稳定平台中陀螺漂移自适应实时估计算法

 针对陀螺稳定平台的漂移问题,建立了陀螺稳定跟踪装置在不同工作模式下陀螺漂移的数学模型,指出稳定模式下包含常值漂移和相关漂移的陀螺低频噪声是影响稳定精度的主要原因。提出一种自适应实时估计算法,采用卡尔曼滤波框架和滤波器收敛判据,结合Sage-Husa滤波和加权Sage-Husa滤波算法,利用跟踪器跟踪静止目标时输出的脱靶量信号对陀螺常值漂移和相关漂移进行估计。实验结果表明:该算法能够在系统模型和噪声特性均不准确的情况下使用,收敛时间小于3 s,估计均方差小于0.02 (°)/s,具有良好的鲁棒性和自适应能力。     

Tracking targets using adaptive Kalman filtering

A simple algorithm for estimating the unknown process noise variance of an otherwise known linear plant, using a Kalman filter is suggested. The process noise variance estimator is essentially dead beat, using the difference between the expected prediction error variance, computed in the Kalman filter, and the measured prediction error variance. The estimate is used to adapt the Kalman filter. The use of the adaptive filter is demonstrated in a simulated example in which a wildly maneuvering target is tracked     

一类新型的机动目标跟踪算法

针对机动目标跟踪,提出了基于截断正态概率模型的改进自适应目标跟踪算法,该算法具有结构和计算简单,鲁棒性好的特点,通过仿真结果对比,充分说明了文中所提出的跟踪算法能够较好地弥补传统的Kalman滤波方法在跟踪机动目标时的不足。     

A modified Gaussian sum approach to estimation of non-Gaussiansignals

A Gaussian sum estimation algorithm has previously been developed to deal with noise processes that are non-Gaussian. Inherent in this algorithm is a serious growing memory problem that causes the number of terms in the Gaussian sum to increase exponentially at each iteration. A modified Gaussian sum estimation algorithm using an adaptive filter is developed that avoids the growing memory problem of the previous algorithm while providing effective state estimation. The adaptive filter is comprised of a fixed set of estimators operating in parallel with each individual estimate possessing its own corresponding weighting term. A simulation example illustrates the new non-Gaussian estimation technique     

GPS/INS组合导航系统自适应滤波算法与仿真研究

随着组合导航系统应用环境的日趋复杂，给噪声统计特性的准确描述带来困难，这将造成Kalman滤波器不稳定甚至发散。首先对目前解决此问题常用的自适应滤波方法进行了总结和分析，在此基础上，给出了基于滤波收敛性判据，结合Sage-Husa自适应滤波和强跟踪Kalman滤波的改进自适应滤波算法。最后以GPS／INS组合导航系统为例进行了计算机仿真，结果表明：该算法可有效抑制滤波发散，具有较大范围的自适应能力。     

Design and Application of an Adaptive Tracking Filter Control System

The design of an adaptive tracking filter control system and its application to large flexible booster vehicles is described. The problem of elastic vehicle stability is considered along with its solution by conventional and adaptive techniques. The improvement in vehicle stability, which may be achieved with the adaptive tracking filter technique as compared to conventional techniques, is shown. The control system evolved uses two adaptive tracking filters to phase stabilize the first and second bending modes, in addition to conventional compensation techniques. The application of this adaptive technique to a vehicle in which the modal frequencies are in close proximity (<2.5 percent separation) is discussed. The mechanization of the adaptive control system involves the selection of design techniques and components that are electrically and physically compatible with the intended airborne application. The development of a suitable frequency tracking technique and tracking filter is described, in addition to the electronic and mechanical design of the adaptive control system prototype.     

Removal of Out-of-Sequence Measurements from Tracks

In multisensor tracking systems that operate in a centralized or distributed information processing architecture, measurements from the same target obtained by different sensors can arrive at the processing center out of sequence due to system latencies. In order to avoid either a delay in the output or the need for reordering and reprocessing entire sequences of measurements, such latent measurements have to be processed by the tracking filter as out-of-sequence measurements (OOSM). Recent work developed a "one-step" procedure for incorporating OOSM with multiple-time-step latency into the tracking filter, which, while suboptimal, was shown to yield results very close to those obtained by reordering and reprocessing an entire sequence of measurements. The counterpart of this problem is the need to remove (revocate) measurements that have already been used to update a track state. This can happen in real-world systems when such measurements are reassigned to another track. Similarly to the problem of update with an OOSM, it is desired to carry out the removal of an earlier measurement without recomputing the track estimate (and the data association) using possibly a long sequence of subsequent measurements one at a time. A one-step algorithm is presented for this problem of removing a multistep OOSM.     

Mobility control of unmanned aerial vehicle as communication relay in airborne multi-user systems

In this paper, a model-based adaptive mobility control method for an Unmanned Aerial Vehicle(UAV) acting as a communication relay is presented, which is intended to improve the network performance in airborne multi-user systems. The mobility control problem is addressed by jointly considering unknown Radio Frequency(RF) channel parameters, unknown multi-user mobility, and non-available Angle of Arrival(AoA) information of the received signal. A Kalman filter and a least-square-based estimation algorithm are used to predict the future user positions and estimate the RF channel parameters between the users and the UAV, respectively. Two different relay application cases are considered: end-to-end and multi-user communications. A line search algorithm is proposed for the former, with its stability given and proven, whereas a simplified gradient-based algorithm is proposed for the latter to provide a target relay position at each decision time step, decreasing the two-dimensional search to a one-dimensional search. Simulation results show that the proposed mobility control algorithms can drive the UAV to reach or track the optimal relay position movement, as well as improving network performance. The proposed method reflects the properties of using different metrics as objective network performance functions.     

高超声速飞行器气动伺服弹性的自适应抑制

针对弹性高超声速飞行器的气动伺服弹性问题，提出一种结合线性自抗扰和自适应陷波器的综合控制方案。针对强耦合和强不确定性问题，采用线性自抗扰控制对总扰动进行快速估计和补偿。为了在最小化对刚体控制性能影响的同时实现对频率未知且时变的弹性模态的抑制，采用能够在线估计弹性频率的自适应陷波器。根据气动伺服弹性抑制问题的特点提炼出对自适应陷波器的性能需求。针对这些需求设计了两种基于递推最大似然法的多频率直接辨识方案——参数单独自适应和同时自适应方案。为了提高辨识算法在各种随机扰动下的鲁棒性，在此基础上提出一种在线有效性监督机制，并通过大量的数字仿真对两种方案进行了性能对比。最后，在弹性高超声速飞行器模型上进行仿真，仿真结果验证了所提控制方案的有效性。     

A class of near optimal JPDA algorithms

The crucial problem in multiple target tracking is the hit-to-track data association. A hit is a received signal from a target or background clutter which provides positional information If an incorrect hit is associated with a track, that track could diverge and prematurely terminate or cause other tracks to also diverge. Most methods for hit-to-track data association fall into two categories: multiple hypothesis tracking (MHT) and joint probabilistic data association (JPDA). Versions of MHT use all or some reasonable hits to update a track and delay the decision on which hit was correct. JPDA uses a weighted sum of the reasonable hits to update a track. These weights are the probability that the hit originated from the target in track. The computational load for the joint probabilities increases exponentially as the number of targets increases and therefore, is not an attractive algorithm when expecting to track many targets. Reviewed here is the JPDA filter and two simple approximations of the joint probabilities which increase linearly in computational load as the number of targets increase. Then a new class of near optimal JPDA algorithms is introduced which run in polynomial time. The power of the polynomial is an input to the algorithm. This algorithm bridges the gap in computational load and accuracy between the very fast simple approximations and the efficient optimal algorithms     

The Tracking Index: A Generalized Parameter for ? ? ? and ? ? ? ? Target Trackers

A generalized, optimal filtering solution is presented for the target tracking problem. Applying optimal filtering theory to the target tracking problem, the tracking index, a generalized parameter proportional to the ratio of the position uncertainty due to the target maneuverability to that due to the sensor measurement, is found to have a fundamental role not only in the optimal steady-state solution of the stochastic regulation tracking problem, but also in the track initiation process. Depending on the order of the tracking model, the tracking index solution yields a closed form, consistent set of generalized tracking gains, relationships, and performances. Using the tracking index parameter, an initializing and tracking procedure in recursive form, realizes the accuracy of the Kalman filter with an algorithm as simple as the well-known ? ? ? filter or ? ? ? ? ? filter depending on the tracking order.     

基于联邦滤波的SINS/GNSS/OD/高度计多源组合导航算法研究

针对城市情况下车载导航时单一导航源易受干扰的问题,提出了一种基于自适应联邦Kalman滤波的多源组合导航算法.该模型具有两级结构,由子滤波器进行各信息源局部估计后,通过主滤波器进行最优融合估计.融合具有不同工作特点的导航传感器的输出信息组成多源信息组合导航系统,从而提高了导航系统的精度和鲁棒性,且通过故障诊断算法实时检...     

图象序列中机动目标的形心跟踪

从边检测边跟踪的角度探讨了图象序列中机动目标的形心跟踪问题,深入分析了强高斯噪声背景下目标形心估计的统计性质及用于形心估计的图象预处理方法。指出经典的图象二值化变换分割后作形心估计的方法面临着估计偏差和方差的矛盾,提出了用自适应交互三模型(ATIMM)跟踪图象序列中机动目标的方法,同时发现在了解目标形状的条件下,空间匹配滤波,二值变换点集聚类和 ATIMM三者的结合对图象序列中的机动目标具有最好的跟踪性能。     

Adaptive Phased-Array Tracking in ECM using Negative Information

Target tracking with adaptive phased-array radars in the presence of standoff jamming presents both challenges and opportunities to the track filter designer. A measurement likelihood function is derived for this situation which accounts for the effect of both positive and negative contact information. This likelihood function is approximated a? a weighted sum of Gaussian terms consisting of both positive and negative weights, accounting for the positive and negative contact information. Additionally, recent theoretical results have been reported which have derived an accurate measurement error covariance in the vicinity of the jammer when adaptive beamforming is used by the radar to null the effects of the jammer. We compare the impact of using a likelihood function that accounts for negative contact information and the corrected measurement error covariance by comparing five Kalman filter-based trackers in five different scenarios. We show that only those track filters which use both the negative contact information and the corrected measurement error covariance are effective in maintaining track on a maneuvering target as it passes through the jamming region. This approach can also be generalized to any target tracking problem where the sensor response is anisotropic.     

常增益变采样率自适应滤波

作者曾给出一种跟踪飞机机动的自适应滤波,采用直角坐标系中的9维状态量,利用最优检测统计量探测目标机动,并用估计到的机动加速度修正目标的预报值。 本文将在文献[1]的基础上,提出两种简化自适应滤波。其一,采用两套直角坐标系中的9维卡尔曼滤波,其中的一套滤波用于取代最优检测统计量,完成对目标机动的探测和估计的功能;其二,采用三通道解偶的常增益变采样率自适应滤波。     

Adaptive Gaussian sum squared-root cubature Kalman filter with split-merge scheme for state estimation

The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.     

自寻的导弹攻击机动目标的最优制导规律的研究及实现

提出一种适用于红外和雷达自寻的制导导弹的适应性强、制导精度高、易实现的最优制导规律。为解决工程实现的关键问题,提出了目标机动加速度模型。根据该模型并利用导弹导引头的AGC信号或雷达测距信号,给出了目标机动加速度、导弹与目标相对距离和距离变化率的估计算法。     

航空发动机主供油计量活门故障主动容错控制器设计

针对航空发动机主供油计量活门执行机构发生故障时,系统的输出将会发生跳变,不能很好地跟踪系统期望输出的现象,采用模型参考自适应方法设计了主动容错控制器来解决这一问题.首先针对执行机构故障问题进行了数学描述.其次,选取系统的标称模型为参考模型,以输出误差最小化为优化目标,基于李雅普诺夫稳定性原理,寻找合适的自适应调节律,设计了自适应容错控制器.最后,针对发动机某一工况点进行了不同故障情况下的数值仿真.仿真结果表明:在主供油计量活门发生跳变故障和卡死故障时,自适应容错控制器可以通过参数自动调节,使得系统的输出能够很好地跟踪模型的输出,跟踪误差最终逼近于零.      

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.     

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。