Interacting multiple model fixed-lag smoothing algorithm forMarkovian switching systems

We investigate a suboptimal approach to the fixed-lag smoothing problem for Markovian switching systems. A fixed-lag smoothing algorithm is developed by applying the basic Interacting Multiple Model (IMM) approach to a state-augmented system. The computational load is roughly d (the fixed lag) times beyond that of filtering for the original system. In addition, an algorithm that approximates the “fixed-lag” mode probabilities given measurements up to current time is proposed. The algorithm is illustrated via a target tracking simulation example where a significant improvement over the filtering algorithm is achieved at the cost of a time delay (i.e., data up to time k are used to produce the smoothed state estimate at time k-d where the fixed large d>0). the IMM fixed-lag smoothing performance for the given example is comparable to that of an existing IMM fixed-interval smoother. Compared with fixed-interval smoothers, the fixed-lag smoothers can be implemented in real-time with a small delay     

On Finite-Lag Receivers for Fading Channels

This paper reports some results on the development of finite-lag receivers for reception via fading channels. The receivers are given in discrete form and clearly show the additional complexity due to introduction of delay between the instants of reception and estimation of the signal. The channel noise has been assumed to have a multiplicative component due to fading. This results in the optimal demodulators being nonlinear and unrealizable. However, the receivers proposed are suboptimal, but realizable. Simulation studies evaluating the performance of the various receivers are also discussed.     

On the Sensitivity of a Fixed-Point Smoothing Algorithm

This paper presents a simple approach to the derivation of sensitivity measures of smoothing algorithms. It is proposed that the known sensitivity results of the Kalman filtering algorithm be utilized along with the state augmentation approach for this purpose. The sensitivity measures so obtained are easier to compute than the ones available in the literature. It is then shown that the fixed-point smoothing algorithm, derived recently by Biswas and Mahalanabis [1], [2], is less sensitive to model parameter variations than the algorithm studied by Griffin and Sage [7], [8]. The case of a satellite tracking problem is presented by way of illustrating the results.     

Suboptimal Algorithms for Nonlinear Smoothing

This paper presents second-order algorithms for fixed- point and fixed-lag smoothing problems for nonlinear systems. The algorithms are obtained on the basis of the state augmentation approach, used earlier by the authors, for dealing with linear smoothing problems. Both discrete-and continuous-time forms of solutions are presented and the results are illustrated by considering a radar tracking problem. Some of the advantages of the proposed smoothing solutions compared to existing ones are also discussed.     

A simple fixed-lag algorithm for tracking frequency rate-of-change

A family of simple fixed-lag frequency smoothing algorithms which provide good estimates of both frequency and frequency rate-of-change is reported. The smoothers were developed from a fixed-gain αβ tracker by replacing the recursive derivative estimator with a finite-impulse-response (FIR) differentiator. Simulation results are presented which show that the smoothing algorithms provide frequency estimates with a similar variance to those produced by the αβ filter but with greatly improved frequency-rate estimates. The smoothing algorithms and the αβ filter are also compared on the basis of the bias and delay introduced in the frequency-rate estimates. Although the results presented are for frequency estimation, the smoothing algorithms can be used in any single-input tracking application where some lag in the estimates is allowable     

Further Results on Multipath Angle Error Reduction Using Multiple-Target Methods

The two-target technique proposed by the author in an earlier paper [1] for reducing radar multipath angle tracking errors has been simulated on a digital computer assuming an actual closed-loop system. When tracking with noise, the technique provides angle error performance which compares quite favorably with the expected performance given in [1] Furthermore, the large bias errors usually encountered in normal monopulse systems at low elevation angles are removed. Results of typical tracks are given, both for the method of [1], and for a modified version of the method which applies primarily to shipboard radar systems. Some results on loss of lock are also presented.     

Improved multi-target tracking in clutter by PDA smoothing

An algorithm is presented for the recursive tracking of multiple targets in cluttered environment by making use of the joint probabilistic data association fixed-lag smoothing (JPDAS) techniques. It is shown that a significant improvement in the accuracy of track estimation of both nonmaneuvering and maneuvering targets may be achieved by introducing a time lag of one or two sampling periods between the instants of estimation and latest measurement. Results of simulation experiments for a radar tracking problem that demonstrate the effects of fixed-lag smoothing are also presented     

特征结构配置法及其在飞行控制系统设计中的应用

 本文研究了状态反馈系统的特征结构配置（Eigenstructure Assignment）问题。文中提出了配置具有多重特征值及复共轭特征值的特征结构的算法。并以某飞行控制系统的设计为例说明了算法的应用;最后还给出了设计方案的数字～模拟计算机仿真实验结果。     

Comments on "on Probability of Error in Decision Feedback Equalizer"

The above-mentioned paper [1] derives bounds on the error probability of a decision feedback equalizer (DFE) which are claimed to be significantly tighter than the earlier upper bound of [2]. Unfortunately, [1] makes an unsupportable assumption which reddars the conclusions incorrect. We show that valid bounds can be obtained by the methods of [1], but that for the numerical example of [2] , the resulting upper bound is never tighter than, and sometimes significantly weaker than, that derived in [2]. Similarly, a lower bound derived by the method of [1] is shown for the same numerical example to be very close to the error-free past lower bound previously known.     

传感器位置误差条件下的约束总体最小二乘时差定位算法

 现代定位系统中,传感器往往被安放在运动平台上,其位置无法精确得知,存在估计误差,将严重影响对目标的定位精度。针对这一问题,提出基于约束总体最小二乘(CTLS)的到达时差(TDOA)定位算法。首先通过引入中间变量,将非线性TDOA定位方程转化为伪线性方程,再利用CTLS技术,全面考虑伪线性方程所有系数中的噪声。在此基础上推导了定位方程的目标函数,再根据牛顿迭代方法,进行数值迭代,快速得到精确解。采用一阶小噪声扰动分析方法,对该算法的理论性能进行了分析,证明了算法的无偏性和逼近克拉美-罗下限(CRLB)。仿真实验表明,该算法克服了现有总体最小二乘(TLS)算法不能达到CRLB、两步加权最小二乘(two-step WLS)算法在较高噪声时性能发散的缺陷,在较高噪声时定位精度仍然能达到CRLB。     

剩余柔度模态与附着模态在模态综合法中的等价性

在早期的模态综合法中,引起计算误差的主要原因是由于截去了高阶模态。文献[1～4]从不同角度揭示,剩余柔度模态和附着模态可以有效地补偿这种截断误差。在此基础上,已派生出两类新的自由界面模态综合法。第一类方法的特点是将子结构位移用自由界面模态与剩余柔度模态来表示,第二类方法的特点是用自由界面模态与附着模态来表示子结构位移。本文将指出,这两类方法是等价的。其等价性表现在剩余柔度模态与附着模态可以相互替换而不影响模态综合的最终结果。本文将以文献[4]提出的方法为例来证明该等价性,并在文末给出一个数值算例。     

Kullback-Leibler Approach to Gaussian Mixture Reduction

A common problem in multi-target tracking is to approximate a Gaussian mixture by one containing fewer components; similar problems can arise in integrated navigation. A common approach is successively to merge pairs of components, replacing the pair with a single Gaussian component whose moments up to second order match those of the merged pair. Salmond [1] and Williams [2, 3] have each proposed algorithms along these lines, but using different criteria for selecting the pair to be merged at each stage. The paper shows how under certain circumstances each of these pair-selection criteria can give rise to anomalous behaviour, and proposes that a key consideration should the the Kullback-Leibler (KL) discrimination of the reduced mixture with respect to the original mixture. Although computing this directly would normally be impractical, the paper shows how an easily computed upper bound can be used as a pair-selection criterion which avoids the anomalies of the earlier approaches. The behaviour of the three algorithms is compared using a high-dimensional example drawn from terrain-referenced navigation.     

Classification of radar targets using synthetic neural networks

Radar target classification performance of neural networks is evaluated. Time-domain and frequency-domain target features are considered. The sensitivity of the neural network algorithm to changes in network topology and training noise level is examined. The problem of classifying radar targets at unknown aspect angles is considered. The performance of the neural network algorithms is compared with that of decision-theoretic classifiers. Neural networks can be effectively used as radar target classification algorithms with an expected performance within 10 dB (worst case) of the optimum classifier     

叶片空间造型的计算几何方法

 本文把新近发展起来的计算几何中的几种典型方法（包括Bézier曲面、B样条曲面和Bézier-B样条混合曲面等）,同航空发动机叶片空间造型结合起来,形成一套计算机辅助叶片外形设计的新方法。扭曲叶片由三二次混合曲面片拼接而成,型面有完整的数学模型。因此有利于叶片三元流场性能和有限元强度计算分析以及与数控加工相结合。实例表明该方法简单、灵活、计算量小,对大弯度叶片设计亦行之有效。     

Low-voltage CMOS Folded-cascode Mixer

The folded-cascode structure is used to realize the low-voltage low-power consumption mixer, whose performance parameters have big influence on the navigation radio receiver's performance. Adopting the folded-cascode structure, the folded-cascode mixer (FCM) has a lower power supply voltage of 1.2 V and realizes the design trade-offs among the high transconductance, high linearity and low noise. The difficulties of realizing the trade-offs between the linearity and noise performance, the linearity and conversion gain, the conversion gain and noise performance are reduced. Fabricated in an radio frequency (RF) 0.18 μm CMOS process, the FCM has an active area of about 200 μm ×150 μm and consumes approximate 3.9 mW. The test results show that the FCM features a conversion gain (Gc) of some 14.5 dB, an input 1 dB compression point (Pin-1dB) of almost -13 dBm and a dual sideband (DSB) noise figure of around 12 dB. The FCM can be applied to the navigation radio receivers and electronic systems for aviation and aerospace or other related fields.     

基于信息论匀质性测度的各向异性扩散相干斑抑制算法

 针对传统各向异性扩散抑斑算法存在的均匀区域噪声平滑不充分、边缘随迭代弱化及迭代次数的确定缺乏理论指导等问题,提出了一种新的各向异性扩散抑斑算法,该算法采用信息论匀质性测度作为图像中匀质区域与边缘的鉴别因子,使扩散系数能够更准确地控制扩散强度与扩散速率,从而达到充分平滑均匀区域噪声及保护边缘的目的。基于各向异性扩散方程求解与鲁棒误差范数最小化的等效性,提出了一种各向异性扩散方程的迭代停止准则。利用合成孔径雷达图像对本文算法的抑斑和边缘保持性能进行了仿真实验验证。结果表明,本文算法在均匀区域相干斑噪声抑制、边缘保持等方面均取得了优于传统算法的效果。     

Variable structure multiple model fixed-interval smoothing

This paper focuses on fixed-interval smoothing for stochastic hybrid systems. When the truth-mode mismatch is encountered, existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable. We develop a fixed-interval smoothing method based on forward- and backward-filtering in the Variable Structure Multiple Model (VSMM) framework in this paper. We propose to use the Simplified Equivalent model Interacting Multiple Model (SEIMM) in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters, and design a re-filtering procedure in the model-switching stage to enhance the estimation performance. To improve the computational efficiency, we make the basic model-set adaptive by the Likely-Model Set (LMS) algorithm. It turns out that the smoothing performance is further improved by the LMS due to less competition among models. Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.     

矩形波导远区散射场的光学分析

将高频光学法与广义散射矩阵技术相结合,分析计算了矩形波导在终端短路和终端介质材料两种状态下的远区电磁散射,计算与实测结果吻合较好,且优于维特的计算结果。本方法可用于飞行器进气道和尾喷管电磁散射特性的分析。     

An efficient algorithm of data smoothing in real-time calculations

We propose an algorithm for real-time data smoothing due to considerable reduction and simplification of computations by employing the recursive least mean squares technique. The efficiency of algorithms is shown by an example of determining the flight airspeed and altitude based on the data of pressure sensors.     

Estimation of Phase Difference in Multiple Sensor Arrays

The likelihood functional for estimating parameter differences in coherent multiple-sensor receivers is developed assuming Gaussian statistics on both signal and noise. The development relies on a matrix formulation and a subsequent factorization of a parameter constraint matrix from the signal matrix. A two-antenna phase-difference radar example is presented for cases of uncorrelated and antenna-correlated noise.