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Description: 工学博士学位论文
目前，扩展卡尔曼滤波是研究初始对准和惯性/GPS组合导航问题的一个主要手段。
但初始对准和惯性/GPS组合导航问题本质上是非线性的，对模型进行线性化的扩展卡
尔曼滤波在一定程度上影响了系统的性能。近年来，直接使用非线性模型的
UKF(Unscented Kalman Filtering, UKF)和粒子滤波，正在逐渐成为研究非线性估计问题
的热点和有效方法。
本文研究了UKF和粒子滤波两种非线性滤波方法，并将其应用于非线性静基座对
准和惯性/GPS组合导航，系统地研究了初始对准和惯性/GPS组合导航中各种非线性项-Engineering PhD thesis Currently, EKF is the initial alignment study and inertial/GPS navigation of a major means. However, initial alignment and inertial/GPS navigation on the nature of the problem is nonlinear. on the model of linear expansion of the Kalman filter certain extent affected the performance of the system. In recent years, direct use of the non-linear model (UKF Unscented Kalman Filtering. UKF) and the particle filter, is gradually becoming nonlinear estimation of the hot and effective method. This paper studies the UKF and particle filter both nonlinear filtering method, will be applied to nonlinear static Base Alignment and inertial/GPS navigation, systematic study of initial alignment and inertial/GPS navigation various nonlinear term
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Author: daniel |
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Description: UKF滤波方法，改方法比传统的卡尔曼滤波相比，主要是解决非线性问题-UKF filtering method, change methods than the traditional Kalman Filter, the key is to resolve the problem of nonlinear
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Size: 5120 |
Author: 孙希希 |
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Description: Random Set/Point Process in Multi-Target Tracking.
关于PHD滤波实现多目标跟踪很好的课件。-Random Set/Point Process in Multi-Target Tracking. On PHD filter multi-target tracking good courseware.
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Size: 657408 |
Author: 周奇 |
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Description: 自己写的UKF滤波程序，使用2n+1Sigma点采样-UKF filter written by myself, using 2n+1 Sigma-point sampling
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Size: 3072 |
Author: ZHUANG |
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Description: PHD Filter for Estimating the multitarget tracking
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Size: 316416 |
Author: N |
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Description: PHD Filter for Estimating the multitarget tracking
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Size: 121856 |
Author: N |
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Description: Probability Hypothesis Density filter versus Multiple
Hypothesis Tracking
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Size: 199680 |
Author: N |
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Description: Probability hypothesis density filter for MTT tracking
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Size: 3072 |
Author: pedram poshtiban |
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Description: PHD滤波的粒子滤波实现,采用BOT测量模型-particle filter implementation of PHD fitler with BOT measurement model
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Size: 2048 |
Author: zhaolingling |
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Description: ＰＨＤ滤波的应用文章，包括了２００５以后的一些较重要文献-Applications for the PHD filter, including some important literatures after 2005.
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Size: 8850432 |
Author: zhaolingling |
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Description: 粒子滤波与PHD多目标跟踪比较，三目标分别进行粒子滤波和marginalPHD滤波跟踪比较结果。-PHD particle filter multi-target tracking compared with the three goals were to filter tracking particle filter and marginalPHD comparison.
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Size: 13312 |
Author: 钟跃民 |
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Description: Estimating unknown clutter intensity for PHD filter
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Size: 2204672 |
Author: wang |
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Description: 用于多目标追踪的高斯混合概率假设滤波的文档-doc for gm-phd
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Size: 4123648 |
Author: chao |
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Description: 用于多目标追踪的概率假设密度粒子滤波的程序-PHD Filter for MTT
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Size: 49152 |
Author: chao |
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Description: 用PHD滤波的方法实现多目标跟踪，及对PHD滤波性能的检测。-PHD filter method using multi-target tracking, and performance testing for the PHD filter.
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Size: 11264 |
Author: |
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Description: 基于PHD滤波器的多目标跟踪技术的论文，本文针对密集杂波的情形, 提出一种有效的杂波滤除方法-Multi target tracking technology of PHD filter based on paper
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Size: 701440 |
Author: 段红岩 |
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Description: 有关随机有限集一阶矩PHD滤波的matlab实现的程序。-First moment PHD filter matlab implementation procedures for random finite set.
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Size: 7168 |
Author: 谢乐行 |
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Description: 采用PHD filter进行视觉多目标跟踪的两篇顶级论文-Two important papers about using PHD filter to track multiple object in video
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Author: 且志安 |
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Description: The Gaussian mixture probability hypothesis density filter (GM-PHD Filter) was proposed recently for jointly estimating the time-varying number of targets and their states a noisy sequence of sets of measurements. - The Gaussian mixture probability hypothesis density filter (GM-PHD Filter) was proposed recently for jointly estimating the time-varying number of targets and their states a noisy sequence of sets of measurements .
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Size: 1024 |
Author: 石鸿逸 |
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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.)
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Author: ioeyoyo |
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