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1.
The estimation problem is defined, and a review of how the linear estimation approach of Kalman filtering is extrapolated to form an extended Kalman filter (EKF), applicable for state estimation in nonlinear systems is presented. A mechanization of an EKF variation known as an iterated EKF, offering improved tracking performance, is treated. A streamlined version of an iterated EKF that has a lesser computational burden (fewer operations per cycle or time step) than prior formulations is offered. A nonlinear filtering application example, to be used as a testbed for this new approach, is described, and the detailed modeling considerations as needed for exoatmospheric random-variable radar target tracking are discussed. The performance of the streamlined mechanization is illustrated in this radar target tracking example, and comparisons are made with the performance of an EKF without measurement iteration 相似文献
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Siouris G.M. Guanrong Chen Jianrong Wang 《IEEE transactions on aerospace and electronic systems》1997,33(1):232-240
The important tracking problem by radar of an incoming ballistic missile system, which contains uncertainty in modeling and noise in both dynamics and measurements, is studied. The classical extended Kalman filter (EKF) is no longer applicable to such an uncertain system, and so a new extended interval Kalman filter (EIKF) is developed for tracking the missile system. Computer simulation is presented to show the effectiveness of the EIKF algorithm for this uncertain and nonlinear ballistic missile tracking problem. 相似文献
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Deergha Rao K.. Sridhar G. 《IEEE transactions on aerospace and electronic systems》1995,31(3):1193-1198
A new approach using a multilayered feed forward neural network for pulse compression is presented. The 13 element Barker code was used as the signal code. In training this network, the extended Kalman filtering (EKF)-based learning algorithm which has faster convergence speed than the conventional backpropagation (BP) algorithm was used. This approach has yielded output peak signal to sidelobe ratios which are much superior to those obtained with the BP algorithm. Further, for use of this neural network for real time processing, parallel implementation of the EKF-based learning algorithm is indispensable. Therefore, parallel implementation has also been developed 相似文献
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Seong-Taek Park Jang Gyu Lee 《IEEE transactions on aerospace and electronic systems》1998,34(4):1337-1344
The extended Kalman filter (EKF) has been widely used as a nonlinear filtering method for radar tracking problems. However, it has been found that if cross-range measurement errors of the target position are large, the performance of the conventional EKF degrades considerably due to nonnegligible nonlinear effects. A new filtering algorithm for improving the tracking performance with radar measurements is developed based on the fact that correct evaluation of the measurement error covariance is possible in the Cartesian coordinate system. The proposed algorithm may be viewed as a modification of the EKF in which the variance of the range measurement errors is evaluated in an adaptive manner. The filter structure facilitates the incorporation of the sequential measurement processing scheme, and this makes the resulting algorithm favorable to both estimation accuracy and computational efficiency. Computer simulation results show that the proposed method offers superior performance in comparison to previous methods. Moreover, our developed algorithm provides some useful insight into the radar tracking problem 相似文献
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A space-time adaptive processing (STAP) algorithm for delay tracking and acquisition of the GPS signature sequence with interference rejection capability is developed. The interference can consist of both broadband and narrowband jammers, and is mitigated in two steps. The narrowband jammers are modelled as vector autoregressive (VAR) processes and rejected by temporal whitening. The spatial ing is implicitly achieved by estimating a sample covariance matrix and feeding its inverse into the extended Kalman filter (EKF). The EKF estimates of the code delay and the fading channel are used for a t-test for acquisition detection. Computer simulations demonstrate robust performance of the algorithm in severe jamming, and also show that the algorithm outperforms the conventional delay-locked loop (DLL). 相似文献
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In an environment subject to sudden change, the accuracy of tracking and prediction is strongly influenced both by the sensor architecture and by the quality of the sensors. An image-enhanced algorithm is presented for both path following and covariance estimation in applications where the sensors are subject to sudden and unpredictable variation in quality. For an illustrative trajectory, the performance of the algorithm is contrasted with an extended Kalman filter (EKF) and an image-enhanced algorithm based upon the nominal sensors 相似文献
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利用到达方向(DOA)和多普勒频率(DF)建立了固定单站对空中运动辐射源的无源定位与跟踪模型,推导了该模型下的伪线性测量方程,用伪线性卡尔曼滤波(PLKF)算法实现了定位与跟踪;在此基础上用k时刻的状态估计值代替一步预测值对该算法进行了改进;最后与扩展卡尔曼滤波(EKF)算法进行比较。仿真结果表明,改进的PLKF算法具有更快的收敛速度和更高的收敛精度,PLKF算法克服了EKF算法的一些缺点。 相似文献
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《Aerospace Science and Technology》2006,10(2):149-155
A theoretical analysis of on-line autonomous intelligent adaptive tracking controller based on emotional learning model in mammalians brain (BELBIC) for aerospace launch vehicle is presented. The control algorithm is provided with some sensory inputs and reward signal, subsequently it autonomously seeks the proper control signal to be executed by actuators, thus eliminating tracking error without pre-knowledge of the plant dynamics. The algorithm is very robust and fast in adaptation with dynamical change in the plant, due to its on-line learning ability. Development and application of this algorithm for an aerospace launch vehicle during atmospheric flight in an experimental setting is presented to illustrate the performance of the control algorithm. 相似文献
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在建立飞机环控系统数学模型的基础上,提出采用双模型滤波方法进行参数估计、状态预测和故障诊断,提高飞机环控系统故障诊断的快速性和准确性。如果采用最小二乘算法,参数估计是静态的,故障诊断延迟一般较大;采用单模型扩展Kalman滤波算法,虽然能够实现动态估计,但不能同时兼顾稳态过程和过渡过程(突发故障)的参数估计,导致误差较大。为了解决上述难题,针对飞机环控系统换热器故障诊断,提出两模型滤波算法。该算法由两个滤波器组成,分别用于跟踪系统的稳态和过渡过程。由于采用了两滤波器模型分别匹配不同的系统特征,能够改善飞机环控系统不同状态下的参数估计和状态预测性能,从而提高系统故障诊断的精度和速度。仿真结果证实了该算法的有效性。 相似文献
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针对雷达均不能提供目标加速度信息,在目标机动时会出现跟踪精度差甚至跟踪发散的问题,提出一种基于径向加速度的Singer-EKF算法。该算法在信号处理阶段利用Radon-Ambiguity变换(RAT)估计出目标的径向加速度,并通过坐标转换将其引入量测向量中,然后采用基于Singer模型的扩展卡尔曼滤波(EKF)算法实现机动目标的跟踪。仿真验证了该方法的有效性,并与传统的不带径向加速度的扩展卡尔曼滤波(EKF)方法进行了比较,结果表明该方法在径向距离、位置、加速度和速度估计精度方面都有所提高。 相似文献
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针对机动目标跟踪巾扩展卡尔曼算法(EKF)收敛速度慢、跟踪精度低的问题,基于粒子滤波(PF)和辅助粒子滤波(APF)的基本思想,结合目标先验信息将速度约束条件加入到跟踪过程巾,对辅助粒子滤波算法进行了仿真分析,与扩展卡尔曼进行仿真对比,分析了跟踪性能和误差。仿真结果表明,对机动目标跟踪问题,辅助粒子滤波不仅解决了扩展卡尔曼线性化困难难题,与EKF相比还具有收敛速度快,跟踪精度高的优点。 相似文献
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UKF方法及其在方位跟踪问题中的应用 总被引:13,自引:0,他引:13
采用UKF(Unscented Kalman Filter)方法处理了平面内地面站对目标的方位跟踪的估计问题。目标的位置和速度由选定的高斯分布采样点来近似,在每个更新过程中,采样点随着状态方程传播并随着非线性测量方程变换,由此不但得到目标位置和速度的均值及较高的计算精度,而且避免了对非线性方程的线性化过程。仿真结果表明,UKF方法比传统的扩展卡尔曼滤波(EKF)算法有更高的估计精度,并能有效地克服非线性严重时,方位跟踪问题中很容易出现的滤波发散问题。 相似文献
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利用GPS进行姿态估计的一种算法 总被引:3,自引:0,他引:3
首先建立了全球定位系统(GPS)姿态确定的观测方程;然后给出了利用GPS进行飞行器姿态估计的模型,并对该模型进行了线性处理;最后利用攻推广卡尔曼滤波技术,针对某飞行器进行了仿真计算。计算结果表明,对于不同的测量噪声和系统噪声,滤波器都有较好的估计,姿态估计的精度明显高于单纯GPS姿态确定的精度,可以满足大多数飞行器对姿态确定的要求,证实了模型和算法可用性。 相似文献
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Two-step optimal estimator for three dimensional target tracking 总被引:1,自引:0,他引:1
Gurfil P. Jeremy Kasdin N. 《IEEE transactions on aerospace and electronic systems》2005,41(3):780-793
This study presents an adaptation of a novel estimation methodology to the general nonlinear three-dimensional problem of tracking a maneuvering target. The two-step optimal estimator (TSE) suggests an attractive alternative to the standard extended Kalman filter (EKF). A superior performance is accomplished by dividing the estimation problem into two steps: a linear first step and a nonlinear second step. The target tracking performance of the TSE is shown to be better than an EKF implemented in either inertial or modified spherical coordinates. In the passive case, where bearing/elevation angles only are measured, the TSE yields excellent range and target acceleration estimates. In the active case, where range measurement is available as well, a homing missile employing closed-loop optimal guidance based on the TSE state estimates obtains smaller miss distances than with either versions of the EKF. 相似文献
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为了解决大场景下基于三维到达角的目标跟踪问题,提出了一种具有无偏性的伪线性卡尔曼滤波。首先,基于三维到达角信息对目标运动模型与量测模型进行建模;之后,对量测模型进行了伪线性化处理,得到了线性形式的目标量测模型。为了解决伪线性卡尔曼滤波存在的有偏性问题,提出了一种结合EKF(extend Kalman filter)的三维伪线性无偏卡尔曼滤波。仿真实验表明,该模型能够对非机动目标与机动目标有效跟踪,对于百公里级别的目标,当角测量误差从0.1°变化到0.5°,算法在仿真时间结束时均能将绝对位置误差降低至10 km以内,且算法的运行速度与EKF为同一个量级,同时兼顾了抗干扰能力、定位跟踪精度、运行效率的要求,能够为大场景下的目标跟踪提供有效方法。 相似文献
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基于自适应扩展卡尔曼滤波的载波跟踪算法 总被引:2,自引:1,他引:1
精确的载波相位测量是精密测距中一个很重要的研究点。针对传统扩展卡尔曼滤波(EKF)的固定设计在先验信息不充分和动态变化环境中存在的不足,提出了一种基于自适应扩展卡尔曼滤波(AEKF)的载波跟踪算法。该算法通过实时监测滤波器新息或残差的动态变化,以修正状态噪声方差和观测噪声方差,进而调整滤波器增益,控制状态预测值和观测值在滤波结果中的权重。理论分析和仿真结果表明,本算法充分利用了观测信号的统计特性,克服了传统扩展卡尔曼滤波算法的不足,能够获得更好的载波跟踪性能。 相似文献
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