非线性系统中多传感器目标跟踪融合算法研究

 研究了在非线性系统中 ,基于转换坐标卡尔曼滤波器的多传感器目标跟踪融合算法。通过分析得出 :在非线性系统的多传感器目标跟踪中 ,基于转换坐标卡尔曼滤波器 ( CMKF)的分布融合估计基本可以重构中心融合估计。仿真实验也证明了此结论。由此可见分布的 CMKFA是非线性系统中较优的分布融合算法     

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.     

利用非线性优化滤波器跟踪图象序列中的小目标

研究一种基于相关技术的自适应跟踪装置。这种跟踪装置使用判别式非线性滤波器，对于噪声、背景和物体失真具有更好的Robust性。计算机模拟显示出了小目标跟踪的试验结果。在对相关面不进行处理的条件下展示了跟踪装置的良好的性能。研究出的算法在众所周知的线形滤波器失效的情况下也能发挥作用。     

混合线性/非线性模型的准高斯Rao-Blackwellized粒子滤波法

 针对混合线性/非线性模型，提出一种新的递推估计滤波算法，称为准高斯Rao-Blackwellized粒子滤波器（Q-GRBPF）。算法采用Rao-Blackwellized思想，将线性状态与非线性状态进行分离，对非线性状态运用准高斯粒子滤波（Q-GPF）算法进行估计，并将其后验分布近似为单个高斯分布，再利用非线性状态的估计值对线性状态进行卡尔曼滤波（KF）估计。将Q-GRBPF应用于目标跟踪的仿真结果表明，与Rao-Blackwellized粒子滤波器（RBPF）相比，Q-GRBPF在保证估计精度的前提下有效降低了计算复杂度，计算时间约为RBPF的58%；与Q-GPF相比，x坐标与y坐标的估计精度分别提升了45％和30％，而计算时间也节省了约30％。     

非线性系统中多传感器目标跟踪融合算法研究

主要阐述了在非线性系统中多传感器目标跟踪的融合算法，提出了基于变换测量卡尔曼滤波器（CMKF）的分布式融合算法，从该理论出发，导出了分布式变换测量卡尔曼滤波算法（DCMKFA）几乎能够重视集中式融合估计，仿真结果证明了这一结论，因此，DCMKFA对于非线性系统中的目标跟踪是一个有效的分布式融合算法。     

利用幅值信息的超视距雷达多路径概率数据互联算法

天波超视距雷达是通过电离层反射实现超视距广域监视的,其地理坐标系下的量测方程存在强非线性,同时由于电离层的不同分层,造成了多路径传播的严重问题,即同时存在多个量测模型。多路径概率数据互联(MPDA)滤波器将坐标配准与概率数据互联相结合,解决了超视距目标跟踪中的多路径传播问题,但在杂波环境下滤波跟踪精度不高。文中提出了一种基于信号幅值特征信息的MPDA算法(A-MPDA),当跟踪单一的、存在4种可能非线性量测的非机动目标时,仿真结果表明所提出的算法比标准MPDA有更好的跟踪精度。     

基于标签多伯努利滤波的衍生目标跟踪算法

针对现有随机有限集(RFS)滤波器在低信噪比环境下对衍生目标跟踪性能严重下降的问题,提出了一种基于Delta扩展标签多伯努利(δ-GLMB)滤波器的改进算法。基于随机集理论和伯努利衍生模型,推导了新的预测方程,并采用了假设裁剪及分组手段和多伯努利近似技术以降低算法的计算量。针对假设增多引起的虚警问题,将多帧平滑思想和算法相结合,利用标签信息对新目标进行回溯处理。仿真结果表明,所提算法能对目标数目进行无偏估计,在低探测概率和强杂波环境下性能明显优于概率假设密度(PHD)算法,计算开销在衍生初始阶段增长快于PHD,目标较分散时低于PHD。     

混合线性/非线性状态空间模型的边缘Rao-Blackwellized粒子滤波法(英文)

本文提出了边缘 Rao-Blackwellized 粒子滤波器(marginal Rao-Blackwellized particle filter, MRBPF)算法,算法融合了 Rao-Blackwellized 粒子滤波器(Rao-Blackwellized particle filter , RBPF)算法和边缘粒子滤波器(marginal particle filter, MPF)算法。算法中状态被分为线形和非线性两部分,分别用 MPF 和卡尔曼滤波器(Kalman Filter)进行估计。地形辅助导航(terrain aided navigation, TAN)的仿真结果表明,与 RBPF 相比,提出算法的非线性状态估计的误差均方根(root mean square error, RMSE)和误差方差分别降低了约 29%和 96%,独立粒子数提高了约80%,获得了更好的收敛结果。分析表明,现有RBPF是MRBPF的一个特例。     

基于混合滤波的无线传感器网络融合跟踪方法

 针对无线传感器网络(WSN)中的多传感器融合目标跟踪，提出一种混合滤波算法，称为无迹混合集中式粒子滤波(UM CPF)。该算法使用了一个混合的粒子传播方案。在使用集中式粒子滤波(CPF)对WSN中的节点测量信息进行融合时，粒子滤波器中的一部分粒子使用从无迹变换(UT)获得的高斯分布作为建议分布进行粒子传播，而剩余的另一部分粒子则简单地使用状态转移先验分布进行粒子传播。WSN中的融合跟踪仿真结果表明，和纯粒子滤波算法CPF相比，在仿真速率相当的情况下，混合滤波算法明显提高了跟踪精度和稳定性。     

BOC(2n,n)信号同步方法研究

分析了BOC信号的特性,BOC信号同步的难点在于其自相关函数的副峰会引起错误捕获和歧异跟踪。现有算法中只有单边带处理法适用于自相关函数峰值较多的信号,但算法中的滤波器实现复杂。对原算法进行改进,省去了复数滤波器,更便于软件实现。采用二维码相位估计来解决歧异跟踪的问题,但次载波的存在使载波频率跟踪范围减小,对跟踪策略进行改进,使跟踪范围满足需求。仿真结果表明,改进算法可在不损失跟踪精度的条件下,有效解决BOC信号的同步问题。     

Unsupervised Tracking of Maneuvering Vehicles

The design of adaptive filters for the tracking of high-performance maneuvering targets is a fundamental problem in real-time surveillance systems. As is well known, a filter which provides heavy smoothing can not accurately track an evasive maneuver, and conversely. Consequently, one is led to the consideration of adaptive methods of filter design. This paper presents an improved self-adaptive filter algorithm for on-line solution of the above problem. Basically, this algorithm utilizes the orthogonality property of the residual time series to force the filter to automatically track the optimal gain levels in a changing environment.     

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。     

增广误差模型算法在目标跟踪中的应用

针对目标机动运行过程中,滤波模型与机动状态模型失配的问题,提出了一种新的增广状态误差滤波模型。不同于现有增广方案,该模型从模型失配所致状态滤波误差的角度出发,将状态估计误差增广为一状态量,通过滤波估计后用其校正原状态量。算法分析表明,该增广滤波模型具有自适应调节多重渐消因子的等效特性,增强了对目标的跟踪能力。基于该增广状态误差滤波模型,给出了滤波算法设计并进行了仿真实验。实验结果表明,基于该模型的滤波算法在对机动目标进行跟踪时具有更强的鲁棒性。     

Multiple-Model Probability Hypothesis Density Filter for Tracking Maneuvering Targets

Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with nonlinear state and/or measurement equations. With multiple targets, representing the full posterior distribution over target states is not practical. The problem becomes even more complicated when the number of targets varies, in which case the dimensionality of the state space itself becomes a discrete random variable. The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment (the PHD) of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems with a varying number of targets. The integral of PHD in any region of the state space gives the expected number of targets in that region. With maneuvering targets, detecting and tracking the changes in the target motion model also become important. The target dynamic model uncertainty can be resolved by assuming multiple models for possible motion modes and then combining the mode-dependent estimates in a manner similar to the one used in the interacting multiple model (IMM) estimator. This paper propose a multiple-model implementation of the PHD filter, which approximates the PHD by a set of weighted random samples propagated over time using sequential Monte Carlo (SMC) methods. The resulting filter can handle nonlinear, non-Gaussian dynamics with uncertain model parameters in multisensor-multitarget tracking scenarios. Simulation results are presented to show the effectiveness of the proposed filter over single-model PHD filters.     

Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets

The probability hypothesis density (PHD) filter has been recognized as a promising tech-nique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter (PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking mul-tiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches.     

Maneuvering Target Tracking with Hypothesis Testing

A new approach is proposed for maneuvering target tracking.Target motion is described by nonlinear models in a sphericalcoordinate system. States of these models are estimated byquantization, multiple hypothesis testing, and a suboptimumdecoding algorithm of information theory. This approach does notrequire linearization of nonlinear models. Hence it is superior toclassical estimation techniques, such as the extended Kalman filter.Simulation results, some of which are presented here, haveshown the superiority of the proposed approach over target trackingwith the extended Kalman filter.     

A Gaussian Mixture PHD Filter for Jump Markov System Models

The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. The PHD filter admits a closed-form solution for a linear Gaussian multi-target model. However, this model is not general enough to accommodate maneuvering targets that switch between several models. In this paper, we generalize the notion of linear jump Markov systems to the multiple target case to accommodate births, deaths, and switching dynamics. We then derive a closed-form solution to the PHD recursion for the proposed linear Gaussian jump Markov multi-target model. Based on this an efficient method for tracking multiple maneuvering targets that switch between a set of linear Gaussian models is developed. An analytic implementation of the PHD filter using statistical linear regression technique is also proposed for targets that switch between a set of nonlinear models. We demonstrate through simulations that the proposed PHD filters are effective in tracking multiple maneuvering targets.     

Novel Nonlinear Filtering & Prediction Method for Maneuvering Target Tracking

A new nonlinear filtering and prediction (NFP) algorithm with input es?imation is proposed for maneuvering target tracking. In the proposed method, the acceleration level is determined by a decision process, where a least squares (LS) estimator plays a major role in detecting target maneuvering within a sliding window. We first illustrate that the optimal solution to minimize the mean squared error (MSE) must consider a trade-off between the bias and error variance. For the application of target tracking, we then derive the MSE of target positions in a closed form by using orthogonal space decompositions. Then we discuss the NFP estimator, and evaluate how well the approach potentially works in the case of a set of given system parameters. Comparing with the traditional unbiased minimum variance filter (UMVF), Kalman filter, and interactive multiple model (IMM) algorithms, numerical results show that the newly proposed NFP method performs comparable or better in all scenarios with significantly less computational requirements.     

Suboptimal filter design with pseudomeasurements for targettracking

A suboptimal Kalman filter design method is presented for the problem of tracking a maneuvering target. The design method is essentially based on linear target dynamics and linear-like structured measurements called pseudomeasurements. The pseudomeasurements are obtained by manipulating the original nonlinear measurements algebraically. The resulting filter has computational advantages over other filters with similar performance. Also, a variant of the Berg model is proposed as a target acceleration model under the assumption of a coordinated turn maneuver. The proposed model is consistent with the underlying assumption. Monte Carlo computer simulation results are included to demonstrate the effectiveness of the proposed suboptimal filter associated with the target acceleration model