检索结果-内蒙古大学图书馆

International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： A. Logothetis V. Krishnamurthy CRC for Sensor Signal and Information Processing Department of Electrical and Electronic Engineering University of Melbourne Parkville VIC Australia

We consider the de-interleaving of N independent autoregressive (AR) processes from 1-bit quantized measurements. De-interleaving has applications in radar and signal detection. Other possible applications are computer communications and neural systems. The received signal (pulse train) is the superposition of N 1-bit quantized Gaussian AR processes observed in white Gaussian noise. The aim is to identify which sources are responsible for the observed noisy pulses. Furthermore, it is desired to obtain parameter estimates for the N sources. The proposed algorithm, (subject to model assumptions) optimally combines hidden Markov model and binary time series estimation techniques.

关键词： Autoregressive processes Application software Hidden Markov models Radar applications signal detection Computer applications Quantum computing signal processing Gaussian noise Parameter estimation

来源：评论

学校读者我要写书评

暂无评论

Mode-matched filtering via the EM algorithm

Mode-matched filtering via the EM algorithm

引用

American Control Conference (ACC)

作者： L.A. Johnston V. Krishnamurthy CRC for Sensor Signal and Information Processing Department of Electrical and Electronic Engineering University of Melbourne Parkville VIC Australia

We show that a generalization of the EM algorithm, the alternating expectation conditional maximization (AECM) algorithm, can be used to derive a mode matched filtering algorithm called the MMAECM. Mode-matched filtering methods are used for state estimation of jump Markov linear systems. Such models are used in a wide variety of areas in which the system switches between different modes of operation, as in target tracking. The optimal conditional mean estimator for jump Markov linear systems is of exponential complexity, hence algorithms are necessarily suboptimal. We derive the MMAECM according to the maximum a posteriori criterion. Performance of an online version of the MMAECM algorithm is compared to existing mode-matched filtering algorithms such as the interacting multiple model algorithm and generalized pseudo Bayesian methods.

关键词： Filtering algorithms Linear systems State estimation State-space methods Nonlinear filters Bayesian methods Integrated circuit modeling Yttrium Cyclic redundancy check signal processing

来源：评论

学校读者我要写书评

暂无评论

Performance of OTH radar array calibration

Performance of OTH radar array calibration

引用

International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： I.S.D. Solomon Yu.I. Abramovich D.A. Gray S.J. Anderson CRC For Sensor Signal & Inf. Process. Adelaide Univ. SA Australia Defence Science and Technology Organisation CRC for Sensor Signal and Information Processing University of Adelaide Salisbury Australia

Array calibration algorithms for over-the-horizon (OTH) radar arrays have been proposed in the literature. These algorithms perform array calibration by using echoes from ionised meteor trails, and estimate sensor position errors and mutual coupling. We derive the Cramer-Rao performance bound for this array calibration problem and then investigate the performance bound. We obtain insight on the achievable accuracy as a function of the signal-to-noise ratio, number of snapshots and number of sources. Further, we consider the advantage of using sources with known bearings, as opposed to unknown bearings, and consider the identifiability of the array calibration problem.

关键词： Calibration sensor arrays Mutual coupling Covariance matrix Symmetric matrices Additive noise Phase noise Spaceborne radar signal to noise ratio Statistics

来源：评论

学校读者我要写书评

暂无评论

On the equivalence of the extended Kalman smoother and the expectation maximisation algorithm for polynomial signal models

On the equivalence of the extended Kalman smoother and the e...

引用

information, Decision and Control (IDC)

作者： L.A. Johnston V. Krishnamurthy CRC for Sensor Signal and Information Processing (CSSIP) Department of Electrical and Electronic Engineering University of Melbourne Parkville VIC Australia

The iterated extended Kalman smoother (IEKS) is shown to be equivalent to one iteration of the expectation maximisation (EM)-based SAGE algorithm for the class of nonlinear signal models containing polynomial dynamics. Thus the IEKS is a maximum a posteriori (MAP) state sequence estimator for this class of systems. The iterated extended Kalman filter (IEKF) can be thought of as a heuristic, online version of a SAGE algorithm, derived via EM formalism rather than via linearisation around approximate conditional mean state estimates. We apply the polynomial SAGE algorithm to the discrete time, cubic sensor problem and show that it outperforms the standard extended Kalman smoother.

关键词： Kalman filters Polynomials State estimation signal processing Cyclic redundancy check information processing Filtering theory Stochastic processes Chaos System identification

来源：评论

学校读者我要写书评

暂无评论

Closed-Loop Frequency Tracking and Rejection

引用

IFAC Proceedings Volumes 1997年第6期30卷 1263-1268页

作者： Allan J Connolly Barbara F La Scala Peter J Kootsookos CRC for Robust and Adaptive Systems Department of Systems Engineering The Australian National University Canberra ACT 0200 Australia CRC for Sensor Signal and Information Processing Department of Electrical Engineering University of Melbourne Parkville VIC 3052 Australia

This paper develops an adaptive controller for active vibration control. The method is based on the LQG approach via disturbance modelling given in De Nicolao et al. (1992b). This approach to the narrow band disturbance rejection problem is then applied to the problem of eliminating the effects of roll eccentricity in steel-strip rolling mills.

关键词： adaptive control disturbance rejection vibration control

来源：评论

学校读者我要写书评

暂无评论

A statistical rationalisation of Hartley's normalised eight-point algorithm

A statistical rationalisation of Hartley's normalised eight-...

引用

International Conference on Image Analysis and processing

作者： W. Chojnacki M.J. Brooks A. van den Hengel D. Gawley School of Computer Science University of Adelaide Adelaide SA Australia CRC for Sensor Signal and Information Processing Mawson Lakes SA Australia

The eight-point algorithm of Hartley occupies an important place in computer vision, notably as a means of providing an initial value of the fundamental matrix for use in iterative estimation methods. In this paper, a novel explanation is given for the improvement in performance of the eight-point algorithm that results from using normalised data. A first step is singling out a cost function that the normalised algorithm acts to minimise. The cost function is then shown to be statistically better founded than the cost function associated with the non-normalised algorithm. This augments the original argument that improved performance is due to the better conditioning of a pivotal matrix. Experimental results are given that support the adopted approach. This work continues a wider effort to place a variety of estimation techniques within a coherent framework.

关键词： Iterative algorithms Cost function Cameras Australia Iterative methods Eigenvalues and eigenfunctions Computer science Cyclic redundancy check signal processing information processing

来源：评论

学校读者我要写书评

暂无评论

The pmht with hysteresis

The pmht with hysteresis

引用

International Conference on information Fusion

作者： S.J. Davey D.A. Gray Defence Science and Technology Organisation Australia School of Elec Engineering University of Adelaide Australia CRC for Sensor Signal Information Processing Australia

ISBN: (纸本)0972184457

The Probabilistic Multi-Hypothesis Tracker (PMHT) is a recent algorithm based on the application of Expectation Maximisation to multi-target tracking. The algorithm treats the sensor measurements as independent observations of a mixture model, and the assignment of each measurement as an independent realisation of the mixing process. A result of this is that data association between tracks and measurements is performed independently for each measurement. If the mixing proportions of the models are unknown, then the PMHT estimates them, assuming them to be time independent. For most applications, the mixing proportions will not be time independent, but rather change smoothly with time. The PMHT with Hysteresis provides a state model for the mixing proportions, allowing the PMHT to model them as random processes with arbitrary statistics.

关键词： Hysteresis Target tracking Australia Random processes information processing signal processing Performance evaluation Statistics State estimation Parameter estimation

来源：评论

学校读者我要写书评

暂无评论

l/sub 1/ state estimation of jump Markov linear systems

l/sub 1/ state estimation of jump Markov linear systems

引用

IEEE Conference on Decision and Control

作者： C. Carlemalm A. Logothetis V. Krishnamurthy Automatic Control Royal Institute of Technology Stockholm Sweden CRC for Sensor Signal and Information Processing Department of Electrical and Electronic Engineering University of Melbourne Australia

We consider the state estimation in an l/sub 1/ sense of non-stationary jump Markov linear systems. The parameters of a jump Markov linear system evolve with time according to the realization of a finite state Markov chain. An iterative algorithm is presented, which cross couples an interior point based method with the Viterbi algorithm. The Viterbi algorithm yields the Markov chain sequence estimate and the interior point based scheme yields the state sequence estimate of the jump Markov linear system.

关键词： State estimation Linear systems Iterative algorithms Viterbi algorithm Yield estimation Time varying systems Linear programming Polynomials Automatic control Cyclic redundancy check

来源：评论

学校读者我要写书评

暂无评论

Parametric Pulse Train De-Interleaving of Stochastic Sources

引用

IFAC Proceedings Volumes 1996年第1期29卷 4231-4236页

作者： Andrew Logothetis Vikram Krishnamurthy H. Vincent Poor CRC for Sensor Signal and Information Processing Department of Electrical and Electronic Engineering University of Melbourne Parkville Victoria 3052 Australia Department of Electrical Engineering Princeton University Princeton NJ 08544-5263 USA

In this paper we consider de-interleaving a finite number of stochastic parametric sources. The sources are modeled as independent autoregressive (AR) processes. Based on a Markovian switching policy, we assume that the different sources transmit signals on the same single channel. The receiver records the 1-bit quantized version of the transmitted signal and aims to identify the sequence of active sources. Once the source sequence has been identified, the characteristics (parameters) of each source is estimated.

关键词： Autoregressive Models Parameter Estimation Expectation Maximization Algorithm Hidden Markov Models Binary Time Series

来源：评论

学校读者我要写书评

暂无评论

Multiple target tracking with a pixelized sensor

Multiple target tracking with a pixelized sensor

引用

International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： M.R. Morelande C.M. Kreucher K. Kastella CRC for Sensor Signal and Information Processing University of Melbourne Australia University of Michigan Ann Arbor MI USA General Dynamics Ann Arbor MI USA

This paper describes a computationally efficient method for tracking multiple moving targets. The method is predicated on estimation of the joint multitarget probability density (JMPD), which is a single probabilistic entity capturing uncertainty in both the number of targets and the states of the individual targets. The non-Gaussian/nonlinear measurement model adopted here does not permit exact computation of the JMPD so some method of approximation is required. A novel particle filtering algorithm for recursive estimation of the JMPD is proposed which provides computational tractability by automatically factoring the high dimensional multitarget state when applicable and using the optimal importance density with Rao-Blackwellisization on each of the factors. The efficiency of the proposed algorithm is shown via simulation results using real, recorded target trajectories.

关键词： Target tracking Computational modeling Recursive estimation Trajectory signal to noise ratio Filtering Nonlinear equations Particle tracking Particle filters State estimation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：