共查询到18条相似文献,搜索用时 140 毫秒
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一种新的基于机动检测的机动目标跟踪算法 总被引:3,自引:0,他引:3
针对Kalman滤波跟踪机动目标发散和目前多数自适应Kalman滤波算法对运动模型适应性不强的问题,提出了一种新的基于机动检测的机动目标跟踪算法,通过实时自适应的改变滤波模型提高对机动目标跟踪精度。对这种方法与Kalman滤波算法进行了计算机仿真比较,结果表明,该方法计算量小,可实时精确地自适应匹配目标的运动模型,可实现对机动目标稳定可靠的跟踪。 相似文献
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H∞滤波算法及其在GPS/SINS组合导航系统中的应用 总被引:15,自引:0,他引:15
在对 H∞ 估计问题进行数学描述的基础上 ,建立了一种 H∞ 次优滤波算法的迭代方程。定性讨论了H∞滤波算法与传统 Kalman滤波器的关系 ,通过在 GPS/SINS组合系统中的实际应用进一步从精度、鲁棒性等性能指标方面对 H∞ 滤波和 Kalman滤波算法进行了比较。仿真结果表明 ,在理想条件下 ,Kalman滤波方法具有较高的精度 ;但是 ,当系统模型和外部干扰统计特性发生变化时 ,H∞ 滤波算法明显具有良好的鲁棒性能 ,同时 ,估计精度也较高 ,有效地克服了 Kalman滤波器存在的局限性 相似文献
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H_∞滤波算法及其在GPS/SINS组合导航系统中的应用 总被引:4,自引:0,他引:4
在对 H∞ 估计问题进行数学描述的基础上,建立了一种 H∞ 次优滤波算法的迭代方程。定性讨论了H∞滤波算法与传统 Kalman滤波器的关系,通过在 GPS/SINS组合系统中的实际应用进一步从精度、鲁棒性等性能指标方面对 H∞ 滤波和 Kalman滤波算法进行了比较。仿真结果表明,在理想条件下,Kalman滤波方法具有较高的精度;但是,当系统模型和外部干扰统计特性发生变化时,H∞ 滤波算法明显具有良好的鲁棒性能,同时,估计精度也较高,有效地克服了 Kalman滤波器存在的局限性。 相似文献
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一类新型的机动目标跟踪算法 总被引:1,自引:1,他引:0
针对机动目标跟踪,提出了基于截断正态概率模型的改进自适应目标跟踪算法,该算法具有结构和计算简单,鲁棒性好的特点,通过仿真结果对比,充分说明了文中所提出的跟踪算法能够较好地弥补传统的Kalman滤波方法在跟踪机动目标时的不足。 相似文献
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联邦滤波器广泛应用于多传感器信息融合领域,联邦滤波中的信息分配原则影响滤波精度.针对联邦Kalman滤波器进行改进,采用基于估计协方差阵奇异值动态确定信息分配系数.对子滤波器进行重置时,采用新的重置方法,保证了子滤波器误差协方差阵的对称性,确保Kalman滤波器的一致收敛稳定性.新的联邦滤波算法允许每个状态分量拥有不同的动态信息分配因子,从而改进了联邦滤波信息融合的精度.设计了SINS/GPS/电子罗盘组合导航系统,仿真结果说明,与传统联邦滤波算法相比,改进的联邦滤波器估计精度得到了提高,可以更好地对SINS误差进行校准,提高系统的精度. 相似文献
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基于超球体平方根无迹Kalman滤波算法的涡扇发动机气路部件故障诊断 总被引:2,自引:1,他引:1
研究了一种超球体平方根无迹Kalman滤波算法用来有效跟踪涡扇发动机气路部件发生渐变性和突变性故障的健康参数.该算法通过超球体单形采样来降低算法的计算量,采用测量残差协方差阵的平方根代替方差阵进行递推运算,提高了算法的计算效率和数值稳定性.分别采用扩展Kalman滤波算法、无迹Kalman滤波算法和超球体平方根无迹Kalman滤波算法对某型涡扇发动机进行仿真,结果表明:超球体平方根无迹Kalman滤波算法的滤波时间减少50%左右,能够实现渐变性和突变性故障中健康参数的准确估计,是一种有效的涡扇发动机气路部件参数估计和故障诊断方法. 相似文献
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针对机动目标难以精确跟踪的问题,提出了一种可在线学习的循环Kalman神经网络跟踪算法。考虑到状态转移矩阵、量测噪声和过程噪声矩阵在机动目标跟踪中难以实时、离线估计,且在实际应用中对应数据集获取成本高,因此使用在线学习的神经网络对其进行实时估计。由于Kalman滤波算法本身是一种循环结构,将简单的全连接层网络与其嵌合,全连接层网络实时输出状态转移矩阵、量测和过程噪声矩阵估计,构成一种广义的循环Kalman神经网络,根据网络最终输出的位置估计进行端到端的在线学习,并且通过理论推导证明了其在线学习的可行性。将提出的循环Kalman神经网络同3种经典机动目标算法进行了仿真对比,结果表明:循环Kalman神经网络跟踪需要很少的先验信息,在最优区域内较之其他3种算法具有最高的跟踪精度和鲁棒性,并且具有效率高、训练成本低以及可扩展性强的特点。 相似文献
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针对临近空间高超声速滑翔目标跟踪问题,提出一种基于反向传播神经网络修正改进迭代扩展卡尔曼滤波(Back Propagation Neural Network-aided Improved Iterative Extended Kalman Filter, BP-IIEKF)的目标轨迹跟踪方法。在雷达站坐标系下建立目标运动模型和量测模型。引入阻尼因子修正IEKF算法中的协方差预测矩阵,并定义算法的代价函数,给出迭代终止条件,保证了算法收敛精度,减小状态的观测更新误差,提高了目标状态估计精度。利用BP神经网络修正滤波结果,补偿系统滤波误差,进一步提高了跟踪精度。仿真结果表明所提算法对高超声速滑翔目标具有更高的跟踪精度。 相似文献
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一类新型机动目标跟踪算法 总被引:1,自引:1,他引:0
阐述了当跟踪非机动目标时,传统的Kalman滤波可以得到很好的跟踪精度。但是当日标机动时,传统的Kalman滤波不能对目标的突然变化做出及时的改正和预测,因此跟踪精度很差,甚至出现丢失目标的情况。文中采用的基于截断正态概率模型的改进自适应目标跟踪算法, 其结构和计算简单,鲁棒性好,较好地解决了使用Kalman滤波带来的不足。 相似文献
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为了解决大场景下基于三维到达角的目标跟踪问题,提出了一种具有无偏性的伪线性卡尔曼滤波。首先,基于三维到达角信息对目标运动模型与量测模型进行建模;之后,对量测模型进行了伪线性化处理,得到了线性形式的目标量测模型。为了解决伪线性卡尔曼滤波存在的有偏性问题,提出了一种结合EKF(extend Kalman filter)的三维伪线性无偏卡尔曼滤波。仿真实验表明,该模型能够对非机动目标与机动目标有效跟踪,对于百公里级别的目标,当角测量误差从0.1°变化到0.5°,算法在仿真时间结束时均能将绝对位置误差降低至10 km以内,且算法的运行速度与EKF为同一个量级,同时兼顾了抗干扰能力、定位跟踪精度、运行效率的要求,能够为大场景下的目标跟踪提供有效方法。 相似文献
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The conventional Kalman tracking filter incurs mean tracking errors in the presence of a pilot-induced target maneuver. Chan,Hu, and Plant proposed a solution to this problem which used themean deviations of the residual innovation sequence to make corrections to the Kalman filter. This algorithm is further developedhere for the case of a one-dimensional Kalman filter, for which an Implementable closed-form recursive relation exists. Simulation results show that the Chan, Hu, and Plant method can accurately detect and correct an acceleration discontinuity under a variety of maneuver models and radar parameters. Also, the inclusion of thislogic into a multiple hypothesis tracking system is briefly outlined. 相似文献
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A continuously adaptive two-dimensional Kalman tracking filter for a low data rate track-while-scan (TWS) operation is introduced which enhances the tracking of maneuvering targets. The track residuals in each coordinate, which are a measure of track quality, are sensed, normalized to unity variance, and then filtered in a single-pole filter. The magnitude Z of the output of this single-pole filter, when it exceeds a threshold Z1 is used to vary the maneuver noise spectral density q in the Kalman filter model in a continuous manner. This has the effect of increasing the tracking filter gains and containing the bias developed by the tracker due to the maneuvering target. The probability of maintaining track, with reasonably sized target gates, is thus increased, The operational characteristic of q versus Z assures that the tracker gains do not change unless there is high confidence that a maneuver is in progress. 相似文献
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A general method of continually restructuring an optimum Bayes-Kalman tracking filter is proposed by conceptualizing a growing tree of filters to maintain optimality on a target exhibiting maneuver variables. This tree concept is then constrained from growth by quantizing the continuously sensed maneuver variables and restricting these to a small value from which an average maneuver is calculated. Kalman filters are calculated and carried in parallel for each quantized variable. This constrained tree of several parallel Kalman filters demands only modest om; puter time, yet provides very good performance. This concept is implemented for a Doppler tracking system and the performance is compared to an extended Kalman filter. Simulation results are presented which show dramatic tracking improvement when using the adaptive tracking filter. 相似文献
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Efficient Approximation of Kalman Filter for Target Tracking 总被引:1,自引:0,他引:1
A Kalman filter in the Cartesian coordinates is described for a maneuvering target when the radar sensor measures range, bearing, and elevation angles in the polar coordinates at high data rates. An approximate gain computation algorithm is developed to determine the filter gains for on-line microprocessor implementation. In this approach, gains are computed for three uncoupled filters and multiplied by a Jacobian transformation determined from the measured target position and orientation. The algorithm is compared with the extended Kalman filter for a typical target trajectory in a naval gun fire control system. The filter gains and the tracking errors for the proposed algorithm are nearly identical to the extended Kalman filter, while the computation requirements are reduced by a factor of four. 相似文献
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在对弹道目标跟踪预警的工程实践中,雷达系统对目标运动的信息处理速度尤为重要,因而,文章选取自适应跟踪模型与卡尔曼滤波相结合的方法解决自由段弹道目标的跟踪问题,并与扩展卡尔曼跟踪算法做了对比分析。仿真显示,2种滤波方式分别与自适应跟踪模型相结合后,卡尔曼滤波和扩展卡尔曼滤波跟踪性能相差不大,但其算法简单、运算时间短,可以较好满足自由段弹道目标跟踪的工程需求。 相似文献
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Ramachandra K.V. Mohan B.R. Geetha B.R. 《IEEE transactions on aerospace and electronic systems》1993,29(1):215-222
A three-state Kalman tracker is described for tracking a moving target, such as an aircraft, making use of the position and rate measurements obtained by a track-white-scan radar sensor which employs pulsed Doppler processing, such as the moving target detector providing unambiguous Doppler data. The steady-state filter parameters have been analytically obtained under the assumption of white noise maneuver capability. The numerical computations of these parameters are in excellent agreement with those obtained from the recursive Kalman filter matrix equations. The solution for the case when only the range measurements are available is obtained as a special case of this model. Graphs of normalized covariances and gains are presented to illustrate how the solution depends on different parameters 相似文献