Posterior Cramer-Rao bounds for multi-target tracking

This study is concerned with multi-target tracking (MTT). The Cramer-Rao lower bound (CRB) is the basic tool for investigating estimation performance. Though basically defined for estimation of deterministic parameters, it has been extended to stochastic ones in a Bayesian setting. In the target tracking area, we have thus to deal with the estimation of the whole trajectory, itself described by a Markovian model. This leads up to the recursive formulation of the posterior CRB (PCRB). The aim of the work presented here is to extend this calculation of the PCRB to MTT under various assumptions.     

Coordinate Conversion and Tracking for Very Long Range Radars

The problem of tracking with very long range radars is studied in this paper. First, the measurement conversion from a radar's r-u-v coordinate system to the Cartesian coordinate system is discussed. Although the nonlinearity of this coordinate transformation appears insignificant based on the evaluation of the bias of the converted measurements, it is shown that this nonlinearity can cause significant covariance inconsistency in the conventionally converted measurements (CM1). Since data association depends critically on filter consistency, this issue is very important. Following this, it is shown that a suitably corrected conversion (CM2) eliminates the inconsistency. Then, initialized with the converted measurements (using CM2), four Cartesian filters are evaluated. It is shown that, among these filters, the converted measurement Kalman filter with second order Taylor expansion (CM2KF) is the only one that is consistent for very long range tracking scenarios. Another two approaches, the range-direction-cosine extended Kalman filter (ruvEKF) and the unscented Kalman filter (UKF) are also evaluated and shown to suffer from consistency problems. However, the CM2KF has the disadvantage of reduced accuracy in the range direction. To fix this problem, a consistency-based modification for the standard extended Kalman filter (E1KF) is proposed. This leads to a new filtering approach, designated as measurement covariance adaptive extended Kalman filter (MCAEKF). For very long range tracking scenarios, the MCAEKF is shown to produce consistent filtering results and be able to avoid the loss of accuracy in the range direction. It is also shown that the MCAEKF meets the posterior Carmer-Rao lower bound for the scenarios considered.     

Efficient recursive state estimator for dynamic systems withoutknowledge of noise covariances

An efficient recursive state estimator for dynamic systems without knowledge of noise covariances is suggested. The basic idea for this estimator is to incorporate the dynamic matrix and the forgetting factor into the least squares (LS) method to remedy the lack of knowledge of noises. We call it the extended forgetting factor recursive least squares (EFRLS) estimator. This estimator is shown to have similar asymptotic properties to a completely specified Kalman filter state estimator. More importantly, the performance of EFRLS greatly exceeds that of existing filtering techniques when the noise variance is misspecified. In addition, EFRLS also performs well when there is cross-correlation between the process and measurement noise streams or temporal dependencies within those streams. Some discussions and a number of simulations are made to provide practical guidance on the choice of an optimal forgetting factor and evaluate the performance of the EFRLS algorithms, which strongly dominates that of the standard forgetting factor recursive least squares (FRLS) and some misspecified Kalman filtering     

Moving target detection via airborne HRR phased array radar

We study moving target detection in the presence of temporally and spatially correlated ground clutter for airborne high range resolution (HRR) phased array radar. We divide the HRR range profiles into large range segments to avoid the range migration problems that occur in the HRR radar data. Since each range segment contains a sequence of HRR range bins, no information is lost due to the division and hence no loss of resolution occurs. We show how to use a vector autoregressive (VAR) filtering technique to suppress the ground clutter. Then a moving target detector based on a generalized likelihood ratio test (GLRT) detection strategy is derived. The detection threshold is determined according to the desired false alarm rate, which is made possible via an asymptotic statistical analysis. After the target Doppler frequency and spatial signature vectors are estimated from the VAR-filtered data as if a target were present, a simple detection variable is computed and compared with the detection threshold to render a decision on the presence of a target. Numerical results are provided to demonstrate the performance of the proposed moving target detection algorithm     

非线性状态方程自校准滤波方法

针对工程实际中遇到的非线性系统状态方程中含未知输入（如环境因素的影响、模型和参数选取不当等）的情况,采用自校准技术,基于秩滤波与无迹Kalman滤波算法提出了一种非线性状态方程自校准滤波方法,并分别讨论了自校准秩滤波(SRF)与自校准无迹Kalman滤波(SUKF)两种情况。大量仿真结果和工程应用表明:与无迹Kalman滤波(UKF)相比,该方法通过对系统状态方程中的未知输入进行自动估计和补偿,改善了系统受未知输入影响下的滤波效果,从算例中可以看到,估计精度至少提高了80%,且计算简单,便于工程应用。      

复合高斯杂波中距离扩展目标的迭代近似GLRT检测器

 研究了结构化的复合高斯杂波(CGC)背景中距离扩展目标自适应检测问题。针对异质杂波背景中的近似广义似然比检验(AGLRT-HTG)检测器应用于CGC背景中时存在一定的信杂比损失问题,结构化的复合高斯杂波采用自回归过程建模,结合近似广义似然比检验(AGLRT)方法和迭代估计思想,提出了CGC背景中距离扩展目标的迭代近似广义似然比检测器(RAGLRT-CGC)。从理论上分析了极限情况下RAGLRT-CGC虚警概率与检测门限关系的解析表达式。仿真结果表明,在CGC背景中,RAGLRT-CGC对不同多主散射点目标具有较好的鲁棒性,并且检测性能明显优于AGLRT-HTG。     

一种飞行器弹性新型控制方法研究

针对新一代飞行器长细比增加和结构复杂等特征带来弹性频率变低、预示精度差的现象,提出了一种新型弹性控制方法,解决了传统弹性滤波方法带来的控制系统稳定裕度严重不足、机动能力弱的难题。该方法基于扩维Kalman滤波算法建立了系统方程和输出方程,并进行了仿真。仿真结果表明,能够实现刚体信息的快速提取,而且系统滞后小,能大幅提升飞行器作战边界和控制精度。     

Performance measure for Markovian switching systems using best-fitting Gaussian distributions

This paper considers the theoretical posterior Cramer-Rao lower bound (PCRLB) for the case of tracking a manoeuvring target with Markovian switching dynamics. In a recent article [2] it was proposed to calculate the PCRLB conditional on the manoeuvre sequence and then determine the bound as a weighted average, giving an unconditional PCRLB. However, we demonstrate that this approach can produce an overly optimistic lower bound, because the sequence of manoeuvres is implicitly assumed known. Motivated by this, we develop a general approach and derive a closed-form estimate of the PCRLB in the case of Markovian switching systems. The basis of the approach is to, at each time step, replace the multi-modal prior target probability density function (pdf) with a best-fitting Gaussian (BFG) approximation. We present a recursive formula for calculating the mean and covariance of this Gaussian distribution, and demonstrate how the covariance increases as a result of the potential manoeuvres. We are then able to calculate the PCRLB for this BFG model using an existing Riccati-like recursion. Because of the BFG approximation, we are no longer guaranteed a bound and so we refer to our estimate as an "error performance measure" rather than a bound. The presented approach is applied both to filtering and smoothing cases. The simulation results indicate a very close agreement between the proposed performance measure and the error performance of an interacting multiple model estimator.     

基于试飞任务单的轨迹仿真问题研究

阐述了基于试飞任务单的试飞轨迹仿真生成软件系统在辅助试飞管理中的重要作用,对该软件开发中所涉及到的一些新的问题和方法进行了研究。建立了带电传操纵系统飞机的数学模型,并针对试飞轨迹仿真生成中的逆问题,提出了一种基准控制带反馈调节的方法。简要介绍了轨迹仿真生成软件与任务单管理数据库软件的接口设计,对仿真结果进行了动态显示处理。     

基于双状态卡方故障检测的组合导航系统

故障检验与隔离是利用联邦滤波实现组合导航系统安全可靠运行的关键技术。针对常规残差卡方检验法对于慢变故障检验效果差,以及状态卡方检验受到初值误差、系统噪声和建模误差影响导致子系统被频繁隔离的问题,提出一种改进的SINS/GPS/ADS/DVL组合导航系统故障检验算法。该算法采用2个状态推进器交替地对滤波数据进行重置,避免状态递推器被未检测出来的故障污染。实验结果表明,改进的算法能够有效提高故障检验的灵敏度,增强组合导航系统的可靠性。     

递推阻尼最小二乘法及其在火控系统中的应用

由于火控系统误差模型未知且稳定性较差,采用Kalman滤波实现滤波时,其滤波特性较差;而此时最小二乘法是实际工程应用中的常用方法,但是容易产生参数爆发现象。文中提出了一种基于多输入-多输出(MI-MO)系统的递推阻尼最小二乘法,将其应用到火控滤波系统中。仿真结果表明,利用递推阻尼最小二乘法能达到递推最小二乘法同样的精度,且能明显抑制参数爆发现象,达到良好的滤波效果。     

非线性非高斯秩滤波方法

基于秩滤波原理,提出一种非线性非高斯秩滤波方法,给出其递推过程.目前常用的非线性滤波方法有无迹Kalman滤波和粒子滤波,无迹Kalman滤波只适用于高斯分布的情况,粒子滤波方法却存在粒子退化及重采样引起的粒子贫化问题.而非线性非高斯秩滤波方法不仅适用于常见的多元t分布、多元极值分布等非高斯分布的非线性滤波,并且计算简单、工作量小,便于工程应用.从仿真算例可以看到,该方法的滤波精度与无迹Kalman滤波和粒子滤波方法相比提高了500%以上.      

A Suboptimum Rank Test for Nonparametric Radar Detection

The optimum rank detector structure, in the Neyman-Pearson sense and under Gaussian noise conditions, is approximated by a suboptimum structure that depends on an adjustable parameter. This new rank detector, which operates on radar video signal, includes other well-known detectors as particular cases. The asymptotic relative efficiency (ARE) of the proposed rank detector is computed, with its maximum value the ARE of the locally optimum rank detector (LORD). The detection probability versus signal-to-noise ratio, and the effects of interfering targets are also calculated by Monte-Carlo simulations for different parameter values.     

Nonlinear filtering: A hybrid approximation scheme

This paper is concerned with nonlinear filtering schemes for systems which allow non-Gaussian noise. Using the most probable trajectory (MPT) approach, a finite-dimensional recursive hybrid filtering scheme is derived. By appropriately selecting a switching process, a linear hybrid system can be obtained that approximates the original nonlinear system. Then the MPT approach is used to obtain the hybrid filtering schemes for the nonlinear systems. Numerical experiments are carried out and reported     

Wavelet filtering of measurement information in unmanned aerial vehicle control system

The problems of filtering the measurement signals in the unmanned aerial vehicle control system are considered. We propose that the methods of filtering based on the wavelet transformation be used.     

飞机主动重心控制系统设计及应用

以我国先进飞机需求牵引为背景,研究了主动重心控制系统方案和设计方法.首先,构建了一种飞机主动重心控制系统结构方案,用于协调纵向重心与气动焦点之间的关系;其次,基于平衡输油系统建立了重心位移模型,提出了最佳重心位置设计准则,在此基础上设计了基于干扰观测器补偿的主动重心控制器,并将其应用到某型飞机超声速巡航中.仿真结果表明,该主动重心控制系统合理地协调了飞机的纵向稳定性和操纵性,并有效地减小了巡航阻力,提高了巡航的效率和航程.     

基于矢量化检测联邦滤波的INS/BDS/地磁组合导航容错方法

组合导航能够将多种类型的导航信息进行结合,实现优势互补,因此成为了目前导航应用领域的主要发展方向。然而,导航信息的增多势必会引入更多的风险源,从而降低导航系统的可靠性。基于联邦滤波的容错方法是目前抑制故障信息影响的主要解决手段,但是现有的故障容错方法普遍采用统一的检测机制,没有根据各个导航子系统的误差传播特性针对性地构建故障检测模型,因此会引起较高的误警率与漏检率。针对上述问题,提出了基于矢量化检测联邦滤波的INS/BDS/地磁组合导航容错方法。通过构建面向INS/BDS/地磁不同导航信息的故障检测函数,能够实现更加准确的矢量化信息分配,从而可以有效避免可用导航信息的损失以及故障导航信息对整体系统的影响。仿真结果表明,提出的方法可以有效隔离不同类型的故障信息,并减小其对无故障导航信息及整体系统的影响,从而提高了组合导航的精度和可靠性。     

Fault Detection in Dynamic Systems via Decision Fusion

Due to the growing demands for system reliability and availability of large amounts of data, efficient fault detection techniques for dynamic systems are desired. In this paper, we consider fault detection in dynamic systems monitored by multiple sensors. Normal and faulty behaviors can be modeled as two hypotheses. Due to communication constraints, it is assumed that sensors can only send binary data to the fusion center. Under the assumption of independent and identically distributed (1ID) observations, we propose a distributed fault detection algorithm, including local detector design and decision fusion rule design, based on state estimation via particle filtering. Illustrative examples are presented to demonstrate the effectiveness of our approach.     

Application of Recursive Estimation and Kalman Filtering to Doppler Tracking

The techniques of recursive estimation and Kalman filtering are applied to the problem of estimation of space vehicle orbits and trajectories using only measurements of the Doppler shifts in radio signals transmitted by or reflected from the target. Two approaches are described. One is a global fit technique in which the parameters of an assumed trajectory model are estimated. The second is a pointwise mapping technique in which the trajectory estimate is extrapolated as the target tracking data is received. The paper provides a tutorial illustration of the direct application of recursive optimization techniques to a class of engineering problems.     

无人飞行器集群仅测距初始相对位姿确定方法研究CSCD

针对全球导航卫星系统(GNSS)拒止环境下无人飞行器集群成员间的相对定位问题,研究了一种基于机载惯性测量单元(IMU)、气压高度计与数据链测距组合的初始相对位姿求解算法。首先,在高度计稳定输出较为精确的高度信息的前提下,将飞行器的三维运动解耦成二维水平运动,给出了三维加速度和角速度、测距信息的水平坐标系投影等效模型。在此基础上,以待求量相对位置和航向角的非线性形式构造了新的待求状态量,并将相对位置和航向角的非线性求解问题转化成了新状态量的线性模型最小二乘求解问题。然后,通过引入递推最小二乘算法(RLS),建立了该相对位姿求解算法的实时输出递推形式,有效降低了机载在线计算的负载。接着,对所提算法进行了可观测性分析,并给出了使系统状态量不可观测的几种相对运动形式。最后,对所提算法进行了数值仿真实验,仿真结果表明,该算法能够有效、快速求解初始相对位姿,位置误差在初始相对距离的10%以内,航向角误差在初始相对角度的1%以内。