自跟踪陷波器

本文提出一种自动调谐陷波器,其陷波中心频率自动跟踪市电频率或某一外加干扰频率,以抑制其干扰.陷波器的谐振电路由LC组成,而电感L是用普遍阻抗转换器(GIC)来实现,GIC的端接电阻为压控MOS管电阻,因此可以用一电压控制此电阻以改变电感值,进而控制陷波频率.这种陷波电路可用于抑制市电频率的干扰噪声并防止测量或数据采集系统中前置放大器的饱和.实验结果表明在频率从4555Hz,其陷波频率跟踪精度优于0.5Hz,而陷波深度(串模抑制比)为3040dB.     

Frequency estimation of distorted non-stationary signals using complex H∞ filter

Mechanical vibration signals are always composed of harmonics of different order. A novel estimator is proposed for estimating the frequency of sinusoidal signals from measurements corrupted by White Gaussian noise with zero mean. Also low frequency sinusoidal signal is considered along with third and fifth order harmonics in presence of noise for estimating amplitudes and phases of different harmonics. The proposed estimator known as complex H_∞ filter is applied to a noisy sinusoidal signal model. State space modeling with two and three state variables is used for estimation of frequency in presence of white noise. Various comparisons in terms of simulation results for time varying frequency reveal that the proposed adaptive filter has significant improvement in noise rejection and estimation accuracy. Comparison in performance between two and three states modeling is presented in terms of mean square error (MSE) under different SNR conditions .The computer simulations clearly indicate that two states modeling based on Hilbert transform performs better than three states modeling under high noisy condition. Frequency estimation performance of the proposed filter is also being compared with extended complex Kalman filter (ECKF) under same noisy conditions through simulations.     

基于级联自适应陷波器的正弦波频率估计

常用的传统和现代方法对于噪声环境中正弦信号的频率估计均存在一定的缺陷。本文利用二阶陷波器级联构造自适应陷波器,实现对多个信号频率的估计;采用递推误差信号和递推最小二乘法同时优化各个二阶陷波器的参数。仿真结果表明,该方法对于单频、多频和频率变化等情况均具有较好的估计结果,且对于噪声干扰具有较强的鲁棒性。     

Car tracking by quantised input LMS, QX-LMS algorithm in traffic scenes

The tracking algorithm is an important tool for motion analysis in computer vision. A new car tracking algorithm is proposed which is based on a new clipping technique in the field of adaptive filter algorithms. The uncertainty and occlusion of vehicles increase the noise in vehicle tracking in a traffic scene, so the new clipping technique can control noise in prediction of vehicle positions. The authors present a new quantised version of the LMS, namely the QX-LMS algorithm, which has a better tracking capability in comparison with the clipped LMS (CLMS) and the LMS and also involves less computation. The threshold parameter of the QX-LMS algorithm causes controllability and the increase of tracking and convergence properties, whereas the CLMS and LMS algorithms do not have these capabilities. The QX-LMS algorithm is used for estimation of a noisy chirp signal, for system identification and in car tracking applications. Simulation results for noisy chirp signal detection show that this algorithm yields a considerable error reduction in comparison to the LMS and CLMS algorithms. The proposed algorithm, in tracking some 77 vehicles in different traffic scenes, shows a reduction of the tracking error relative to the LMS and CLMS algorithms.     

基于直扩的伪码频偏估计

提出了一种基于直接序列扩频信号的伪码频偏估计方法。介绍了解扩原理,包括中频采样、数字下变频、相关累积、伪码频率估计、环路滤波和伪码跟踪等。分析了伪码频偏估计的具体方法:伪码搜索的方法实现伪码大频偏的粗略估计;用锁相环实现伪码的同步;对锁相环的相位误差的提取,环路滤波器的结构和参数给出了具体的实现方法和表达式,并对环路等效噪声带宽的选取及其影响进行了讨论。     

无线信道中基于代价函数的OFDM频偏跟踪算法

在OFDM系统中,频偏对系统的性能有很大的影响。本文推导出两种新的实际频偏和估计频偏的差值表达式, 在此基础上,提出了相应的基于代价函数的自适应频偏跟踪算法,并在多径瑞利衰落信道中针对多种时变频偏的情况进行了计算机仿真,获得了频偏跟踪曲线和均方误差曲线。仿真结果表明,由于算法的快速收敛特性,取得了较好的频偏跟踪性能。它们克服了非自适应方法和跟踪缓慢的自适应算法难以适应时变频偏的缺点。将跟踪到的频偏在接收端补偿以后, 可以减小子载波间串扰,降低系统解调后的误码率。     

基于无噪后验错误矢量信号能量的变正则因子仿射投影算法

变正则因子技术是提高仿射投影自适应算法性能的重要方法之一.由于环境噪声的影响,现有的变正则因子自适应算法收敛速度较慢且稳态误差较大,各种测量、评估误差的存在进一步恶化了算法性能.为提高自适应算法的跟踪性能,本文在分析无噪先验错误矢量、无噪后验错误矢量和额外均方错误间关系的基础上,提出通过最小化无噪后验错误矢量信号能量来推导自适应变正则因子表达式的方法.在实践应用中,该方法利用了测量噪声的统计方差特性,并提出一种更加光滑且更加容易控制的指数缩放因子评估方法.系统辨识的仿真结果表明本文方法与传统的变正则因子方法以及变步长方法相比有更快的收敛速度与更低的稳态误差.     

自适应非线性GM-PHD滤波及在无源跟踪中的应用

首先针对无源传感器目标跟踪中的非线性问题,将高斯-厄米特求积分规则运用于高斯混合概率假设密度滤波,提出一种求积分卡尔曼概率假设密度滤波。其次,针对未知时变过程噪声,将基于极大后验估计原理的噪声估计器运用到概率假设密度滤波中,同时依据目标状态一步预测与状态滤波结果之间的残差,提出一种对滤波发散情况判断和抑制的算法。最后通过无源传感器双站跟踪仿真表明:相较于已有的非线性高斯混合概率假设密度滤波,所提算法有更高的精度,并且在未知时变噪声环境中具有较好跟踪效果。     

CO-OFDM系统中基于卡尔曼滤波对相位噪声补偿算法的研究

基于卡尔曼滤波提出了两种相干光正交频分复用(CO-OFDM)系统的相位噪声补偿算法,这两种算法在发射端的时域均插入导频,并在接收端对导频进行卡尔曼滤波,最后利用插值算法补全全部子载波的相位噪声.仿真结果表明,基于最小均方误差(MMSE)准则的判决反馈算法在相位噪声比率为10-1时,系统误码率约为10-4,并且出现了错误平层,而基于卡尔曼滤波所提出的两种相位噪声算法在大相位噪声的情况下仍然具有较好性能且能有效地降低错误平层,因而所提出的相位噪声补偿算法能改善CO-OFDM系统的性能.     

Adaptive MAPSD algorithms for symbol and timing recovery of mobileradio TDMA signals

Dual-mode adaptive algorithms with rapid convergence properties are presented for the equalization of frequency selective fading channels and the recovery of time-division multiple access (TDMA) mobile radio signals. The dual-mode structure consists of an auxiliary adaptive filter that estimates the channel during the training cycle. The converged filter weights are used to initialize a parallel bank of filters that are adapted blindly during the data cycle. When the symbol timing is known, this filter bank generates error residuals that are used to perform approximate maximum a posteriori symbol detection (MAPSD) and provide reliable decisions of the transmitted signal. For channels with timing jitter, joint estimation of the channel parameters and the symbol timing using an extended Kalman filter algorithm is proposed. Various methods are described to reduce the computational complexity of the MAP detector, usually at the cost of some performance degradation. Also, a blind MAPSD algorithm for combining signals from spatially diverse receivers is derived. This diversity MAPSD (DMAPSD) algorithm, which can be easily modified for the dual-mode TDMA application, maintains a global set of MAP metrics even while blindly tracking the individual spatial channels using local error estimates. The performance of these single-channel and diversity MAPSD dual-mode algorithms are studied via computer simulations for various channel models, including a mobile radio channel simulator for the IS-54 digital cellular TDMA standard     

Extensions of the SMC-PHD filters for jump Markov systems

The probability hypothesis density (PHD) filter is a promising algorithm for multitarget tracking, which can be extended for jump Markov systems (JMS). Since the existing multiple model sequential Monte Carlo PHD (MM SMC-PHD) filter is not interacting, two extensions of the SMC-PHD filters are developed in this paper. The interacting multiple-model (IMM) SMC-PHD filter approximates the model conditional PHD of target states by particles, and performs the interaction by resampling without any a priori assumption of the noise. The IMM Rao-Blackwellized particle (RBP) PHD filter uses the idea of Rao-Blackwellized to further enhance the performance of target state estimation for JMS with mixed linear/nonlinear state space models. The simulation results show that the proposed algorithms have better performances than the existing MM SMC-PHD filter in terms of state filtering and target number estimation.     

一种残差模糊匹配的非线性目标跟踪改进算法

为解决目标跟踪中因系统滤波初值不准确和噪声统计特性未知引起标准非线性卡尔曼算法估计误差变大问题,该文提出一种基于残差的模糊自适应(RTSFA)非线性目标跟踪算法。在确定采样型滤波基本框架的基础上,给出了在线性化误差约束条件下高斯权值的积分一般形式,并利用李雅普诺夫第二方法证明了该算法估计误差有界收敛的充分条件。进一步构建自适应噪声协方差矩阵在线估计噪声特性,并引入Takagi-Sugeno模型和量测椭球界限规则选择噪声估计器调节因子,有效提高了算法的收敛速度和滤波精度。通过滤波初值信息不明和量测噪声时变的纯方位目标跟踪模型,验证了非线性目标跟踪算法具有更好的跟踪精度和更强的鲁棒性。     

Comparative study of four adaptive frequency trackers

We study and compare four algorithms for adaptive retrieval of slowly time-varying multiple cisoids in noise: the adaptive notch filter, the multiple frequency tracker, the adaptive estimation scheme, and the hyperstable adaptive line enhancer. The local behavior of the algorithms in a neighborhood of their equilibrium state [assuming high signal-to-noise ratio (SNR) and large data sample] for a two-cisoid signal is treated in a similar way to the linear filter approximation technique used for a single-cisoid case. The validity of the results is confirmed by computer simulations     

A DS-CDMA tracking mode receiver with joint channel/delayestimation and MMSE detection

A tracking mode receiver for asynchronous direct-sequence CDMA is presented based on the extended Kalman filter (EKF). The EKF jointly estimates the delays and multipath coefficients of the received CDMA waveform, and provides a modified minimum mean-square error (MMSE) estimate of the user data (MMSE-EKF). In order to obtain a practical algorithm, each user signal is tracked individually, with the remaining users modeled as colored Gaussian noise. However, the EKFs are coupled through the multiple access interference (MAI) covariance estimates. In order to obtain meaningful performance measures, approximate worst-case undesired user delays that minimize the desired user SNR and delay estimation Cramer-Rao bound are obtained. It is shown that such worst-case delays can be efficiently computed using the alternating maximization (A-M) algorithm. The resulting bit error rate (BER) performance of the MMSE-EKF tracking receiver is evaluated through a combination of simulation and analysis. The mean-time to lose lock (MTTLL) for a genie-aided EKF delay estimator is also obtained using the A-M computed delays     

On-line estimation of signal and noise parameters with application to adaptive Kalman filtering

This paper presents two algorithms for on-line estimation of the optimal gain of the Kalman filter applied to sensor signals when the signal-to-noise ratio is unknown. First-order spectra of a pure signal and colored measurement noise have been assumed. The proposed adaptive Kalman filtering algorithms have been tested for various spectra of the pure signal and noise, and for various signal-to-noise ratios. The effect of the length of an adaptation step and a sampling frequency on the mean square errors of the pure signal estimation has also been examined. Although the test have been performed for stationary signals, the algorithms presented can also be used successfully for time-varying sensor signals when the signal-to-noise ratios vary very slowly in comparison with the length of the adaptation step.The results are helpful for designers who synthesize optimal linear digital filters for sensor signals with first-order spectra and colored measurement noise. The estimation error curves presented enable designers to determine the noise reduction attainable for particular applications of the adaptive Kalman filtering algorithms.     

Estimation and compensation for the influence of interchannel interference in reception of OFDM signals

The algorithms intended for estimating and compensating for the distortions of orthogonal frequency-division multiplexing signals are discussed under the conditions of reception in frequency-selective fading channels. The results of computer simulation thereof are presented. These algorithms are based on a multidimensional digital Kalman filter (DKF) and involve interpolation of the channel frequency response estimates obtained with the help of the DKF and compensation for distortions, which are optimal in terms of the minimum mean squared error, and estimation of the variance of noise and disturbance induced by interchannel interference via Doppler spectrum analysis.     

Robust and Improved Channel Estimation Algorithm for MIMO-OFDM Systems

Multiple-input multiple-output (MIMO) system using orthogonal frequency division multiplexing (OFDM) technique has become a promising method for reliable high data-rate wireless transmission system in which the channel is dispersive in both time and frequency domains. Due to multiple cochannel interferences in a MIMO system, the accuracy of channel estimation is a vital factor for proper receiver design in order to realize the full potential performance of the MIMO-OFDM system. A robust and improved channel estimation algorithm is proposed in this paper for MIMO-OFDM systems based on the least squares (LS) algorithm. The proposed algorithm, called improved LS (ILS), employs the noise correlation in order to reduce the variance of the LS estimation error by estimating and suppressing the noise in signal subspace. The performance of the ILS channel estimation algorithm is robust to the number of antennas in transmit and receive sides. The new algorithm attains a significant improvement in performance in comparison with that of the regular LS estimator. Also, with respect to mean square error criterion and without using channel statistics, the ILS algorithm achieves a performance very close to that of the minimum mean square error (MMSE) estimator in terms of the parameters used in practical MIMO-OFDM systems. A modification of the ILS algorithm, called modified ILS (MILS), is proposed based on using the second order statistical parameters of channel. Analytically, it is shown that the MILS estimator achieves the exact performance of the MMSE estimator. Due to no specific data sequences being required to perform the estimation, in addition to the training mode, the proposed channel estimation algorithms can also be extended and used in the tracking mode with decision-aided method.     

A Non-Pilot Aided Iterative Carrier Frequency Offset Estimator Using Optimal Filtering for an Interleaved OFDMA Uplink System

In an uplink transmission of a coded orthogonal frequency division multiple access (C-OFDMA) system, channel estimation, time and frequency synchronization has to be addressed. For this purpose a control data, i.e. a known training sequence called “preamble” and pilot sub-carriers are used. As an alternative to the classic scheme and in order to maximize the data rate, we propose a non-pilot aided estimator based on an iterative architecture that does not require pilot sub-carriers. Our approach combines 1/ the so-called minimum mean square error successive detector to estimate the signal sent by each user 2/ a recursive method estimating the CFOs. Various algorithms such as the extended Kalman filter, the sigma-point Kalman filters and the extended H _∞ filter are tested and their performances are compared in terms of convergence speed and estimation accuracy. When considering an interleaved OFDMA uplink system over a Rayleigh fading channel, simulation results clearly show the efficiency of the proposed algorithm in terms of CFO estimation and bit error rate performances.     

机动目标DOA跟踪粒子滤波算法

针对标准粒子滤波算法在机动目标波达方向(direction of arrival, DOA)随时间快速变化导致跟踪精度下降、实时性变差及多目标跟踪误差大等不足的问题,本文提出了一种改进粒子滤波(particle filter, PF)算法。该算法依据阵列信号处理模型和匀速(constant velocity,CV)模型,建立了机动目标跟踪的状态方程和观测方程作为状态空间模型,并在此基础上,借鉴多重信号分类(multiple signal classification,MUSIC)算法谱函数修改了粒子滤波的似然函数,实现了对目标方位的实时动态跟踪。仿真结果表明,与传统子空间类跟踪算法和标准粒子滤波算法相比,本文方法跟踪精度更高,收敛速度更快,抗噪能力及鲁棒性更强,对轨迹交叉的多目标跟踪性能也更优。      

Set-membership adaptive equalization and an updator-sharedimplementation for multiple channel communications systems

This paper considers the problems of channel estimation and adaptive equalization in the novel framework of set-membership parameter estimation. Channel estimation using a class of set-membership identification algorithms known as optimal bounding ellipsoid (OBE) algorithms and their extension to tracking time-varying channels are described. Simulation results show that the OBE channel estimators outperform the least-mean-square (LMS) algorithm and perform comparably with the RLS and the Kalman filter. The concept of set-membership equalization is introduced along with the notion of a feasible equalizer. Necessary and sufficient conditions are derived for the existence of feasible equalizers in the case of linear equalization for a linear FIR additive noise channel. An adaptive OBE algorithm is shown to provide a set of estimated feasible equalizers. The selective update feature of the OBE algorithms is exploited to devise an updator-shared scheme in a multiple channel environment, referred to as updator-shared parallel adaptive equalization (USHAPE). U-SHAPE is shown to reduce the hardware complexity significantly. Procedures to compute the minimum number of updating processors required for a specified quality of service are presented