Speech Data Rate Reduction Part I: Applicability of Modern Estimation Theory

Efficient coding of continuous speech signals for digital representation has attracted much interest in recent years. The underlying aim of efficient coding methods is to reduce the channel capacity required to represent a signal to meet a specific reconstruction fidelity criterion. To achieve this objective, modern speech data compression techniques rely on two very similar procedures. One procedure uses predictive deconvolution which subtracts from the current signal value that portion which can be predicted from its past and thus removes redundancy in the speech by removing sequential correlation. The signal thus requires fewer bits for equivalent quantization error. The second procedure involves identification of a complete mathematical model of the speech producing mechanism. This involves determination of the characteristics of the source that drives this transfer function. Data reduction is again achieved since the rate of change of the parameters of the speech model is much smaller than the rate of change of the speech waveform. This paper develops these data reduction procedures in terms of modern estimation theory, specifically a Kalman filter model, and illustrates the utility of this model as an analysis tool by means of an example based on a uniform tube which provides a qualitative assessment of the potential of the technique for application to real speech signals.     

直升机火控系统精度敏感性分析的BNSobol法

针对现有研究大都只分析了单误差源对直升机火控系统精度影响的现状,提出进行直升机火控系统精度的敏感性分析。根据传统敏感性分析法在数据量不充分或无法提供精确模型时存在的局限性,对基于贝叶斯网络和Sobol指数相结合的敏感性分析新方法（BNSobol法）进行了研究,并与传统Sobol法对比分析。结果表明BNSobol法在满足精度要求的同时还减小了对数据量的需求,它的分析不依赖于模型且可以确定出满足约束条件的具体误差源取值区间。该方法可为直升机火控系统设计研究时如何分配各模块误差大小从而提高总体效能提供参考和理论支持。     

Geometrical entropy approach for variable structure multiple-model estimation

The variable structure multiple-model(VSMM) estimation approach, one of the multiple-model(MM) estimation approaches, is popular in handling state estimation problems with mode uncertainties.In the VSMM algorithms, the model sequence set adaptation(MSA) plays a key role.The MSA methods are challenged in both theory and practice for the target modes and the real observation error distributions are usually uncertain in practice.In this paper, a geometrical entropy(GE) measure is proposed so that the MSA is achieved on the minimum geometrical entropy(MGE) principle.Consequently, the minimum geometrical entropy multiple-model(MGEMM) framework is proposed, and two suboptimal algorithms, the particle filter k-means minimum geometrical entropy multiple-model algorithm(PF-KMGEMM) as well as the particle filter adaptive minimum geometrical entropy multiple-model algorithm(PF-AMGEMM), are established for practical applications.The proposed algorithms are tested in three groups of maneuvering target tracking scenarios with mode and observation error distribution uncertainties.Numerical simulations have demonstrated that compared to several existing algorithms, the MGE-based algorithms can achieve more robust and accurate estimation results when the real observation error is inconsistent with a priori.     

Performance Bounds for Continuous-time Filters in the Presence of Modeling Errors

We are concerned with obtaining bounds on the performance of Kalman-type, linear, continuous-time filters susceptible to modeling errors. Limiting the discussion to stationary performance, we obtain bounds on the performance index, the mean square error of estimates for suboptimal and optimal (Kalman) filters. The bounds are expressed in terms of the model matrices and the range of errors of the matrices. The results are useful to a designer in comparing the performance of a suboptimal filter with that of the optimal filter when he has information on the range of modeling errors. The tightness of the bounds is shown by an application of the results in the estimation of the motion of an aircraft carrier at sea.     

Sensitivity Reduction by Double Perfect Model Following

We propose a double perfect model following (DPMF) system which uses two models: one is used to define the desirable response and the other is used to reduce sensitivity to parameter perturbation. The concept is shown and methods of model selection and controller synthesis are discussed, followed by numerical examples and an aircraft control example which demonstrate performance of the DPMF concept. It is shown that the DPMF system can be less sensitive than a nominally equivalent single perfect model following (SPMF) system and that sensitivity reduction is enhanced by proper selection of the model. It is also shown that the sensitivity of multiinput systems can be reduced componentwise or blockwise by using a decoupled type model.     

准实时性前斜视INS/SAR组合导航滤波算法研究

为消除常规利用正侧视SAR图像辅助导航信息对惯性误差进行修正时的滞后影响,结合前斜视SAR成像工作特性,本文分析提出了新的准实时性组合导航滤波方案和算法,以解决SAR/INS组合导航系统中的量测滞后影响;本文分析了前斜视SAR成像辅助导航系统工作特点,分析了SAR量测输出模型;建立了准实时性前斜视INS/SAR组合导航滤波算法模型,并通过仿真分析了组合导航系统性能。仿真结果表明：采用所提出的惯性/SAR组合方案和算法,可以有效克服成规正侧视SAR辅助匹配输出滞后的影响,有效满足组合系统性能要求。     

一种基于盲同态解卷积的SAR成像自聚焦算法

针对大气扰动及飞行平台不稳引起机载合成孔径雷达(SAR)图像散焦的问题,提出了一种新的自聚焦算法来估算运动误差。该算法是基于运动误差函数及场景散射函数平滑特性的差异来进行估计的,利用复对数变换将图像的幅度与相位信息分离,进而分别对相位及幅度信息进行处理。运动误差通常为慢变函数,而场景的散射信息具有某种随机特性。因此,经过复对数变换后,运动误差及散射信息可以通过滤波器进行分离,将相位中的随机噪声去除,从而保留了慢变的运动误差函数。为了去除噪声信息,需要建立一个平滑滤波器,利用Daubechies小波的尺度函数构造Riesz基向量,从而建立了正交子空间,通过所建立信号子空间及噪声子空间组建平滑滤波器,最终可以获得准确的运动误差。在实验部分,分别利用仿真数据及实测数据对本文方法进行验证,最终结果分析表明该方法具有很高的估计精度及执行效率。     

基于参数不确定性的MAV模型线化方法研究

微型飞行器(MAV)体积小、重量轻,设计参数与飞行状态参数的微小变化对其飞行性能影响很大,MAV飞行控制器的设计必须考虑这些不确定性因素。本文对此进行了分析,讨论了对具有参数不确定性的MAV系统进行线性化处理的方法与步骤,针对某无尾MAV的一组优化设计开展了相关研究。     

Adaptive robust cubature Kalman filtering for satellite attitude estimation

This paper is concerned with the adaptive robust cubature Kalman filtering problem for the case that the dynamics model error and the measurement model error exist simultaneously in the satellite attitude estimation system. By using Hubel-based robust filtering methodology to correct the measurement covariance formulation of cubature Kalman filter, the proposed filtering algorithm could effectively suppress the measurement model error. To further enhance this effect and reduce the impact of the dynamics model error, two different adaptively robust filtering algorithms, one with the optimal adaptive factor based on the estimated covariance matrix of the predicted residuals and the other with multiple fading factors based on strong tracking algorithm, are developed and applied for the satellite attitude estimation. The quaternion is employed to represent the global attitude parameter, and three-dimensional generalized Rodrigues parameters are introduced to define the local attitude error. A multiplicative quaternion error is derived from the local attitude error to maintain quaternion normalization constraint in the filter. Simulation results indicate that the proposed novel algorithm could exhibit higher accuracy and faster convergence compared with the multiplicative extended Kalman filter, the unscented quaternion estimator, and the adaptive robust unscented Kalman filter.     

Structures for radar detection in compound Gaussian clutter

The problem of coherent radar target detection in a background of non-Gaussian clutter modeled by a compound Gaussian distribution is studied here. We show how the likelihood ratio may be recast into an estimator-correlator form that shows that an essential feature of the optimal detector is to compute an optimum estimate of the reciprocal of the unknown random local power level. We then proceed to show that the optimal detector may be recast into yet another form, namely a matched filter compared with a data-dependent threshold. With these reformulations of the optimal detector, the problem of obtaining suboptimal detectors may be systematically studied by either approximating the likelihood ratio directly, utilizing a suboptimal estimate in the estimator-correlator structure or utilizing a suboptimal function to model the data-dependent threshold in the matched filter interpretation. Each of these approaches is studied to obtain suboptimal detectors. The results indicate that for processing small numbers of pulses, a suboptimal detector that utilizes information about the nature of the non-Gaussian clutter can be implemented to obtain quasi-optimal performance. As the number of pulses to be processed increases, a suboptimal detector that does not require information about the specific nature of the non-Gaussian clutter may be implemented to obtain quasi-optimal performance     

VTOL Flight-Control System Design Using Sensitivity Analysis

This paper considers the use of sensitivity and error analysis algorithms for combined estimation and control systems to conduct trade-off studies for sensor equipment packages for a simple longitudinal inal motion VTOL hover controller. Simulation results indicate that in many problems the extra effort expended in model identification ation is justified in order to reduce the sensitivity of the system performance to implementation tolerances. The results of the hover controller design problem demonstrate the use of the algorithms as an aid in design decision making.     

巴特沃兹有源滤波器设计中的灵敏度分析

通过对两种巴特沃兹有源滤波器设计方案的灵敏度分析,仿真了电路元件参数灵敏度随 Q 因数的变化规律,从而介绍了一种利用灵敏度分析比较网络设计方案优劣的方法。     

A novel optimal data fusion algorithm and its application for the integrated navigation system of missile

For Inertial Navigation System(INS)/Celestial Navigation System(CNS)/Global Navigation Satellite System(GNSS) integrated navigation system of the missile, the performance of data fusion algorithms based on the Cubature Kalman Filter(CKF) is seriously degraded when there are non-Gaussian noise and process-modeling errors in the system model. Therefore, a novel method is proposed, which is called Optimal Data Fusion algorithm based on the Adaptive Fading maximum Correntropy generalized high-degree...     

再论联邦滤波在组合导航中的应用可行性

在联邦滤波算法中，针对子滤波器存在私有状态从而导致全局状态信息融合估计为次优估计的问题，通过一个简单的3维状态、2维量测系统的数值仿真以及速度+姿态传递对准组合导航仿真，验证了联邦滤波的次优估计结果误差太大，难以满足实际系统的高精度滤波需求。联邦滤波估计的次优程度没有经过严格证明，且难以证明，因而存在应用风险。对于以精度为主要性能指标的组合导航系统，不建议采用联邦滤波算法，否则其精度损失不足以弥补其带来的计算量降低和容错性提高的优势。     

Evaluation of multiple radar target trackers in stressfulenvironments

This paper evaluates the performance of multiple target tracking (MTT) algorithms in real-life stressful radar tracking environments. Real closely spaced maneuver radar data, generated by six F-18 fighters and other targets, were collected jointly by the defence departments of Canada and United States to support this practical MTT algorithm evaluation study. A set of performance metrics was defined here to compare the suboptimal nearest neighbor (SNN), global nearest neighbor (GNN), and various variants of the joint probabilistic data association (JPDA) MTT trackers. Results reveal an interesting result that all these MTT algorithms exhibited very close performance. In addition, the weighted sum approach of the PDA/JPDA trackers which are theoretically effective were observed to perform poorly in tracking closely spaced targets. Overall speaking, the GNN filter based on the Munkres algorithm had the best performance in terms of both tracking performance and robustness     

基于贝叶斯理论的低循环疲劳寿命模型不确定性量化

为量化低循环疲劳寿命模型中的不确定性因素,利用贝叶斯理论,采用经典的模型校准形式确立了寿命模型的不确定性量化形式,并结合正态性检验对误差项进行验证;应用马尔可夫链-蒙特卡罗(MCMC)算法获得了模型参数后验分布的抽样样本,在小子样试验数据条件下确定了低循环疲劳寿命的95%不确定性区间,较好地覆盖了寿命的分散性;对参数样本进行了相关性分析,并将异方差回归概率模型与贝叶斯概率模型进行了比较。最后,利用Morris全局灵敏度分析方法获得了Manson-Coffin模型参数的全局灵敏度指标;同时,验证了在模型参数对先验信息敏感,或者说在先验信息影响极大的情况下,采用无信息先验处理方法的合理性。     

Enhanced variable dimension filter for maneuvering target tracking

An enhancement of the variable dimension (VD) filter for maneuvering-target tracking is presented. The use of measurement concatenation, a procedure whereby fast sampled measurements are stacked while maintaining their proper relationships with the states, leads to significant reduction in estimation error by low processing rate algorithms. The use of double decision logic (DDL) for the maneuver onset and ending detection as well as appropriate procedures for reinitialization of the estimation filters results in improved maneuver detection and filter adaptation. Simulation results show the performance of the proposed enhanced variable dimension (EVD) filter     

检波器谐振频率数学模型及其频响误差的修正

峰值检波器的频响误差在高频段主要是由分布参数产生的高频谐振误差。本文利用实测数据建立了检波器谐振频率数学模型，根据模型的预测值对检波器和了频响误差修正。计量检定表明修正后的频响误差大大减小。     

Suboptimal filter design with pseudomeasurements for targettracking

A suboptimal Kalman filter design method is presented for the problem of tracking a maneuvering target. The design method is essentially based on linear target dynamics and linear-like structured measurements called pseudomeasurements. The pseudomeasurements are obtained by manipulating the original nonlinear measurements algebraically. The resulting filter has computational advantages over other filters with similar performance. Also, a variant of the Berg model is proposed as a target acceleration model under the assumption of a coordinated turn maneuver. The proposed model is consistent with the underlying assumption. Monte Carlo computer simulation results are included to demonstrate the effectiveness of the proposed suboptimal filter associated with the target acceleration model     

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.