一种新的基于机动检测的机动目标跟踪算法

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。     

一类新型的机动目标跟踪算法

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。     

一种基于二级变维Kalman滤波的跟踪算法

Kalman滤波器是一种高速的目标跟踪器.针对不同阶数的Kalman滤波器具有不同的跟踪能力与跟踪效率之间存在的矛盾,设计了一种自适应Kalman滤波算法.该算法使用两级滤波器,根据目标机动性的变化,适当的调整滤波器的阶数,使跟踪结果快速收敛,很好地解决了矛盾.通过对仿真结果分析表明,算法具有可靠、计算简便、快速等特点,模型滤波精度较高,并可实现实时跟踪预测,具有一定的理论价值和实用价值.     

基于自适应SRCKF的空间非合作目标实时轨道确定

针对天基测角对非合作目标跟踪定轨的动力学模型简化误差问题,提出一种基于非线性预测滤波和SRCKF(Square Root Cubature Kalman Filter,平方根容积Kalman滤波)的自适应滤波方法.采用考虑地球J2摄动影响的轨道动力学模型作为状态方程,在跟踪滤波过程中,用NPF(Nonlinear Predictive Filter,非线性预测滤波)对动力学模型进行实时修正,利用SRCKF对修正后的动力学模型进行状态估计.将该方法应用于高轨航天器对非合作低轨目标的实时测角定轨任务中,进行数字仿真,仿真结果证明,该方法相比传统的滤波方法具有更高的精度、更强的鲁棒性和稳定性.     

一种改进的高超声速滑翔目标跟踪方法

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。     

UKF方法及其在方位跟踪问题中的应用

采用UKF(Unscented Kalman Filter)方法处理了平面内地面站对目标的方位跟踪的估计问题。目标的位置和速度由选定的高斯分布采样点来近似，在每个更新过程中，采样点随着状态方程传播并随着非线性测量方程变换，由此不但得到目标位置和速度的均值及较高的计算精度，而且避免了对非线性方程的线性化过程。仿真结果表明，UKF方法比传统的扩展卡尔曼滤波(EKF)算法有更高的估计精度，并能有效地克服非线性严重时，方位跟踪问题中很容易出现的滤波发散问题。     

H∞滤波算法及其在GPS/SINS组合导航系统中的应用

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     

H_∞滤波算法及其在GPS/SINS组合导航系统中的应用

 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。     

在线学习的循环自适应机动目标跟踪算法

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。     

基于自适应Kalman滤波的导引头随动系统设计

主要研究导引头随动系统中探测器信号处理延迟的影响及其补偿控制算法。提出了一种自适应Kalman滤波延迟补偿方案,利用Kalman滤波的预测能力得到当前时刻视线角的估计值,进而得到此时的跟踪误差的估计值,取代被延迟的探测器输出进行闭环控制。考虑到导引头探测器的低更新频率、非等间隔量测等工程特点,又对上述滤波算法进行了一系列改进。仿真表明方法可以明显提高导引头在弹体扰动情况下的跟踪精度。     

An Adaptive Two-Dimensional Kalman Tracking Filter

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.     

Extended solution to multiple maneuvering target tracking

An improved algorithm for tracking multiple maneuvering targets is presented. This approach is implemented with an approximate adaptive filter consisting of the one-step conditional maximum-likelihood technique together with the extended Kalman filter and an adaptive maneuvering compensator. In order to avoid the extra computational burden of considering events with negligible probability, a validation matrix is defined in the tracking structure. With this approach, data-association and target maneuvering problems can be solved simultaneously. Detailed Monte Carlo simulations of the algorithm for many tracking situations are described. Computer simulation results indicate that this approach successfully tracks multiple maneuvering targets over a wide range of conditions     

一种基于三维到达角的无偏伪线性卡尔曼滤波

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。     

Reduced state estimators for consistent tracking of maneuvering targets

Linear Kalman filters, using fewer states than required to completely specify target maneuvers, are commonly used to track maneuvering targets. Such reduced state Kalman filters have also been used as component filters of interacting multiple model (IMM) estimators. These reduced state Kalman filters rely on white plant noise to compensate for not knowing the maneuver - they are not necessarily optimal reduced state estimators nor are they necessarily consistent. To be consistent, the state estimation and innovation covariances must include the actual errors during a maneuver. Blair and Bar-Shalom have shown an example where a linear Kalman filter used as an inconsistent reduced state estimator paradoxically yields worse errors with multisensor tracking than with single sensor tracking. We provide examples showing multiple facets of Kalman filter and IMM inconsistency when tracking maneuvering targets with single and multiple sensors. An optimal reduced state estimator derived in previous work resolves the consistency issues of linear Kalman filters and IMM estimators.     

A Decision?Directed Adaptive Tracker

In the design of a tracking filter for air traffic control (ATC) applications, a maneuvering aircraft can be modelled by a linear system with random noise accelerations. A Kalman filter tracker, designed on the basis of a variance chosen according to the distribution of the potential maneuver accelerations, will maintain track during maneuvers and provide some improvement in position accuracy. However, during those portions of the flight path where the aircraft is not maneuvering, the tracking accuracy will not be as good as if no acceleration noise had been allowed in the tracking filter. In this paper, statistical decision theory is used to derive an optimal test for detecting the aircraft maneuver; a more practical suboptimal test is then deduced from the optimal test. As long as no maneuver is declared, a simpler filter, based on a constant-velocity model, is used to track the aircraft. When a maneuver is detected, the tracker is reinitialized using stored data, up-dated to the present time, and then normal tracking is resumed as new data arrives. In essence, the tracker performs on the basis of a piecewise linear model in which the breakpoints are defined on-line using the maneuver detector. Simulation results show that there is a significant improvement in tracking capability using the decision-directed adaptive tracker.     

A Kalman Filter Based Tracking Scheme with Input Estimation

Beginning with the derivation of a least squares estimator that yields an estimate of the acceleration input vector, this paper first develops a detector for sensing target maneuvers and then develops the combination of the estimator, detector, and a "simple" Kalman filter to form a tracker for maneuvering targets. Finally, some simulation results are presented. A relationship between the actual residuals, assuming target maneuvers, and the theoretical residuals of the "simple" Kalman filter that assumes no maneuvers, is first formulated. The estimator then computes a constant acceleration input vector that best fits that relationship. The result is a least squares estimator of the input vector which can be used to update the "simple" Kalman filter. Since typical targets spend considerable periods of time in the constant course and speed mode, a detector is used to guard against automatic updating of the "simple" Kalman filter. A maneuver is declared, and updating performed, only if the norm of the estimated input vector exceeds a threshold. The tracking sclheme is easy to implement and its capability is illustrated in three tracking examples.     

A Kalman Tracker with a Simple Input Estimator

Two Kalman filter based schemes are proposed for tracking maneuvering targets. Both schemes use least squares to estimate a target's acceleration input vector and to update the tracker by this estimate. The first scheme is simpler and by an approximation to its input estimator the computation can be considerably reduced with insignificant performance degradation. The second scheme requires two Kalman filters and hence is more complex. However, since one of its two filters assumes input noise, it may outperform the first scheme when input noise is indeed present. A detector that compares the weighted norm of the estimated input vector to a threshold is used in each scheme. Its function is to guard against false updating of the trackers and to keep the error covariance small during constant velocity tracks. Simulation results for various target profiles are included. They show that in terms of tracking performance, both schemes are comparable. However, because of its computation simplicity, the first scheme is far superior.     

机动目标“当前”统计模型与自适应跟踪算法

本文提出机动目标“当前”统计模型的概念并建议用修正的瑞利-马尔科夫过程描述目标随机加速机动的统计特性。文中指出了在机动目标运动模型中状态（机动加速度）估值与状态噪声之间的内在联系。在此基础上提出了具有机动加速度均值及方差自适应的卡尔曼滤波算法。对一维和三维的情形进行了计算机模拟。计算结果表明,在仅对目标位置进行观测的情况下,这类自适应估值算法无论对高度机动或无机动的目标均可绘出较好的位置、速度及加速度估值。