Description: 强机动目标跟踪的基本模型仿真，匀速，匀加速模型等，蒙特卡诺滤波的实现等。-Strong maneuvering target tracking simulation of the basic model, uniform, uniform acceleration model and so on, the realization of such蒙特卡诺filtering. Platform: |
Size: 1024 |
Author:琦琦 |
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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: 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: 409600 |
Author:jorgehas |
Hits:
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 |
Hits:
Description: 针对传统α － β 滤波算法不够有效跟踪机动目标的问题， 详细分析了其内在原因， 提出一种改进的α － β 滤波算法。该算法不需要假定目标的机动模型， 而是将目标的机动加速度作为滤波状态直接估计出来， 将估计加速度作为输入控制量引入到传统α － β 滤波器的状态估计方程中进行机动目标的跟踪。然后将它与传统α － β 滤波算法进行比较， 证明了新的算法不仅具有传统算法计算量小的优点， 而且还可以对机动目标进行实时跟踪。仿真结果表明， 新算法在综合性能上明显优于传统算法。(In view of the problem that the traditional alpha beta filtering algorithm is not effective in tracking the maneuvering target, the internal reason is analyzed in detail, and an improved alpha beta filtering algorithm is proposed. In this algorithm, the maneuvering model of the target is not assumed, but the maneuvering acceleration of the target is estimated directly as the filter state, and the estimated acceleration is introduced into the state estimation equation of the traditional alpha beta filter to track the maneuvering target in the state estimation equation of the traditional alpha beta filter. Then compared with the traditional alpha beta filtering algorithm, it is proved that the new algorithm not only has the advantages of small computation in the traditional algorithm, but also can track the maneuvering target in real time. The simulation results show that the new algorithm is superior to the traditional algorithm in terms of comprehensive performance.) Platform: |
Size: 5120 |
Author:王维 |
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Search - acceleration in maneuvering tracking