Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572423
Jeng-Kuang Hwang, Jiunn-Horng Denq
The maximum likelihood parameter estimation of multiple damped sinusoids in noise is considered in this paper. Since the damped signal decays exponentially with time and each signal has two parameters to estimate, the ML criterion is very Mcu l t to optimize. In computing the MLE, it is noted that the convergence performance of the iterative algorithm is highly sensitive to the initial point. Thus we resort to a Newton-type ML algorithm equipped with an improved initialization scheme, which comkts of a robust state-space method followed by a reibing alternating " b a t i o n (AM) procedure. Performance simutation shows that the overall ML algorithm can achieve the CR bound with a lower threshold SNR than other existing methods. lies on how to optimize the highly nonlinear and multidimensional ML Criterion [3-51. As is well known, a key to the global convergence of the ML algorithm is the determination of the initial point. In this paper, we present a two-step initialization scheme for finding a more stable initial point. The first step is a polynomialbased state space method that can resuit in stable estimates of the damping fixtors, and the second step is a rething alternating " b a t i o n (AM) methd used to find more accurate frequency estimates [4]. Once the initialization is completed, Newton-type iterations similar to that in [5] are perfiormed in the main loop to optimize the ML criterion. In the following sections, we will present the problem formulation and the overall ML algorithm. Then its superior performance, as compared to other methods, is confirmed by computer simulations.
研究了噪声条件下多阻尼正弦波的最大似然参数估计问题。由于阻尼信号随时间呈指数衰减，并且每个信号都有两个参数需要估计，因此ML准则非常容易优化。在计算MLE时，注意到迭代算法的收敛性能对初始点高度敏感。因此，我们采用牛顿型机器学习算法，该算法配备了改进的初始化方案，该方案包括鲁棒状态空间方法，然后是控制交替的“b - a - i - o - n (AM)”过程。性能仿真表明，整体ML算法能够以较低的信噪比实现CR边界。在于如何优化高度非线性和多维的ML准则[3-51]。众所周知，ML算法全局收敛的关键是初始点的确定。在本文中，我们提出了一个两步初始化方案来寻找一个更稳定的初始点。第一步是基于多项式的状态空间方法，该方法可以得到阻尼固定器的稳定估计，第二步是一种交替的“b - a - i - o - n (AM)”方法，用于找到更准确的频率估计[4]。初始化完成后，在主循环中执行类似于[5]的牛顿型迭代来优化ML标准。在接下来的章节中，我们将介绍问题的表述和整个ML算法。并通过计算机仿真验证了该方法的优越性。
{"title":"Maximum Likelihood Estimation of Multiple Damped Sinusoids by Using Newton's Iterations and Improved Initialization","authors":"Jeng-Kuang Hwang, Jiunn-Horng Denq","doi":"10.1109/SSAP.1994.572423","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572423","url":null,"abstract":"The maximum likelihood parameter estimation of multiple damped sinusoids in noise is considered in this paper. Since the damped signal decays exponentially with time and each signal has two parameters to estimate, the ML criterion is very Mcu l t to optimize. In computing the MLE, it is noted that the convergence performance of the iterative algorithm is highly sensitive to the initial point. Thus we resort to a Newton-type ML algorithm equipped with an improved initialization scheme, which comkts of a robust state-space method followed by a reibing alternating \" b a t i o n (AM) procedure. Performance simutation shows that the overall ML algorithm can achieve the CR bound with a lower threshold SNR than other existing methods. lies on how to optimize the highly nonlinear and multidimensional ML Criterion [3-51. As is well known, a key to the global convergence of the ML algorithm is the determination of the initial point. In this paper, we present a two-step initialization scheme for finding a more stable initial point. The first step is a polynomialbased state space method that can resuit in stable estimates of the damping fixtors, and the second step is a rething alternating \" b a t i o n (AM) methd used to find more accurate frequency estimates [4]. Once the initialization is completed, Newton-type iterations similar to that in [5] are perfiormed in the main loop to optimize the ML criterion. In the following sections, we will present the problem formulation and the overall ML algorithm. Then its superior performance, as compared to other methods, is confirmed by computer simulations.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127167010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572494
S.I. Shah, L. Chaparro, A. El-Jaroudi
2. EVOLUTIONARY MAXIMUM ENTROPY ESTIMATION Using maximum entropy spectral analysis and the theThe Wold-Cramer representation [4] of a non-stationary by considering it the output of a linear timevarying system (LTV) with white noise as input: ory of the Wold-Cramer evolutionary spectrum we develop signal is the evolutionary maximum entropy @ME) estimator for non-stationary signals. The EME estimation reduces to the fitting of a time-varying autoregressive model to the Fourier coefficients of the evolutionary spectrum. The model parameters are efficientlv found bv means of the Levinson alH(n, w)ejwndZ(w) (1) gorithm. Just as in the stationary case, the EME estimator provides very good frequency resolution and can be used to obtain autoregressive models. In this paper, we present the EME estimator and discuss some of its properties. Our aim is to show that the EME estimator has analogous properties to the classical ME estimator for stationary signals.
{"title":"Properties of the Evolutionary Maximum Entropy Spectral Estimator","authors":"S.I. Shah, L. Chaparro, A. El-Jaroudi","doi":"10.1109/SSAP.1994.572494","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572494","url":null,"abstract":"2. EVOLUTIONARY MAXIMUM ENTROPY ESTIMATION Using maximum entropy spectral analysis and the theThe Wold-Cramer representation [4] of a non-stationary by considering it the output of a linear timevarying system (LTV) with white noise as input: ory of the Wold-Cramer evolutionary spectrum we develop signal is the evolutionary maximum entropy @ME) estimator for non-stationary signals. The EME estimation reduces to the fitting of a time-varying autoregressive model to the Fourier coefficients of the evolutionary spectrum. The model parameters are efficientlv found bv means of the Levinson alH(n, w)ejwndZ(w) (1) gorithm. Just as in the stationary case, the EME estimator provides very good frequency resolution and can be used to obtain autoregressive models. In this paper, we present the EME estimator and discuss some of its properties. Our aim is to show that the EME estimator has analogous properties to the classical ME estimator for stationary signals.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127498361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572479
Chien-Chung Hsiao, Chong-Yung Chi
This paper presents image modeling and restoration by higher-order statistics based 2-D inverse filters. A given original image +(m, n) is processed by an optimum inverse filter v(m, n) which is designed by maximizing cumulant based criteria J r ,m = ICmlr/ICrlm where r is even, m > T 2 2 and C,,, (Cr) denotes mthorder (rth-order cumulant of the output e(m,n) of be modeled as the output of a linear shift-invariant (LSI) system h(m, n) driven by e(m, n) where h(m, n) is a stable inverse filter of v(m,n) . When a blurred image y(m,n) = t(m,n) * g(m,n,) rather than the original image z(m, n) is given, t (m, n) can be restored by first estimating e (m,n) using the previous inverse filter criteria and then obtain t(m,n) = e(m, n) * h(m, n). Some experimental results are provided to support the proposed image modeling and restoration method. the 2-D inverse A Iter. The original image z (m, n) can
{"title":"Image Modeling And Restoration By Higher-order Statistics Based Inverse Filters","authors":"Chien-Chung Hsiao, Chong-Yung Chi","doi":"10.1109/SSAP.1994.572479","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572479","url":null,"abstract":"This paper presents image modeling and restoration by higher-order statistics based 2-D inverse filters. A given original image +(m, n) is processed by an optimum inverse filter v(m, n) which is designed by maximizing cumulant based criteria J r ,m = ICmlr/ICrlm where r is even, m > T 2 2 and C,,, (Cr) denotes mthorder (rth-order cumulant of the output e(m,n) of be modeled as the output of a linear shift-invariant (LSI) system h(m, n) driven by e(m, n) where h(m, n) is a stable inverse filter of v(m,n) . When a blurred image y(m,n) = t(m,n) * g(m,n,) rather than the original image z(m, n) is given, t (m, n) can be restored by first estimating e (m,n) using the previous inverse filter criteria and then obtain t(m,n) = e(m, n) * h(m, n). Some experimental results are provided to support the proposed image modeling and restoration method. the 2-D inverse A Iter. The original image z (m, n) can","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126816942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572504
P. Musumeci
{"title":"Estimation of Signal Parameters for Optimal Array Filters","authors":"P. Musumeci","doi":"10.1109/SSAP.1994.572504","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572504","url":null,"abstract":"","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125364476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572438
H. Messer, P. M. Schultheiss
An important signal parameter estimation problem is time-delay estimation. Here the unknown is the time origin of the signal: s (l, θ) = s (l − θ). The duration of the signal (the domain over which the signal is de ned) is assumed brief compared with the observation interval L. Although in continuous time the signal delay is a continuous-valued variable, in discrete time it is not. Consequently, the maximum likelihood estimate cannot be found by di erentiation, and we must determine the maximum likelihood estimate of signal delay by the most fundamental expression of the maximization procedure. Assuming Gaussian noise, the maximum likelihood estimate of delay is the solution of
信号参数估计的一个重要问题是时延估计。这里的未知数是信号的时间原点:s (l， θ) = s (l−θ)。与观测区间l相比，假设信号的持续时间(信号被定义的域)较短。虽然在连续时间中信号延迟是一个连续值变量，但在离散时间中它不是。因此，不能用微分法求最大似然估计，必须用最大化过程的最基本表达式来确定信号延迟的最大似然估计。假设高斯噪声，时延的最大似然估计是
{"title":"On Time Delay Estimation","authors":"H. Messer, P. M. Schultheiss","doi":"10.1109/SSAP.1994.572438","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572438","url":null,"abstract":"An important signal parameter estimation problem is time-delay estimation. Here the unknown is the time origin of the signal: s (l, θ) = s (l − θ). The duration of the signal (the domain over which the signal is de ned) is assumed brief compared with the observation interval L. Although in continuous time the signal delay is a continuous-valued variable, in discrete time it is not. Consequently, the maximum likelihood estimate cannot be found by di erentiation, and we must determine the maximum likelihood estimate of signal delay by the most fundamental expression of the maximization procedure. Assuming Gaussian noise, the maximum likelihood estimate of delay is the solution of","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125507657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572425
V. Nagesha, S. Kay
Statistical inference for vector time series having a mixed spectral representation is considered. The approach is to use a finite-parameter model and compute the maximum likelihood estimates of the underlying descriptors. Statistically/computationally efficient implementations are studied.
{"title":"Estimation of Multichannel Mixed Spectra","authors":"V. Nagesha, S. Kay","doi":"10.1109/SSAP.1994.572425","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572425","url":null,"abstract":"Statistical inference for vector time series having a mixed spectral representation is considered. The approach is to use a finite-parameter model and compute the maximum likelihood estimates of the underlying descriptors. Statistically/computationally efficient implementations are studied.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132262765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572418
P. L. Combettes
{"title":"Set Theoretic Signal Processing","authors":"P. L. Combettes","doi":"10.1109/SSAP.1994.572418","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572418","url":null,"abstract":"","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132776186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572531
Fengzhen Wang, T. Lo, J. Litva, É. Bossé
This paper presents the multisensor data fusion for airborne target classification with artificial neural network. A feature set, which possesses the dominant characteristics of targets and has a certain dynamic range, is chosen. The entire system consists of identification nets (IN) and classification net (CN). Each identification network is used to extract a particular feature of the target, then the outputs of identification networks are fused by classification network, in which the neural network acts as a decision making processor. In the paper, multilayer perceptrons neural networks trained by back-propagation (BP) rule are discussed. In order to speed up the training or decrease the number of epoch in learning process, both momentum and adaptive learning rate methods are used. The simulation results show that the technique of automatic target classification using neural networks can achieve robust decision performance.
{"title":"Multisensor Automatic Target Classification with Neural Networks","authors":"Fengzhen Wang, T. Lo, J. Litva, É. Bossé","doi":"10.1109/SSAP.1994.572531","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572531","url":null,"abstract":"This paper presents the multisensor data fusion for airborne target classification with artificial neural network. A feature set, which possesses the dominant characteristics of targets and has a certain dynamic range, is chosen. The entire system consists of identification nets (IN) and classification net (CN). Each identification network is used to extract a particular feature of the target, then the outputs of identification networks are fused by classification network, in which the neural network acts as a decision making processor. In the paper, multilayer perceptrons neural networks trained by back-propagation (BP) rule are discussed. In order to speed up the training or decrease the number of epoch in learning process, both momentum and adaptive learning rate methods are used. The simulation results show that the technique of automatic target classification using neural networks can achieve robust decision performance.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127582586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572524
R. Kirlin, A. Trzynadlowski
This paper presents several new results and ideas. First the harmonic spectrum and power spectral density (noise density) of random width modulation for minimum loss vector PWM is analyzed and plots for one case of modulation and randomization parameters are given. As a result of the analysis of this modulation scheme, a novel time-domain formulation (autocorrelation) of the spectral information is presented. In this form we find all of the necessary details for understanding the mechanisms of general randomization schemes for suppressing harmonics and converting harmonic power to the noise spectral density. The insights found in the autocorrelation expressions allow at least one optimum design of the randomization parameters of any PWM method. The proposed optimization is but one of many indicated and implied by our methods. Introduction Research on random pulse width modulation (RPWM) techniques for static power converters, mainly three -phase inverters, has recently gained momentum. Initiated by our paper [l] in 1987, in 1992 alone the studies on various WWM issues were reported in over a dozen publications [2]. The RPWM techniques have been found to significantly improve the noise and vibration characteristics of converter-fed motors in adjustable speed drive systems [3,4]. Figure 1 shows both deterministic and random switching rate or random width P W M signals for producing ac from a dc source. The deterministic pattern is often calculated to maximize fundamental power while sometimes nulling or minimizing some selected harmonics. However all deterministic modulations have exactly the same switching patterns in all periods of the fundamental. This naturally leads to harmonics. The basic principle of RPWM consists in introduction of a random factor to the switching patterns of the controlled converter. With regard to three-phase inverters, each cycle of the output voltage is generated by a different randomized combination of pulses of the a(t) deterministic PWM T I
{"title":"Spectral Design of Randomized Pulse Width Modulation in DC to AC Converters","authors":"R. Kirlin, A. Trzynadlowski","doi":"10.1109/SSAP.1994.572524","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572524","url":null,"abstract":"This paper presents several new results and ideas. First the harmonic spectrum and power spectral density (noise density) of random width modulation for minimum loss vector PWM is analyzed and plots for one case of modulation and randomization parameters are given. As a result of the analysis of this modulation scheme, a novel time-domain formulation (autocorrelation) of the spectral information is presented. In this form we find all of the necessary details for understanding the mechanisms of general randomization schemes for suppressing harmonics and converting harmonic power to the noise spectral density. The insights found in the autocorrelation expressions allow at least one optimum design of the randomization parameters of any PWM method. The proposed optimization is but one of many indicated and implied by our methods. Introduction Research on random pulse width modulation (RPWM) techniques for static power converters, mainly three -phase inverters, has recently gained momentum. Initiated by our paper [l] in 1987, in 1992 alone the studies on various WWM issues were reported in over a dozen publications [2]. The RPWM techniques have been found to significantly improve the noise and vibration characteristics of converter-fed motors in adjustable speed drive systems [3,4]. Figure 1 shows both deterministic and random switching rate or random width P W M signals for producing ac from a dc source. The deterministic pattern is often calculated to maximize fundamental power while sometimes nulling or minimizing some selected harmonics. However all deterministic modulations have exactly the same switching patterns in all periods of the fundamental. This naturally leads to harmonics. The basic principle of RPWM consists in introduction of a random factor to the switching patterns of the controlled converter. With regard to three-phase inverters, each cycle of the output voltage is generated by a different randomized combination of pulses of the a(t) deterministic PWM T I","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123269588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572484
D. C. Shin, C. Nikias
A nonlinear adaptive interference mitigation (AIM) algorithm is introduced when the signal of interest is a broadband signal, the additive strong interference is a narrowband signal, and its channel noise distribution belongs to generalized Gaussian distributions. The nonlinear function for the new AIM algorithm is obtained by using Taylor series ezpansion and properties of the generalized Gaussian distributions. Its filter weights are adoptively adjusted through the normalized LMS algorithm. Through MonteCarlo runs, its performance is demonstrated and compared with that of ezisting linear and nonlinear AIM algorithms, when the channel noise distribution is Laplace.
{"title":"Nonlinear Adaptive Narrowband-Interference Mitigation in Generalized Gaussian Noise Channels","authors":"D. C. Shin, C. Nikias","doi":"10.1109/SSAP.1994.572484","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572484","url":null,"abstract":"A nonlinear adaptive interference mitigation (AIM) algorithm is introduced when the signal of interest is a broadband signal, the additive strong interference is a narrowband signal, and its channel noise distribution belongs to generalized Gaussian distributions. The nonlinear function for the new AIM algorithm is obtained by using Taylor series ezpansion and properties of the generalized Gaussian distributions. Its filter weights are adoptively adjusted through the normalized LMS algorithm. Through MonteCarlo runs, its performance is demonstrated and compared with that of ezisting linear and nonlinear AIM algorithms, when the channel noise distribution is Laplace.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126359721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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