Attitude Determination Via Nonlinear Filtering

The theory of nonlinear filtering has been applied to the problem of the attitude determination of a satellite. Two approximation methods have been studied with a view to practical implementation. Computer simulation results are included.     

Nonlinear Estimation Via Kalman Filtering

The extended Kalman-Bucy filtering algorithm is applied to the problem of estimating the system parameters of a single-channel missile attitude control system. In particular, noisy observations of body angular rate, normal acceleration, and control surface deflec tion are used in the algorithm to obtain estimates of body angular rate, angle of attack, control surface deflection, air density, and missile velocity. Computational results obtained for this problem are presented. These results are evaluated to determine the applicapility of this estimator as a part of an adaptive control scheme. In addition, a general approach to the formulation of practical nonlinear estimation problems is suggested.     

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.     

基于交互式多模型滤波算法的航迹预测

航空器航迹预测是空中交通冲突探测与解脱,以及进离场管理等空中交通管制自动化系统的重要组成部分。基于混杂系统理论,描述航空器在水平面的运动模型,并针对航空器运动的多样性和不确定性提出了利用交互式多模型的航空器航迹预测算法。将算法应用于航空器的航迹预测,并与单一模型的卡尔曼滤波、α-β滤波和α-β-γ滤波的仿真预测结果对比,结果表明算法的有效性。     

混合高斯测量噪声下的多模型滤波

就带有混合高斯测量噪声的离散时间系统 ,提出了一种简化的多模型滤波。理论分析证明该滤波方法用较少的计算量得到了与交互多模型滤波相同的估计性能。为满足应用要求 ,给出了该滤波器的数值鲁棒实现方法。一个关于仅有方位测量的制导例子验证了该滤波算法的有效性。     

自适应滤波技术的研究

应用常规卡尔曼滤波器(KF)要求知道系统精确的数学模型和系统噪声与量测噪声的统计特性,才能获得理想的滤波效果,否则可能产生发散现象。人们越来越倾向于利用自适应滤波(AKF)技术来解决发散的问题。针对AKF技术的研究现状,本文探讨一种结构简单、实时性较强、工程上比较实用的AKF算法。仿真结果表明,这种算法具有较强的自适应性,为一种实用而有效的滤波方法。     

On an Intrusion-Detection Approach Via Adaptive Prediction

We introduce the notion of using adaptive (digital) prediction in an intrusion-detection application, where stimuli due to intruders are transient signals. The basic idea is to use an adaptive digital predictor for removing correlated noise. As such, this approach assists in reducing false (nuisance) alarms in the absence of intruder stimuli. It also yields an improvement in signal-to-noise ratio when intruder stimuli are present, thereby assisting in their detection. The performance of this approach is assessed in terms of a fixed-point arithmetic simulation. Some aspects of a corresponding hardware implementation that involves a commercial microprocessor are also included.     

Adaptive Clutter Filtering Using Autogressive Spectral Estimation

A new adaptive filter to reject clutter is derived using autoregressive spectral analysis techniques. The adaptive filter performs open. Ioop processing, resulting in a shorter transient response, and is therefore suitable for radar waveforms containing only a small number of samples. A number of examples including application to ballistic missile defense are presented to demonstrate the performance capabilities of the new adaptive filter.     

GPS多径信号的自适应滤波估计方法

 研究全球定位系统(GPS)多径信号估计的问题。通过分析自适应滤波器的原理,建立了数字中频信号处理的数学模型,提出一种用自适应滤波实现GPS多径幅度、码相位和载波相位估计的方法。该方法采用不同延迟的伪随机序列对信号进行解扩、解调和累加,得到了作为期望信号的系列自适应滤波相关值。对该方法与其他3种方法进行了理论上的分析比较,得出本方法具有信噪比高、自适应滤波性能好、带有码相位信息和不存在载波模糊度问题等优点。根据各种滤波器算法的特点和本应用的需求,给出了选用递归最小二乘算法实现的方法。通过计算机仿真,验证了提出的方法能够在14 dB的信噪比下,以1个采样间隔的时间延迟分辨率和0.005周的载波相位估计精度估计出GPS L1的多径信号。     

基于变分贝叶斯的自适应鲁棒滤波算法

针对组合导航姿态估计中,观测同时受到野值与时变观测噪声影响的问题,构造一种基于变分贝叶斯的自适应鲁棒滤波算法。该算法可以有效地解决自适应与鲁棒滤波策略的矛盾,利用变分贝叶斯近似估计变换的观测噪声,在变分贝叶斯的滤波框架内,利用Huber滤波鲁棒化方法处理连续野值。在组合导航姿态估计试验中,验证了该算法具有良好的自适应与鲁棒性,并能够保持较高的估计精度。     

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。     

Noncoherent Detection of Split-Phase FSK by Multiple Predetection Filtering

When the cumulative drift in the center frequency of a binary split-phase FSK signal exceeds the peak deviation of the signal, a conventional noncoherent receiver (i.e., one provided with only two IF filters) may be unable to achieve the probability of error per bit which the designer desires. This limitation may be overcome if the receiver is provided with a bank of more than two contiguous filters (each followed by an envelope detector) tospan the total IF band the instantaneous IF signal might occupy. It is shown that the probability of error per bit for such a receiver is a function of 1) the ratio F of peak frequency deviation to peak frequency drift, 2) the number M of IF filter/detectors, and 3) the signal-to-noise ratio ? in the output of the filter containing the signal. It is further shown thatfor a given value of F an increase in M reduces the amount of transmitter power the communication system designer must provide to yield a given probability of error per bit.     

改进的Sage-Husa自适应滤波算法在MEMS航姿参考系统中的应用

针对经典Kalman滤波和扩展Kalman滤波融合算法存在的计算量大、精度低、实时性差的缺点,引入了改进的Sage-Husa自适应扩展Kalman滤波算法。该算法对经典扩展Kalman滤波算法进行了自适应改进,并在此基础上利用加权渐消记忆法获取了遗忘因子,并通过预测残差得出了最优解。同时,用调整有偏增益估计的措施来保证系统噪声预测方差矩阵与噪声预测方差矩阵的对称性和正定性,对滤波器发散进行了有效的抑制,减少了算法的计算量。实验结果表明,该算法有效改善了可靠性、精确性及自适应能力。     

单兵导航多传感器信息融合自适应滤波算法

基于足绑式INS的单兵导航系统,通过将惯性导航系统、人体运动学约束、磁强计等多传感器信息进行融合得到准确的单兵导航信息。对于匀速步行时的单兵导航,可采用普通Kalman滤波算法进行多传感器信息融合,但不能满足跑步等激烈运动模式下单兵导航需求。提出衰减记忆新息自适应Kalman滤波算法,可满足多步态模式下的单兵导航多源信息融合的需求,实验结果验证了算法的有效性。     

一种变步长LMS自适应滤波算法研究

为了解决最小均方(LMS)算法的稳定性以及收敛速度和稳态误差之间的矛盾,提出了一种复数型自适应变步长的LMS算法,它的权系数的调整取决于误差曲面在新权值点上的梯度。分析了新算法的收敛特性以及参数选择对算法性能的影响。算法具有较快的收敛速度、鲁棒稳定性且运算小易于实现的特点。计算机仿真的结果与理论分析相一致,证实了算法的收敛性能优于标准的LMS算法并且具有较好的实用性。     

车载组合导航中的自适应滤波技术研究

针对现有的自适应卡尔曼滤波算法结构繁杂,采用了一种自适应卡尔曼滤波的车载SINS/GPS 组合导航算法,并与常规卡尔曼滤波算法作了比较。仿真结果表明,这种算法具有结构简单、实时性好和抗野值等的优点,不失为一种实用而有效的滤波方法。     

自适应滤波算法在SINS/GPS组合导航系统中的应用研究

以SINS/GPS组合导航系统为应用背景,对具有代表性的Sage自适应滤波和渐消卡尔曼滤波进行了研究,分析了这些方法在SINS/GPS组合导航应用中存在的问题,提出了适合工程应用的改进方法。改进的Sage自适应滤波主要对系统状态噪声协方差阵利用状态误差进行估计,更符合SINS/GPS组合导航系统的实际情况,提高了滤波稳定性。改进的渐消卡尔曼滤波采用矩阵因子的形式直接对状态预测协方差阵各分量进行不同程度的调节,使调节更趋合理。此外,改进算法增加了对观测粗差的处理,降低了观测粗差对滤波结果的影响。最后,用实际跑车试验验证了改进方法的有效性。     

Adaptive Arrays for Multiple Simultaneous Desired Signals

Steered beam adaptive arrays for multiple simultaneous desired signals are discussed. It is shown that the performance of a steered beam adaptive array depends upon the range of input signal strengths and the choice of the steering vector. Optimum steering vectors for various input signal strengths are given. All choices of steering vectors are equally effective in the rejection of jammers.     

高超声速滑翔再入飞行器弹道估计的自适应卡尔曼滤波

采用传统状态增广法对高超声速滑翔再入飞行器(HGRV)进行弹道估计,存在模型简化误差过大和过程噪声方差难以构造的问题.依据目标运动特性和模型简化误差定量分析结果,状态方程改进为采用圆形地球模型和拟合大气模型并考虑哥氏力.采用一阶Markov过程描述气动力参数,将过程噪声方差构造为气动力参数方差和机动时间常数的函数,时变气动力参数方差采用“渐消记忆”的统计估计法由气动力参数估计值序列统计获得,而存在跳变的机动时间常数则作为运动模式采用变结构交互多模型法与运动状态一起估计.仿真结果表明,所提算法对位置、速度和气动力参数的估计精度优于传统算法,具有较好的工程实用性、鲁棒性和效费比.