机载雷达目标跟踪与航迹信息提取

针对机动目标跟踪中航迹信息提取精度不高的问题,提出一种ECEF坐标系下基于交互多模型的多机协同跟踪算法。首先,各载机以ECEF坐标系为融合中心对目标量测进行无偏转换处理,以有效减小量测转换误差对目标跟踪的影响;然后,利用交互多模型的方法对目标进行融合跟踪,以进一步提高目标机动时的跟踪精度;最后,通过二次滤波的方法,来有效实现目标航迹信息的精确提取。仿真结果表明,该算法可较好地提高目标机动时的跟踪精度和航迹信息提取精度。     

基于角速度估计自适应的IMM目标跟踪算法

为了解决目标强机动时目标跟踪算法模型集不匹配的问题,提出了一种基于角速度估计的自适应交互式多模型算法。通过对角速度的估计,在目标的不同运动模式下选取最优模型集,角速度估计精度高时,通过角速度估计值构造模型集,减小模型间竞争;角速度估计精度低时,采用标准IMM算法的模型集,提高模型集的覆盖范围,从而提高跟踪精度。仿真结果表明该方法能够明显提升目标跟踪性能,对强机动目标的跟踪效果尤其显著。     

一种混合的多机动目标跟踪算法研究

提出了一种混合的多机动目标跟踪算法:交互多模型模糊联合概率数据关联算法(IMM-FJPDA),该算法将交互多模型算法(IMM)和模糊联合概率数据关联算法(FJPDA)相结合,它克服了IMM-JPDA算法计算量大和IMM-FDA算法在强杂波环境中跟踪精度差的问题.给出了基于模糊C均值(FCM)算法的多机动目标跟踪步骤.仿真结果表明IMM-FJPDA算法跟踪精度与IMM-JPDA算法相当,但计算量明显减小,提高了跟踪实时性.     

基于STF的Jerk模型自适应机动目标跟踪算法

在Jerk模型的基础上,提出了一种新的机动目标自适应跟踪算法STF-Jerk。该算法通过引入强跟踪滤波器(STF)的渐消因子,实时调节滤波器增益,增强了系统对突发机动的自适应跟踪能力,从而很好地改善了Jerk模型在跟踪机动目标时的跟踪精度。仿真结果表明,提出的STF-Jerk自适应跟踪算法显著提高了Jerk模型自适应算法的机动目标跟踪性能。     

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。     

基于人工神经网络的多模型目标跟踪算法

针对在目标跟踪中单模型跟踪算法难以应对目标运动形式的变化,而多模型跟踪算法存在结构固定、跟踪精度被非匹配模型削弱且模型切换缓慢的矛盾,文章提出了一种基于人工神经网络的多模型目标跟踪算法。通过分析目标几种基本运动模式的轨迹特点,归纳出目标运动轨迹的特征向量。利用训练好的BP神经网络对滑窗里的轨迹段进行运动模型识别,按结果进行跟踪模型切换,达到使跟踪算法实时适应目标运动状态的目的。仿真结果证明了该算法的有效性,且与传统的多模型算法相比,具有结构更加简单、更强的灵活性和拓展性的特点。     

随机神经网络在机动多目标跟踪中的应用

研究密集多回波环境下的机动多目标跟踪问题。通过对多目标联合概率数据关联方法性能特征的分析,将其归结为一类约束组合优化问题。在此基础上,利用随机神经网络求解组合优化问题的策略,采用改进的增益退火算法,提出了一种新颖的机动多目标快速自适应神经网络跟踪方法。仿真结果表明,该方法不仅具有很高的收敛速度和跟踪精度,而且计算量小,关联效果好。     

改进的基于隐马尔可夫模型的自适应IMM算法

针对机动目标跟踪中交互式多模型算法(IMM)的马尔可夫转移概率矩阵固定不变造成跟踪精度降低的问题,在已有的基于隐马尔科夫模型(HMM)的自适应IMM算法的基础上,对隐马尔可夫链的长度和Baum-Welch算法迭代次数的2个参数对该算法跟踪性能的影响,进行了深入研究分析,进一步明确了这2个参数选择的依据;并针对该算法在目标机动转换时峰值误差增大的问题,给出了2种修正方法,从而提出了改进的基于HMM的自适应IMM算法。最后,通过仿真分析了算法的参数和修正方法对跟踪性能的影响,并与传统IMM算法进行对比,证明了文章提出算法的有效性。     

基于期望系统噪声模型的自适应机动目标跟踪

针对防空作战中的机动目标跟踪问题,分析了使用IMM算法跟踪目标所存在的缺陷,提出了一种基于期望系统噪声模型的自适应机动目标跟踪算法（MIMM）,改变IMM算法使用固定模型集合的方法,IMM算法中作用权重较大的只是少部分更接近于实际系统的模型,通过对部分系统噪声模型进行自适应辨识,计算出最接近于系统实际噪声水平的模型——期望系统噪声模型（ESNM）。这些模型作为IMM多模型集的子集,是在一定模型框架内所寻求出的一个（或多个）最优模型（集）。仿真结果证明了MIMM算法能够更好地描述目标机动,达到更理想的跟踪性能,其跟踪精度优于使用固定模型集的IMM算法。     

利用跟踪-微分器构造机动目标估计模型

在分析目标运动特点的基础上,提出了一种新的基于跟踪-微分器的机动目标估计模型。该估计模型与卡尔曼滤波算法相结合,能够在动态过程完全未知的情况下估计出目标运动参数,且估计模型简单,物理意义明确,能够适应于目标的各种机动行为模式。仿真结果表明,具有跟踪-微分器结构的估计模型不但具有很强的适应性,而且能够获得满意的估计精度。     

Minimal submodel-set algorithm for maneuvering target tracking

The variable structure multiple model (VSMM) approach to the maneuvering target tracking problem is considered. A new VSMM design, the minimal submodel-set switching (MSMSS) algorithm for tracking a maneuvering target is presented. The MSMSS algorithm adaptively determines the minimal set of models from the total model set and uses this to perform multiple models (MM) estimation. In addition, an iterative MSMSS algorithm with improved maneuver detection and termination properties is developed. Simulations results demonstrate that, compared with a standard interacting MM (IMM), the proposed algorithms require significantly lower computation while maintaining similar tracking performance. Alternatively, for a computational load similar to IMM, the new algorithms display significantly improved performance.     

Multisensor tracking of a maneuvering target in clutter

An algorithm is presented for tracking a highly maneuvering target using two different sensors, a radar and an infrared sensor, assumed to operate in a cluttered environment. The nonparametric probabilist data association filter (PDAF) has been adapted for the multisensor (MS) case, yielding the MSPDAF. To accommodate the fact that the target can be highly maneuvering, the interacting multiple model (IMM) approach is used. The results of single-model-based filters and of the IMM/MSPDAF algorithm with two and three models are presented and compared. The IMM has been shown to be able to adapt itself to the type of motion exhibited by the target in the presence of heavy clutter. It yielded high accuracy in the absence of acceleration and kept the target in track during the high acceleration periods     

Tracking a 3D maneuvering target with passive sensors

A novel application of the interacting multiple models (IMM) algorithm in which passive infrared sensors are fused for tracking a target maneuvering in three dimensions is discussed. More accurate models of target motion are proposed to improve performance. When the general models are used to describe the maneuvering periods, it is shown that the IMM behaviour is not satisfactory, in that the innovations associated with the different models do not discriminate between the corresponding target maneuvering regimes. The turning of the Markov chain transition matrix, i.e., a priori information, is then crucial to obtaining the correct ordering of the a posteriori regime probabilities. On the contrary, a more satisfactory behavior of the IMM algorithm is obtained by carefully selecting the target motion models in the different regimes     

Modified input estimation technique using pseudoresiduals

An adaptive tracking filter for maneuvering targets is proposed using modified input estimation technique. Pseudoresiduals are defined using measurements and the velocity estimate at the hypothesized maneuver onset time. With the pseudoresiduals and a new target model representing transitions of nominal accelerations, a new input estimation method for tracking a maneuvering target is derived. Since the proposed detection technique is more sensitive to maneuvers than previous work, the shorter window length can be employed to detect and compensate target maneuvers. Also shown is that the tracking performance of the proposed filter is similar to that of interacting multiple model method (IMM) with 3 models, while computational loads of our method are drastically reduced     

Maneuvering target tracking using IMM method at high measurementfrequency

The problem of tracking a maneuvering target with a high measurement frequency is considered. The measurement noise is significantly correlated when the measurement frequency is high. A simple decorrelation process is proposed to enhance the interacting multiple model (IMM) algorithm to track a maneuvering target with correlated measurement noise. It is found that the decorrelation process may improve system performance significantly, especially in velocity and acceleration estimations     

A nonlinear IMM algorithm for maneuvering target tracking

In target tracking, the measurement noise is usually assumed to be Gaussian. However, the Gaussian modeling of the noise may not be true. Noise can be non-Gaussian. The non-Gaussian noise arising in a radar system is known as glint noise. The distribution of glint noise is long tailed and will seriously affect the tracking performance. We develop a new algorithm that can effectively track a maneuvering target in the glint environment The algorithm incorporates the nonlinear Masreliez filter into the interactive multiple model (IMM) method. Simulations demonstrate the superiority of the new algorithm     

Interacting acceleration compensation algorithm for trackingmaneuvering targets

The two-stage Kalman estimator has been studied for state estimation in the presence of random bias and applied to the tracking of maneuvering targets by treating the target acceleration as a bias vector. Since the target acceleration is considered a bias, the first stage contains a constant velocity motion model and estimates the target position and velocity, while the second stage estimates the target acceleration when a maneuver is detected, the acceleration estimate is used to correct the estimates of the first stage. The interacting acceleration compensation (IAC) algorithm is proposed to overcome the requirement of explicit maneuver detection of the two-stage estimator. The IAC algorithm is viewed as a two-stage estimator having two acceleration models: the zero acceleration of the constant velocity model and a constant acceleration model. The interacting multiple model (IMM) algorithm is used to compute the acceleration estimates that compensate the estimate of the constant velocity filter. Simulation results indicate the tracking performance of the IAC algorithm approaches that of a comparative IMM algorithm while requiring approximately 50% of the computations