人员参考。(This book takes Kalman filter as the main introduction object, including the basic principle, derivation method and its application in the tracking system, and matching MATLAB source program. The specific contents include Kalman filter, extended Kalman filter, insensitive Kalman filter and its tracking application in RFID system. This book summarizes the author's research achievements on the basic theory of Kalman filter and its application in target tracking for more than twenty years.) Platform: |
Size: 3828736 |
Author:#@ |
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