Sequence design for IIR inverse filter pulse compression

An infinite impulse response (IIR) inverse filter structure is presented, and compared with the performance of two finite impulse response (FIR) designs. The IIR design is shown to provide better performance and be able to improve further (e.g., -1.4 dB in sidelobe levels per unit delay increase for the length-13 Barker sequence) by increasing delay. The performance parameters of this IIR inverse filter suggest a design criterion for sequences on which the filter operates; that is related to the roots of the Z transform polynomial of the sequence. The sidelobe-optimal sequence derived according to this criterion is shown to provide sharper sidelobe reduction (-6 dB per unit delay increase for the sidelobe optimal length-13 sequence)     

The Effect of Integrator Pole Position on the Performance of an Adaptive Array

The LMS adaptive array requires an integrator in each weight feedback control loop. In practice the integrator is often replaced by a low-pass filter, i.e., by a filter with a single pole at s = - ? (where s is complex frequency). The effect of this pole position on array performance is examined. It is shown that to obtain optimal performance from the array, ? must be less than k?2, where k is the loop gain and ?2 is the thermal noise power per element. When at exceeds k?2, the output signal-to-inter ference-plus-noise ratio from the array is degraded for intermediate values of interference power.     

基于加速度计和角速率陀螺的超小无人直升机姿态控制系统

设计了一种基于加速度计和角速率陀螺的超小型无人直升机姿态控制系统；由卡尔曼滤波算法融合加速度计和陀螺数据，实时估计最优的直升机姿态信号；根据最优的姿态反馈信号，飞行控制系统采用参数自整定的模糊PID控制方法得到舵机控制信号，从而实现了超小型无人直升机的姿态控制。     

Discrete-time min-max tracking

The problem of optimal state estimation of linear discrete-time systems with measured outputs that are corrupted by additive white noise is addressed. Such estimation is often encountered in problems of target tracking where the target dynamics is driven by finite energy signals, whereas the measurement noise is approximated by white noise. The relevant cost function for such tracking problems is the expected value of the standard H/sub /spl infin// performance index, with respect to the measurement noise statistics. The estimator, serving as a tracking filter, tries to minimize the mean-square estimation error, and the exogenous disturbance, which may represent the target maneuvers, tries to maximize this error while being penalized for its energy. The solution, which is obtained by completing the cost function to squares, is shown to satisfy also the matrix version of the maximum principle. The solution is derived in terms of two coupled Riccati difference equations from which the filter gains are derived. In the case where an infinite penalty is imposed on the energy of the exogenous disturbance, the celebrated discrete-time Kalman filter is recovered. A local iterations scheme which is based on linear matrix inequalities is proposed to solve these equations. An illustrative example is given where the velocity of a maneuvering target has to be estimated utilizing noisy measurements of the target position.     

采用一组卡尔曼滤波器检测发动机传感器故障

在发动机全功能数字电子控制系统中,提高传感器工作的可靠性是十分重要的,除了不断对传感器本身的性能加以改进提高外,现在广泛地采用了余度技术。近二十年来对解析余度(Analyt ical Redundancy)进行了广泛的研究,解析余度(AR)方法是基于各状态变量之间存在的解析关系,在系统可观条件下,利用无故障的输出测量值去估计(构造)已故障传感器正常工作状态时的输出信息,从而实现对故障的检测、隔离与重构,保证控制系统具有预定的控制性能。      

航天飞行器制导方法综述

介绍了临界极值的概念 ,在分析反馈策略的基础上 ,给出了一种次优反馈控制律。提出并讨论了 3种数学方法 :次优反馈策略、重复纠正法和连续重复纠正法。其中前两种方法已在航天飞行器的制导系统中得到了验证     

应用卡尔曼滤波的机载雷达跟踪系统

本文论述将滤波理论应用于机载雷达中对单个目标进行距离、速度、方位角和高低角跟踪的多环反馈系统。首先根据目标和天线的相对运动建立控制四坐标跟踪环所需的状态矢量微分方程,然后推导相应的非线性滤波算法。最后给出计算机的模拟结果。计算机模拟的结果清晰地说明采用最佳滤波的系统性能比通常的有很大改善,并且这种瞄准轴坐标系的最佳系统对目标的随机机动是不灵敏的。 本文所讨论的方法和得出的结论可以延用到地面雷达、舰载雷达以及其他有源和无源的跟踪系统。     

An optimal reduced-order stochastic observer-estimator

An optimal reduced-order observer-estimator (filter) is developed which can provide a full-dimensional vector of state estimates for systems where the dimension of the measurement vector is smaller than that of the state vector and none of the measurements are noise free. The reduced-order filter consists of two subfilters each of which provides a subset of the optimal estimate. A two-step L-K transformation is employed to minimize the estimate error variance of each subfilter. The optimal reduced-order filter developed is computationally efficient     

Wide Dynamic Range Preselectors

Crowding of the Military communication spectrum has aggravated the need for preselectors capable of receiving a low-level desired signal in the presence of a high-level adjacent channel signal. Described herein are two novel HF preselection approaches which appear eminently suitable to cope with this problem. Applicable to the frequency range of 2 to 30 Mc/s, one preselector employs parametric mixing and the other exploits the low loss properties of superconductivity. In addition to other advantageous features, both preselectors have demonstrated an outstanding dynamic range capability in the order of 140 dB. Each of the preselectors is evaluated as a conventiona1 filter. In addition, performance data of a high quality military receiver is compared with that obtained when the receiver is preceded by each of two preselectors. Considerable stress is placed on the use of crossmodulation characteristics as a more meaningful and precise measure of preselector performance.     

Optimal FIR filters for time-varying state-space models

An optimal FIR (finite impulse response) filter and smoother is introduced for the time-varying state-space model. The suggested filter has an FIR structure and utilizes finite observation. It is shown that the impulse response of the optimal FIR filter can be obtained by a simple Riccati-type matrix differential equation. Especially for time-invariant systems, this FIR filter reduces to previously known simple forms. For implementation, a recursive form of the optimal FIR filter and smoother is derived by using adjoint variables, and computational algorithms are suggested. It is also shown by sensitivity analysis that the proposed optimal FIR filter alleviates potential divergence characteristics of the standard Kalman filter     

Explicit formulas for two state Kalman, H/sub 2/ and H/sub /spl infin// target tracking filters

The continuous time, two state, target tracking problem is considered from the Kalman, H/sub 2/, and H/sub /spl infin// filter viewpoint. While previous treatments were numerical in nature, analytic transient responses and infinite horizon solutions with analytic performance expressions are presented here. Tracking indices, involving the maneuver and measurement uncertainties, are shown to have a role for both the steady state and transient responses. In addition, the H/sub /spl infin// tracker has a sensor index involving the performance bound and measurement uncertainty, which, along with the tracking index, plays a significant role in the H/sub /spl infin// tracker expressions. Analytical expressions for the probability of target escape, the probability that the target position will be outside the radar beamwidth (BW), are developed not only to compare the performance of various trackers, but also as a design tool to meet tracking specifications. Examples illustrate the performance of the target trackers as a function of the error gain upper bound.     

基于联邦滤波的多源融合导航算法

在复杂多变环境下,单一导航源的定位性能和鲁棒性会受到一定的影响。针对汽车、小型飞行器在城市、峡谷、卫星信号缺失或被遮挡以及导航信息源繁多等情况,研究了基于联邦滤波的多源融合导航算法。该算法综合利用了各种不同的信息源,经过多传感器的高度集成、多信息源的数据融合,生成时空基准统一且具有抗干扰、连续、可靠的PNT服务信息。设计的联邦滤波器采用两级结构,在子滤波器中进行局部估计后,在主滤波器中进行最优合成。此外,每个子滤波器加入了故障诊断算法,且结合自适应滤波理论进行信息因子的自适应分配,有效提高了故障检测能力。最后,通过实验验证了不同信息源组合的有效性,表明所设计的基于联邦滤波的多源融合算法可以提供稳定、可靠以及高精度的多源融合定位服务,具有一定的研究意义和实际价值。     

基于多特征点参数权值优化的蒙皮点云去噪研究

点云去噪的效果对三维扫描过程后续的曲面拟合与造型设计至关重要，如何快速准确提取特征点已成为研究热点，然而点云去噪的关键之处在于奇异值与离群值的检测。提出耦合多特征点参数的去噪模型，分别讨论每个特征点参数对去噪模型的影响程度；采用群智能算法求解出一组最优参数权重，以此确定点云去噪模型，从而达到三维散乱点云最优去噪效果；通过对Bunny 模型进行去噪仿真以及某一型号的蒙皮进行去噪实验，对去噪模型进行验证。结果表明：本文提出的点云去噪模型相较于半径滤波器、统计滤波器、改进体素滤波结合高斯滤波模型，迭代更快、耗时更少，具有更好的去噪效果。     

连续时间系统的定常状态估计器

 对于线性定常的连续时间系统,当系统和测量噪声为平稳白噪声过程时,研究了定常的状态估计器的设计方法。为了改进传统的Kalman-Bucy滤波器的瞬态性能,提出了两种新的性能函数的定义,从而给出了两种便于工程应用的修正的Kalman-Bucy滤波器。     

Degradation of Signal-to-Noise Ratio Due to IF Filtering

A receiver for biphase modulated signals using an integrate-and-dump filter is optimum only if the IF filter bandwidth is infinite. Finite IF filter bandwidth results is a performance degradation. Using the predetection signal-to-noise ratio as the performance criterion, a lower bound on this quantity is determined as a function of the ratio (IF filter bandwidth)/(bit rate). The corresponding upper bound on the error probability is also presented.     

Adaptive Two-Stage Extended Kalman Filter for a Fault-Tolerant INS-GPS Loosely Coupled System

The well-known conventional Kalman filter requires an accurate system model and exact stochastic information. But in a number of situations, the system model has an unknown bias, which may degrade the performance of the Kalman filter or may cause the filter to diverge. The effect of the unknown bias may be more pronounced on the extended Kalman filter (EKF), which is a nonlinear filter. The two-stage extended Kalman filter (TEKF) with respect to this problem has been receiving considerable attention for a long time. Recently, the optimal two-stage Kalman filter (TKF) for linear stochastic systems with a constant bias or a random bias has been proposed by several researchers. A TEKF can also be similarly derived as the optimal TKF. In the case of a random bias, the TEKF assumes that the information of a random bi?s is known. But the information of a random bias is unknown or partially known in general. To solve this problem, this paper proposes an adaptive two-stage extended Kalman filter (ATEKF) using an adaptive fading EKF. To verify the performance of the proposed ATEKF, the ATEKF is applied to the INS-GPS (inertial navigation system-Global Positioning System) loosely coupled system with an unknown fault bias. The proposed ATEKF tracked/estimated the unknown bias effectively although the information about the random bias was unknown.     

IMM estimator versus optimal estimator for hybrid systems

The special feature of the interacting multiple model (TMM) estimator that distinguishes it from other suboptimal multiple model (MM) estimators is the "mixing/interaction" between its "mode-matched" base state filtering modules at the beginning of each cycle. This note shows that the same feature is exactly what it has in common with the optimal estimator for hybrid (MM) systems and this can be seen as the main reason for its success.     

机动目标的广义卡尔曼滤波信号模型的理论推导

卡尔曼滤波方法是一种应用广泛的估值方法,但其要求系统数学模型已知,而广义卡尔曼滤波与卡尔曼滤波相比并不需要系统数学模型已知,具有更强的现实意义。从泛函分析的角度讨论了连续函数的最佳逼近问题,并以此为基础提出了广义卡尔曼滤波的信号模型和递推公式。最后通过广义卡尔曼滤波与经典卡尔曼滤波仿真曲线的比较,验证了广义卡尔曼滤波的有效性。     

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.     

一种基于可观测度分析的SINS/GPS自适应-反馈校正滤波新方法

 针对机载捷联惯导系统(SINS)/全球定位系统（GPS）组合导航系统不完全可观测导致滤波器精度下降甚至发散的问题,提出了一种基于系统状态可观测度分析的自适应反馈校正滤波新方法。该滤波方法改进了系统可观测度的归一化处理方法,将归一化处理后的系统状态可观测度作为反馈因子,对SINS系统进行自适应反馈校正。最后,将该方法应用于机载合成孔径雷达(SAR)运动补偿用SINS/GPS组合导航系统中,飞行试验结果表明该方法在系统不完全可观测的情况下有效地提高了导航精度。