On identification and adaptive estimation for systems with interrupted observations

A linear discrete-time system with interrupted observations is considered. The interrupted observation mechanism is expressed in terms of a finite-state ergodic Markov chain. The transition probability matrix of the Markov chain and some system parameters may be unknown, but constant, and are assumed to belong to a compact set. A novel scheme, called truncated maximum likelihood estimation, is proposed for consistent estimation of the unknown parameters. Sufficient conditions for strong consistency are investigated. The truncated maximum likelihood procedure is computationally feasible whereas the standard maximum likelihood procedure is not, given large observation records. Finally, using the truncated ML algorithm, a suboptimal adaptive state estimator is proposed and its asymptotic behavior is analyzed.     

Adaptive estimation and identification for discrete systems with Markov jump parameters

Linear discrete-time stochastic dynamical systems with parameters which may switch among a finite set of values are considered. The switchings are modeled by a finite state ergodic Markov chain whose transition probability matrix is unknown and is assumed to belong to a compact set. A novel scheme, called truncated maximum likelihood estimation, is proposed for consistent estimation of the transition probabilities given noisy observations of the system output variables. Conditions for strong consistency are investigated assuming that the measurements are taken after the system has achieved a statistical steady state. The case when the true transition matrix does not belong to the unknown transition matrix set is also considered. The truncated maximum likelihood procedure is computationally feasible, whereas the standard maximum likelihood procedure is not, given large observation records. Finally, using the truncated ML algorithm, a suboptimal adaptive state estimator is proposed and its asymptotic behavior is analyzed.     

SIMO SC-FDE系统的CFR盲估计

提出了一种基于线性预测的单输入多输出单载波频域均衡系统频域信道响应(也称为信道频率响应,CFR)盲估计算法.与传统的线性预测时域信道估计方法不同,提出的算法不需要计算新息以及新息和输出序列的互相关,而是直接从预测滤波器系数获得频域信道响应估计的闭式解.算法仅采用输出序列的二阶统计量,对信道阶次过估计具有鲁棒性,并且估计性能优于传统的线性预测时域信道估计方法.计算机仿真结果验证了理论分析的正确性.     

SIMO信道下MPSK信号的载波相位盲估计

提出了一种SIMO信道下多个MPSK信号的载波相位联合盲估计算法,以提高低信噪比条件下载波相位的估计性能.该算法综合利用多路接收信号以去除调制信息对相位估计的影响,构建与各信号相位相关的多个方程,并最终通过加权最小二乘解算方法完成对信号相位的联合估计.仿真结果表明,与传统的V&V算法相比,本算法在低信噪比条件下具有更加...     

MIMO-OFDM系统中一种改进的最大似然信道估计算法*

对MIMO-OFDM系统中的信道估计进行研究,提出了一种对最大似然（ML）算法的改进,该算法首先采用ML算法获得初始估计值,然后联合检测进行迭代信道估计,充分利用上了接收端联合数据检测得到的数据信号信息与信道估计进行信息交互来提高估计性能,仿真结果表明,相对于传统估计方法,这种改进方法能够得到更好的均方误差和误码率性能,尤其是在导频数量较少时,此改进算法的性能提升将更明显。     

适用于含公共零点SIMO信道的盲均衡算法

在含公共零点单输入多输出(SIMO)模型的基础上,提出一种针对含公共零点的SIMO信道的直接无限冲击响应(IIR)盲均衡算法。该算法利用IIR预测均衡算法对输入信号进行初始均衡和对均衡结果进行相偏纠正,通过最小均方误差准则提高算法在高斯白噪声环境中的适应性,克服IIR预测算法中的相位偏转问题与IIR预测算法对信噪比敏感的缺点。仿真实验结果表明,该算法对IIR信道及含公共零点信道都具有较好的均衡效果。     

Pilot-assisted data recovery for OFDM systems with blind selected mapping scheme and space-frequency block coding

Pilot-assisted blind selected mapping (PB-SLM) scheme for multiple input single output (MISO) orthogonal frequency division multiplexing (OFDM) systems is proposed. At the receiver, individual values of autocorrelation of two consecutive pilot sub-channel responses are evaluated for all the phase sequences available at the transmitter. The phase sequence index corresponding to the highest value of autocorrelation is considered as the side information (SI) and used for data recovery. Due to the robust phase sequence selection based on pilot symbols, data decoding performance of the PB-SLM scheme is substantially better than that of the recent blind SLM (B-SLM) scheme for MISO OFDM systems with space-frequency block coding, which is based on sub-optimal maximum likelihood of data symbols, and almost identical with that by the simplified SLM scheme with perfect SI. Moreover, computational complexity is significantly lower than that of the B-SLM scheme.     

基于极大似然准则和最大期望算法的自适应UKF 算法

针对噪声先验统计特性未知情况下的非线性系统状态估计问题,提出了基于极大似然准则和 最大期望算法的自适应无迹卡尔曼滤波(Unscented Kalman filter, UKF) 算法.利用极大似然准则构造含有噪声统计特性的对数似然函数,通 过最大期望算法将噪声估计问题转化为对数似然函数数学期望极大化问题,最终得到带次优递 推噪声统计估计器的自适应UKF算法.仿真分析表明,与传统UKF算法相比,提出的自适应UKF算法 有效克服了传统UKF算法在系统噪声统计特性未知情况下滤波精度下降的问题,并实现了系统噪 声统计特性的在线估计.     

Bi-iterative least squares algorithms for blind channel identification and equalization with second-order statistics

We present an adaptive algorithm for blind identification and equalization of single-input multiple-output (SIMO) FIR channels with second-order statistics. We first reformulate the blind channel identification problem into a low-rank matrix approximation solution based on the QR decomposition of the received data matrix. Then, a fast recursive algorithm is developed based on the bi-iterative least squares (Bi-LS) subspace tracking method. The new algorithm requires only a computational complexity of O(md2)...     

基于粒子群算法的盲多用户检测器

在信道参数未知的多径环境下,盲多用户检测算法性能存在诸如收敛速度慢和估计精度低等问题。将粒子群算法运用到基于恒模算法的盲多用户检测中。仿真结果显示,粒子群算法能够更精确地估计出信道的参数,且其收敛速度非常快,在估计出信道参数之后再进行盲多用户检测,检测性能优良。     

ODE analysis and redesign in blind adaptation

In blind adaptation the dynamics of an unknown channel (system) is recovered together with the transmitted symbol sequence (input signal). This paper generalizes the author's previous algorithm (1994, 1997), that is not derived by criterion minimization, to include adaptive regressor filtering. The resulting scheme, which adapts a finite-dimensional IIR channel model, is designed to converge only to a well-defined set of parameters when binary symbols are transmitted over the channel. This provides a partial solution to the recently discussed ill-convergence problem in blind adaptation. Some connections to adaptive control theory are noted     

基于GAMP-MAP估计器的非对齐空中计算

现有的空中计算依赖融合中心的最大似然(maximum likelihood, ML)估计恢复来自不同设备传输信号的算术和,但在实践中,实现准确的信道增益预编码和设备之间的严格同步很困难,ML估计在非对齐空中计算场景中会遭受严重的错误传播,而且计算复杂度高。针对非对齐空中计算问题,设计了一个基于广义近似消息传递(generalized approximate message passing, GAMP)计算框架的最大后验估计器,即GAMP-MAP。该估计器使用传输符号数据的均值和方差作为先验以提高估计准确性,利用GAMP算法标量估计特性降低计算复杂度。在不同信道相位偏移和时间偏移下进行仿真实验,实验结果表明GAMP-MAP估计器的准确性明显优于ML估计器,其运行时间相对ML估计器和LMMSE估计器有显著下降。     

多个三维刚体运动参数的鲁棒估计算法

本文提出了一种估计多个三维刚体运动参数的鲁棒算法，可以处理包含高斯噪声和出格点的对应点数据，根据贝叶斯统计决策规则和蕴含在问题中的启发式规则，我们将运动参数估计问题转化为极大似然估计过程，实现部分模型拟合。因此，这种优化算法就是估计一组三维运动参数，使对应点数据最大限度地拟合似然函数，从而保证算法的鲁棒性。     

Adaptive rate coding using convolutional codes for asynchronous code division multiple access communications over slowly fading channels

This paper presents a method of code rate adaptation using punctured convolutional codes for direct sequence spread spectrum communication systems over slowly fading channels. A blind channel estimation technique is used to estimate the nature of the multi-user channel at the detector (before the decoder). The path gains obtained from the channel estimation technique are used to adapt the code rates. Punctured codes derived from a specific rate 1/2 (M = 4) mother code are used to provide error protection corresponding to the actual channel state. The upper and lower bounds on the bit error probability and the upper bound on the error event probability are derived for hard-decision and soft-decision decoding over Rayleigh and Rician fading channels. The throughput gains obtained using the adaptive scheme and the performances of the punctured codes are studied.     

Convergence of continuous-time partitioned adaptive state estimators

The asymptotic behaviour of Bayes optimal adaptive state estimation schemes (also called the partitioned adaptive estimation algorithms) for continuous-time linear dynamic Gauss-Markov systems with unknown parameters is investigated. The unknown system parameters are asssumed to belong to a finite set. The results are developed through, weak consistency of the maximum likelihood and the maximum a posteriori probability estimates of the unknown parameters.     

无线多媒体传感器网络中一种自适应的报头压缩机制

无线多媒体传感器网络传输大数据量的音频、视频、图像等数据,需要高效地利用宝贵的无线信道资源.报头压缩技术能够减小传输的数据量,提高信道利用效率.提出一种自适应的报头压缩机制AAHC,基于对信道状态的准确估计,动态调整压缩算法的参数,并使用UDP(user datagram protocol) Lite协议作为传输层协议. AAHC机制在获得较高压缩效率的同时,可保证报头压缩的抗差错鲁棒性.仿真结果表明,AAHC机制的性能优于传统的ROHC(robust header compression)机制和CRTP(compressed RTP)机制.     

Identification of sparse FIR systems using a general quantisation scheme

This paper presents an identification scheme for sparse FIR systems with quantised data. We consider a general quantisation scheme, which includes the commonly deployed static quantiser as a special case. To tackle the sparsity issue, we utilise a Bayesian approach, where an ?₁ a priori distribution for the parameters is used as a mechanism to promote sparsity. The general framework used to solve the problem is maximum likelihood (ML). The ML problem is solved by using a generalised expectation maximisation algorithm.     

Estimation-based approach for real-time optimisation of uncertain nonlinear systems

In this paper, the minimisation of an unknown but measurable cost function with uncertain dynamics is considered. The drift term of the uncertain dynamical system and the gradient of the objective function are treated as unknown time-varying parameters. A novel estimation scheme based on almost invariant manifolds is proposed to estimate the time-varying parameters. A direct gradient-based adaptive extremum-seeking controller is designed to solve the uncertain optimisation problem. This approach is shown to improve the transient performance of real-time optimisation control systems.     

针对OFDM系统的一种新的半盲信道估计方法

针对填补零的OFDM（ZP-OFDM）系统,提出了一种新的基于子空间的自适应半盲信道估计算法。该算法利用ZP-OFDM系统导频信息,给出精确的信道参数初始值。然后通过一种新的子空间跟踪以及功率方法,进行自适应信道估计。该算法不仅降低了计算复杂度,而且改善了盲信道估计收敛速度慢的缺点,仿真结果也表明其具有较好的估计精度。