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。     

Performance Evaluation of Suboptimal Filters

The use of a Kalman filter in an applications problem requires a detailed model of both the system dynamics and the measurement dynamics. The model for many problems may be extremely large in dimensionality. However, in many instances one has a limited computer capability and, thus, must purposely introduce modeling errors into the filter in order to gain a computational advantage. However, as is well known, this may lead to the phenomenon of filter divergence. This paper considers the development of equations which allow one to evaluate a filter of reduced state. The equations are based upon using covariance analysis techniques in order to determine the true root-mean-square estimation error. These equations are computationally more advantageous than others appearing in the literature.     

Losses for Frequency Diversity Waveform Systems

The use of a frequency diversity waveform to improve target detectability by reducing the fluctuation loss is a well-established technique. The diversity gain is commonly computed under the assumptions of a matched filter processor and the independent fluctuation of the target from channel to channel. Sometimes these conditions are not met and the full diversity gain cannot be obtained. In order to accurately compute the attainable diversity gain, it is necessary to calculate losses that occur only for diversity waveforms. The losses that have been considered are 1) unknown target velocity loss, 2) dependent samples due to small channel-to-channel frequency spacing, 3) linear envelope detector law loss, and 4) constant false alarm rate (CFAR) lpss.     

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.     

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。     

The effect of the clutter-to-noise ratio on Doppler filterperformance

The effect of the clutter-to-noise ratio on the performance of a Doppler filter is considered. Clutter is assumed to have a power level which is unknown and varies in range. The assessment of the performance of a Doppler filter is based on the gain of the filter, which is the normalized output signal-to-interference ratio improvement at a given Doppler. The gain is generally a complex function of the statistics of the clutter. New upper and lower bounds on the gain differential between the expected design point clutter-to-noise ratio and the actual clutter-to-noise ratio are found. These bounds are independent of the clutter covariance matrix and are only a function of the unknown clutter-to-noise ratio. The bounds are valid for both Gaussian and non-Gaussian noise and for arbitrary linear filters. The upper and lower bounds differ by the theoretical coherent integration gain, 10 logN dB, where N is the number of pulses. A tighter lower bound is found for the case when the filters are matched filters. A simple exact expression is found for matched filters assuming a Gaussian Markov clutter model as the clutter spectral width approaches zero. An easily implementable adaptive procedure is given which improves performance due to the unknown clutter-to-noise ratio. This work extends a previous result, valid for the Emerson filter, that shows the effect of clutter-to-noise ratio on performance in terms of an average quantity, the improvement factor     

Altitude Estimation Using Asynchronous ? - ? Tracking Filters

In the analysis of the ?-? tracking filter it is usually assumed that the tracking filter and data source operate in synchronism at a constant data rate. However, in a multisensor environment in which the tracking algorithm operates at fixed intervals, the tracking filter cannot be synchronized with the sensors. An analytical solution is obtained for the case in which the tracking filter and data source operate asynchronously with a ?time-correction? process used to approximate the synchronous operation of the tracking filter. An example is given in which the effects of data quantization on the performance of an altitude tracking filter for air traffic control are examined. It is shown that the asynchronous operation of the tracking filter in the example without the time-correction process will result in significant errors in the predicted altitude.     

A passive tracking filter for missile capture

A practical filter is suggested for ground-based missile tracking in a capture-guidance mode utilizing angle-only measurements from a passive missile sensor, and its performance is evaluated by a realistic system-simulation study. A missile-acceleration model that provides inputs to the filter is also suggested. The filter has a decoupled structure of independent azimuth and elevation channels, which requires fewer computations in solving the filter gain compared to a coupled structure. Simulation results show good tracking performance of the filter when used in association with the proposed missile-acceleration model     

Convergence analysis for inexact mechanization of Kalman filtering

A computational aspect of real-time estimation is considered, in which the estimation algorithm to be used has the standard optimal Kalman filtering structure, but the actual inverse matrix within the Kalman gain is replaced by an expedient approximation at each instant. In real-time applications, most Kalman filtering schemes are approximate to a degree as a consequence of numerical roundoff matrix inversion. The convergence properties and error estimates of such schemes are obtained to provide a theoretical basis for gauging the utility of using the above approximations of the Kalman gain matrix at each time instant. A new exponentially convergent scheme is also suggested for approximating the inverse matrix within the Kalman gain. Conditions are determined under which online approximate matrix inversion can be eliminated as the cause of Kalman filter divergence in real-time implementations     

Quasi-Optimal Demodulation of Pulse-Frequency Modulation Systems

Novel demodulator structures are derived using a theory for the quasi-optimal on-line demodulation of pulse-frequency modulated (PFM) signals in the presence of white Gaussian channel noise. The basic demodulator consists of a phase-locked loop with time-varying gain elements. Furthermore, its integrators are appropriately reset as each new pulse is received. This modulator may be augmented with additional integrators and gain elements to achieve quasi-optimal demodulation with delay. The quasi-optimal demodulation approaches optimal demodulation, in the minimum mean-square-error sense, as the signal-to-noise ratio increases. The various quasi-optimal receivers are derived by application of the extended Kalman filter theory to a state-space signal model.     

Maximizing the Usable Bandwidth of MTI Signal Processors

A technique is presented for maximizing the percentage of usable Doppler bandwidth throughout which a radar return can be detected while maintaining an acceptable clutter suppression. The technique employs the weighted Chebyshev approximation to the design of a transversal high-pass digital filter which has an optimal passband ripple for a given number of filter weights and associated integration gain consistent with the required increase in signal-to-noise ratio needed for acceptable probabilities of detection and false alarm. Conventional approaches to the design of a movingtarget arget indictor (MTI) filter which maximizes the improvement factor by clutter suppression typically improve the signal-to-background noise ratio over less than 50 percent of the range between dc and the pulse-repetition frequency fT. This technique can increase the usable bandwidth to 80 percent or more of fT. Two examples are included which utilize parameter values from the Army Missile Command's experimental radar and demonstrate the interactive influence of such filter parameters as the number of weights, passband ripple and bandedge, and stopband attenuation and cutoff.     

Kalman Filter Applications in Airborne Radar Tracking

This paper studies the application of Kalman filtering to single-target track systems in airborne radar. An angle channel Kalman filter is configured which incorporates measures of range, range rate, and on-board dynamics. Theoretical performance results are given and a discussion of methods for reducing the complexity of the Kalman gain computation is presented. A suboptimal antenna controller which operates on the outputs of the angle Kalman filter is also described. In addition, methodological improvements are shown to exist in the design of range and range-rate trackers using the Kalman filter configuration.     

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。     

Unsupervised Tracking of Maneuvering Vehicles

The design of adaptive filters for the tracking of high-performance maneuvering targets is a fundamental problem in real-time surveillance systems. As is well known, a filter which provides heavy smoothing can not accurately track an evasive maneuver, and conversely. Consequently, one is led to the consideration of adaptive methods of filter design. This paper presents an improved self-adaptive filter algorithm for on-line solution of the above problem. Basically, this algorithm utilizes the orthogonality property of the residual time series to force the filter to automatically track the optimal gain levels in a changing environment.     

Programmed-Amplitude Binary Signaling with Decision and Information Feedback

A sequential detection scheme with combined decision and information feedbacks, where the signal amplitude at each iteration may be properly programmed, is analyzed. This approach allows a remarkable gain in transmission rate and a significant reduction in the peak-to-average power ratio, relative to the nonsequential scheme with constant signal amplitude.     

A Kalman Filter Based Tracking Scheme with Input Estimation

Beginning with the derivation of a least squares estimator that yields an estimate of the acceleration input vector, this paper first develops a detector for sensing target maneuvers and then develops the combination of the estimator, detector, and a "simple" Kalman filter to form a tracker for maneuvering targets. Finally, some simulation results are presented. A relationship between the actual residuals, assuming target maneuvers, and the theoretical residuals of the "simple" Kalman filter that assumes no maneuvers, is first formulated. The estimator then computes a constant acceleration input vector that best fits that relationship. The result is a least squares estimator of the input vector which can be used to update the "simple" Kalman filter. Since typical targets spend considerable periods of time in the constant course and speed mode, a detector is used to guard against automatic updating of the "simple" Kalman filter. A maneuver is declared, and updating performed, only if the norm of the estimated input vector exceeds a threshold. The tracking sclheme is easy to implement and its capability is illustrated in three tracking examples.     

Performance of a Two-Sample Mann-Whitney Nonparametric Detector in a Radar Application

The detection of signals in an unknown, typically non-Gaussian noise environment, while attempting to maintain a constant false-alarm rate, is a common problem in radar and sonar. The raw receiver data is commonly processed initially by a bank of frequency filters. The further processing of the outputs from the filter bank by a two-sample Mann-Whitney detector is considered. When the noise statistics in all filters are identical, the Mann-Whitney detector is distribution free, i. e., the false-alarm probability may be prescribed in advance regardless of the precise form of the noise statistics. The primary purpose of this paper is to demonstrate the potential advantage of nonparametric detectors over conventional detectors. The signal detection performance of the Mann-Whitney detector is compared to that of an ordinary linear envelope detector plus integrator in the presence of Gaussian and several hypothetical forms of non-Gaussian noise. This comparison is made for both uniform and nonuniform distributions of noise power across the filter bank. Besides providing a much more constant false-alarm rate than the conventional detector, the Mann-Whitney detector's signal detection performance is found also to be much less sensitive to the form of the noise statistics. In one case, its detection sensitivity is found to be 11 dB better than that of the conventional detector. Even when the noise power density is made moderately nonuniform across the filter bank, the detection performance of the Mann-Whitney detector is found not to be significantly affected.     

基于一种灰色聚类滤波算法的飞机远程故障诊断虚警辨识方法

 在航线排故过程中,常会出现飞机远程实时故障报文信息的“飞机通信与寻址系统(ACARS)虚警”现象,使排故效率降低。对此,本文将灰色聚类和滤波理论结合提出了一种新的辨识算法——灰色聚类滤波算法,将常增益滤波引入灰色聚类算法,在灰色聚类算法中使用ACARS报文故障的当前数据分类故障、判断虚警的基础上,增加报文故障的历史数据,用滤波方法将历史的和当前的数据结合起来,提高辨识虚警的准确性。本算法应用于航空公司的波音777机队,能够有效辨识飞机实时故障报文信息中的虚警。     

关于闭环特征多项式的数值解及其应用问题

本文提出应用分析法,在不同的阻尼比数值下,直接计算闭环伺服机构放大系数K的简化法。当K值算出后,可通过高阶特征多项式系数比值近似地解出方程的根值,并可通过劈因解根法,在要求精度内解出根值并绘出根轨迹曲线。     

Optimum Steady State Position, Velocity, and Acceleration Estimation Using Noisy Sampled Position Data

A one-dimensional tracking filter based on the Kalman filtering techniques for tracking of a dynamic target such as an aircraft is discussed. The target is assumed to be moving with constant acceleration and is acted upon by a plant noise which perturbs its constant acceleration motion. The plant noise accounts for maneuvers and/or other random factors. Analytical results for estimating optimum steady state position, velocity, and acceleration of the target are obtained.