一种新型模糊自适应Kalman滤波器在组合导航中的应用

针对光纤陀螺捷联惯导(FOG SINS)/GPS组合导航系统实际工作环境中,由于系统噪声与量测噪声模型发生变化而带来的滤波器发散的问题,提出一种新型模糊自适应Kalman滤波器(FSHAKF).通过引入IMU精度因子与GPS水平精度因子,构造模糊推理系统(FIS),实时更新自适应参数,有效地解决了传统Sage-Husa自适应滤波器(SHAKF)估计模型不准确、系统噪声与量测噪声无法同时估计以及滤波器长时间易发散的问题.仿真实验表明,本文提出的FSHAKF算法相较于SHAKF算法,估计精度得到明显提高,且避免了滤波器的发散.     

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.     

Navigation Accuracy Category-Position Models and Estimate Position Uncertainty Calculations for TIS-B System

Navigation accuracy category-position (NACp) is an important parameter for system accuracy of traffic information service-broadcast (TIS-B), which is determined by estimate position uncertainty (EPU). Centered about the problems that the existing EPU calculation based on noise measurement is low in accuracy and unfit for describing uncorrected biases in target reports, this article analyses the traditional NACp model, and uses the least square estimation (LSE) in EPU calculation. Furthermore, it proposes an extended NACp model, which considers both noise and biases and acquires EPU estimation with the help of approximate multiplex Taylor expression. Analysis and simulation show that the proposed method not only leads to significant improvement of the accuracy of EPU calculation, but is fit for EPU calculation with tracking biases in TIS-B system as well. As such it can find application in practice to depict different kinds of error models in TIS-B system.     

Reduced state estimator for systems with parametric inputs

A reduced state estimator is derived for systems with bounded parameters as inputs. Optimal filter gains are derived for minimizing the total covariance of the estimation error due to measurement noise and parameter uncertainty. It is shown that these filter gains for a two-state system with a Gaussian parameter satisfy the Kalata relation in steady state. Equations are also derived for optimally filtering measurements in arbitrary time order. This reduced state estimator offers novelties over a traditional Kalman filter in its application to the class of problems considered. The total error covariance, which is minimized, makes no use of plant noise. Furthermore, the filter is easier to optimize in high dimensional and multiple sensor applications as well as in processing out-of-sequence measurements.     

Generalized input-estimation technique for tracking maneuveringtargets

A new input estimation technique for target tracking problem is proposed. Conventional input estimation techniques assume that the target maneuver level is constant within the detection window, which has been the major drawback of the techniques. The proposed technique is developed to overcome this drawback by modeling the target maneuver as a linear combination of some basic time functions. The resulting algorithm has a generalized formulation including earlier works on input estimation. A detection performance of the proposed algorithm is analyzed by investigating the detection sensitivity according to the selection of maneuver models and other design parameters such as the detection window size, measurement noise level, and sampling step size. A computer simulation study shows that the estimation performance of the proposed algorithm is comparable to Bogler's input estimation method while the computation time is greatly reduced     

基于短时分数阶傅里叶变换的 含噪信号特征提取

通过对比噪声背景下短时傅里叶变换和短时分数阶傅里叶变换的时频分辨能力,进一步明确了短时分数阶傅里叶变换对频率时变信号的独特优势。研究了短时分数阶傅里叶变换的窗口滑动方式对瞬时频率估计的影响,可以发现:首先,在调频率较大或是变化剧烈时,短时分数阶傅里叶变换具有更好的鲁棒性;其次,在基于短时分数阶傅里叶变换的瞬时频率估计,实际应用中应主要采用以第一采样时刻作为估计值的逐点滑动方式。     

Passive target tracking using maximum likelihood estimation

Estimation of target trajectory from passive sonar bearings and frequency measurements in the presence of multivariate normally distributed noise, with unknown inhomogeneous general covariance, is modeled as a nonlinear multiresponse parameter estimation problem. It is shown that maximum likelihood estimation in this case is identical to optimizing a determinant criterion which has a concise form and contains no elements of unknown covariance matrix. A Gauss-Newton type algorithm using only the first-order derivatives of the model function and a new convergence criterion, is presented to implement such estimation. The simulation results demonstrate that performance of the maximum likelihood estimation method with the above noise model is superior to that with the traditional noise assumption     

Time delay estimation at high signal-to-noise ratio

The estimation of the delay between two signals is examined in the limit of high signal-to-noise ratio (SNR). It is shown that for the case of white noise, cross correlation with no prefiltering approaches the optimal maximum-likelihood (ML) estimator as the SNR grows to infinity. In simulation experiments with SNRs greater than 1, it outperforms the approximate ML estimator, which is based on estimated spectra. Other algorithms, such as generalized cross correlation or parameter estimation algorithms, are shown to be suboptimal at high SNRs     

集成运算放大器的噪声及其简化计算

介绍了运算放大器产生的机构，运放的Ｅｎ－Ｉｎ噪声等效电路模型，在等效电路的基础上求出了运放的噪声系数及最佳噪声电阻的估算公式。     

Arrival Time Estimation by Adaptive Thresholding

Arrival time estimation by adaptive thresholding is described. The probability density of arrival time is derived for differentiable Markov processes. The special case of additive, stationary noise is given particular attention. A direct derivation of the probability density of arrival time for pulses with sharply rising edges is given for arbitrary noise. The results are applied to the Gaussian and Rice distributions. Comparison with the Cramer-Rao bound of estimation theory indicates the asymptotic optimality of adaptive thresholding in the latter two cases.     

Noise analysis of a digital tanlock loop

The noise performance analysis of a nonuniform digital phase-locked loop (DPLL), called the digital tanlock loop (DTL), is investigated by both analytic and computer-simulation methods. The results are presented in terms of phase error probability mass function and mean time to skip cycle versus input signal-to-noise ratio (SNR). These results are compared to the ones obtained with the conventional sinusoidal DPLL loop (DPLL). It is found that, for low-to-moderate input SNR, the DTL has only a slight improvement over the DPLL. The DTL, however, has larger linear characteristics than the conventional DPLL, which makes it attractive for applications that require an increased tracking range or as a first stage in carrier tracking systems based on optimum estimation procedures such as a Kalman smoother     

Calculation of Output Noise Variances for Discrete Time-Invariant Filters

Certain calculations to minimize output noise variance are introduced. Many applied problems in sampled data systems require that data be smoothed in the presence of noise for the prediction of future positions, velocities, or accelerations. Smoothing coefficients in discrete time-invariant filters are computed to minimize the output noise variance, but under the constraints that the function and derivatives be predicted ahead. The output noise variance is seen to be a function of the input noise, the number of input signals (N+1) that the filter has to smooth, and the prediction time ?T. Four examples are given in the derivation of smoothing coefficients for step and ramp inputs subjected to either almost white noise or Gaussian-Markoff noise. The examples illustrate the number of constraint relations that the filter smoothing coefficients must satisfy for function and/or derivative convergence under noise-free conditions. The smoothing coefficients are also a function of the type of noise input into the system or the discrete filter. From the examples, it can be observed that as N becomes larger, the output noise variance becomes smaller, but the computation time is increased.     

Experiments in Adaptive Estimation of Unknown Binary Waveforms

A special-purpose adaptive machine is described which carries out estimation in real time of an unknown binary waveform which is perturbed with additive Gaussian noise. Unknown waveforms of over 103 samples in duration can be recovered. The unknown waveforms are of unknown epoch and can reappear at either random or periodic time intervals. The observed signal is received at moderate or low signal-to-noise ratios so that a single observation of the received data (even if one knew the precise signal arrival time) is not sufficient to provide a good estimate of the signal waveshape. Experimental results are described which show transient behavior waveform estimate. The transient behavior is expressed as the number of errors in the current estimate of the signal plotted vs. time. In a noisy environment, each ``learning' transient is a random time function. These learning transients are shown for several different signal-to-noise ratios and indicate the threshold noise levels for various types of initial states of the machine memory.     

Analysis of Modeling and Bias Errors in Discrete-Time State Estimation

This paper concerns the effects of modeling and bias errors in discrete-time state estimation. The newly derived algorithms include the effect of correlation between plant and measurement noise in the system. The effects of nonzero mean noise terms and bias errors are considered. With plant or measurement matrix errors, divergence can occur. The local or linear sensitivity approach to error analysis, where the sensitivity is defined as a partial derivative with respect to a variable parameter taken about the modeled value, will not show this divergence due to neglect of higher order terms. Approximate algorithms are presented which circumvent the problem inherent in the local sensitivity approach. These make use of a "conditional bias" concept which views system error as a bias, conditioned on knowledge of the state estimates. It is shown that the actual error in optimum estimation is orthogonal to the residue error for suboptimum estimation where the residue error is defined as the difference between the actual estimation error and the optimum estimation error. Two examples, one concerning an integrated navigation system, demonstrate the theoretical results.     

High-Resolution Frequency Measurement by Linear Prediction

The efficacy of linear prediction spectrum analysis (LPSA) (also called autoregressive or maximum entrophy spectrum analysis) for problems concerning narrowband signals in noise is examined by means of Monte Carlo simulation. For the case of pure tones (sinusoids) in white noise, both frequency estimation accuracy and resolution of two closely spaced tones with the linear prediction approach are discussed. In addition, estimation of the carrier frequency of a narrowband random (fading) Gaussian signal in noise is considered. The results are compared with standard fast Fourier transform (FFT) methods. These comparisons indicate that there is no significant advantage to using the linear prediction approach with real (bandpass) data. It is concluded that these experiments should be repeated with complex (baseband) data before a complete assessment of the LPS approach can be made.     

低成本GNSS终端中北斗三号多频观测的分析及应用CSCD

低成本消费级GNSS芯片相较于传统测绘级接收机在体积、功耗等方面具有突出优势,在未来无人驾驶、物联网等新兴应用领域具有极大潜力。然而当前低成本GNSS芯片的观测量存在非常严重的噪声和误差,为了改善观测质量,低成本芯片正逐渐从单频向多频过渡。首先调研了目前低成本GNSS接收机对多频信号的支持情况,然后介绍了北斗三号多频观测在电离层延迟和整周模糊度估计等方面的应用。通过对真实数据的采集和分析,针对当前原始观测量存在的诸多问题,提出了具体的预处理方案,并在此基础上开展了信号强度、电离层和模糊度实验。实验结果表明,基于华为Mate 40手机的低成本GNSS方案的北斗三频电离层延迟估计误差的均值为-2.97m,三频整周模糊度估计的成功率达93.8%,相较于单频整周模糊度估计的3.11%成功率有显著提升。     

翼身融合民机总体气动技术研究进展与展望

翼身融合（BWB）布局作为下一代亚声速民机主流方案已得到共识,研究步伐正在加快,进入工程应用指日可待。在回顾国内外BWB民机发展历程的基础上,简要阐述了飞翼布局（FW）和BWB布局的差异,明确了BWB概念特征及应用范围。聚焦BWB飞机总体气动设计中的技术挑战和对策,重点论述分析了BWB布局技术瓶颈及设计思想演化,新型结构与重量估算、适航符合性等总体设计问题,气动布局设计原则、高-低速性能协调等气动布局设计问题,飞-发干扰与一体化设计、新型发动机技术应用等飞机-发动机综合集成设计问题,降噪技术及其衍生的设计冲突、考虑噪声指标的总体设计策略等问题。并从技术进展和工程可实现性角度,展望了BWB民机的发展趋势。     

Monopulse-radar tracking of swerling III-IV targets using multiple observations

This paper proposes a novel statistical prediction of monopulse errors (Levanon, 1988) for a radar Swerling III-IV target embedded in noise or noise jamming where multiple observations are available. First, the study of the maximum likelihood estimator (MLE) of the complex monopulse ratio for a Swerling III-IV target embedded in spatially white noise allows us to extend the use of the MLE practical approximate form introduced by Mosca (1969) for Swerling 0-I-II cases. Afterward, we derive analytical formulas for both the mean and variance of the MLE in approximate form conditioned by the usual detection step performed on the sum channel of a monopulse antenna. Last, we provide a comparison of target direction of arrival (DOA) estimation performance based on monopulse ratio estimation as a function of the Swerling model in the context of a multifunction radar.     

ARMA Time Series Modeling: an Effective Method

The ability to generate rational models of time series plays an important role in such applications as adaptive filtering, spectral estimation, digital control, array processing, and forecasting. A method for effecting an autoregressive moving average (ARMA) model estimate is presented which possesses a number of admirable properties: 1) it has an elegant algebraic structure, 2) its modeling performance in spectral estimation applications has been empirically found to typically exceed that of such contemporary techniques as the periodogram, the Burg method, and the Box-Jenkins method on a variety of problems, 3) it is implementable by computationally efficient algorithms, and 4) it is based on pseudomaximum likelihood concepts. Taken in combination, these properties mark this method as being an effective tool in challenging applications requiring high modeling performance in a real time setting.