Description: 多假设跟踪算法（MHT）是一种在数据关联发生冲突时，形成多种假设以延迟做决定的逻辑。与PDA合并多种假设的做法不同，MHT算法把多个假设继续传递，让后续的观测数据解决这种不确定性。举个例子，PDA对所有假设以对应的概率进行加权平均，然后再对航迹进行更新。因此，如果有10个假设，PDA会将这10个假设有效的合并只留下一个假设。而另一方面，MHT却是保持这10个假设的子集并延迟决定，这样可以利用之后的观测数据解决当前扫描帧的不确定性问题。 -Multiple Hypothesis Tracking (MHT) is a kind of data association in the event of a conflict, the formation of a variety of assumptions in order to delay a decision logic. PDA combined with the practice of a variety of different assumptions, MHT algorithm is to pass on to a number of assumptions, so that follow-up observations to resolve this uncertainty. For example, PDA for all assumptions to the corresponding probability-weighted average, and then update the right track. Therefore, if there are 10 assumptions, PDA will be assumed that an effective merger of 10, leaving only a hypothesis. On the other hand, MHT was to keep this a subset of 10 hypothetical and delay the decision, so that after the observational data can be used to resolve the current scan frame uncertainties. Platform: |
Size: 129024 |
Author:haiser |
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Description: Implementation of the Murty algorithm to obtain the best K assignments. Includes the implementation of the Jonker-Volgenant algorithm.
Usual applications are multiple target tracking algorithms, Joint Probabilistic Data Association (JPDA), Multiple Hypothesis Tracking (MHT), Global Nearest Neighbours (GNN), etc.
Based on the original code by Jonker-Volgenant, and the Murty Matlab implementation by Eric Trautmann
Can be used both from Java or Matlab.
Time for a 100x100 assignment problem, with k=20:
• Matlab implementation: 54.5 sec
• Java implementation: 1 sec
-Implementation of the Murty algorithm to obtain the best K assignments. Includes the implementation of the Jonker-Volgenant algorithm.
Usual applications are multiple target tracking algorithms, Joint Probabilistic Data Association (JPDA), Multiple Hypothesis Tracking (MHT), Global Nearest Neighbours (GNN), etc.
Based on the original code by Jonker-Volgenant, and the Murty Matlab implementation by Eric Trautmann
Can be used both from Java or Matlab.
Time for a 100x100 assignment problem, with k=20:
• Matlab implementation: 54.5 sec
• Java implementation: 1 sec
Platform: |
Size: 27648 |
Author:hansen |
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Description: An effective approach to the detection and tracking of
small moving targets with low contrast is proposed-The detection and tracking of small moving targets in low
signal-to-noise ratio and cluttered environments is a very
important problem in surveillance and target tracking [l].
In the past two decades, extensive research has been
carried out to solve the problem, including Kalman filter,
probabilistic data association, multiple hypothesis testing
121 and etc. Platform: |
Size: 600064 |
Author:蒋星星 |
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Description: 程序对多目标跟踪进行了仿真，运用的是多假设模型-Procedures for multi-target tracking simulation, the use of multiple hypothesis model Platform: |
Size: 28672 |
Author:小胡 |
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Description: 针对杂波环境下的多个机动目标跟踪问题, 本文将多模型概率假设密度 (Multiple-model probability hypothesis
density, MM-PHD) 滤波器和平滑算法相结合, 提出了 MM-PHD 前向 – 后向平滑器. 为了避免引入复杂的随机有限集
(Random finite set, RFS) 理论, 本文根据 PHD 的物理空间 (Physical space) 描述法推导得到了 MM-PHD 平滑器的后向更
新公式. 由于 MM-PHD 前向–后向平滑器的递推公式中包含有多个积分-By integrating the multiple-model probability hypothesis density (MM-PHD) filter with the smoothing al-
gorithms, an MM-PHD forward-backward smoother is proposed in this paper for tracking multiple maneuvering targets
in clutter. To avoid use of complex random finite set (RFS) theory, the backward updated equation of the MM-PHD
smoother can be derived according to the physical-space explanation of the PHD Platform: |
Size: 252928 |
Author:正东 |
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Description: MHT(多假设跟踪)是一种基于多个扫描周期量测, 进行数据互联的技术, 理论上是解决数
据关联的最优方法。文中重点阐述多假设跟踪算法中数据聚簇、假设生成及假设概率计算、假设约简与
剪枝等环节。从工程角度出发, 采用K best最优假设和N scan 回溯剪枝以提高算法效率和实用-MHT (multiple hypothesis tracking) is a kind of multiple scan cycle measurement based, data interconnection technology, theory is to solve the number
According to the optimal method of association. This paper focuses on multiple hypothesis tracking algorithm in data clustering, hypothesis generation and hypothesis probability calculation, assuming that reduction and
The pruning process. From the engineering point of view, using K best optimal hypothesis and N scan backtracking pruning algorithm to improve the efficiency Platform: |
Size: 269312 |
Author:张成宝 |
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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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Search - multiple hypothesis tracking