We consider the general signal-processing problem of learning about certain attributes of interest from measurements. These attributes, which may be time-varying (dynamic) or time-invariant (static), can be anything that are relevant to the physical processes that produce the measurements. In statistical signal processing, imperfections or uncertainties in the physical processes are described using probability models, and the complete probabilistic solution to the problem is given by the distribution of the attributes conditioned on all available measurements (the posterior distribution). We describe an algorithm for computing this solution, especially in situations with many measurements or low signal-to-noise ratios. The algorithm combines sequential importance sampling (SIS) and Markov chain Monte Carlo (MCMC) so as to achieve computational efficiency and stability. MCMC is performed sequentially for batches of measurements whose sizes are determined adaptively, hence the name sequential MCMC filter. For measurements within a batch, SIS is performed. Thus, bigger batch sizes mean that MCMC is performed less frequently. SIS is computationally efficient but with a finite Monte Carlo sample size, stability is not guaranteed indefinitely. MCMC is therefore needed from time to time to "refresh" the Monte Carlo sample, eliminating any errors that may have accumulated from the SIS steps. When MCMC is performed, it does not start from scratch but uses the most recent Monte Carlo sample from SIS to construct the proposal distribution. Adaptive batch sizing is based on a Kullback-Leibler distance that is easy to compute. By extending the algorithm to multiple models, the sequential MCMC filter can deal simultaneously with the dual pillars of statistical signal processing, namely detection (more generally, model selection) and parameter estimation. We discuss general uses of the sequential MCMC filter, and demonstrate its use for simultaneous weak signal detection and parameter estimation in a real-data experiment.
{"title":"The sequential MCMC filter: formulation and applications","authors":"D.S. Lee, N. Chia","doi":"10.1109/SSP.2001.955214","DOIUrl":"https://doi.org/10.1109/SSP.2001.955214","url":null,"abstract":"We consider the general signal-processing problem of learning about certain attributes of interest from measurements. These attributes, which may be time-varying (dynamic) or time-invariant (static), can be anything that are relevant to the physical processes that produce the measurements. In statistical signal processing, imperfections or uncertainties in the physical processes are described using probability models, and the complete probabilistic solution to the problem is given by the distribution of the attributes conditioned on all available measurements (the posterior distribution). We describe an algorithm for computing this solution, especially in situations with many measurements or low signal-to-noise ratios. The algorithm combines sequential importance sampling (SIS) and Markov chain Monte Carlo (MCMC) so as to achieve computational efficiency and stability. MCMC is performed sequentially for batches of measurements whose sizes are determined adaptively, hence the name sequential MCMC filter. For measurements within a batch, SIS is performed. Thus, bigger batch sizes mean that MCMC is performed less frequently. SIS is computationally efficient but with a finite Monte Carlo sample size, stability is not guaranteed indefinitely. MCMC is therefore needed from time to time to \"refresh\" the Monte Carlo sample, eliminating any errors that may have accumulated from the SIS steps. When MCMC is performed, it does not start from scratch but uses the most recent Monte Carlo sample from SIS to construct the proposal distribution. Adaptive batch sizing is based on a Kullback-Leibler distance that is easy to compute. By extending the algorithm to multiple models, the sequential MCMC filter can deal simultaneously with the dual pillars of statistical signal processing, namely detection (more generally, model selection) and parameter estimation. We discuss general uses of the sequential MCMC filter, and demonstrate its use for simultaneous weak signal detection and parameter estimation in a real-data experiment.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"22 1","pages":"30-33"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74457625","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}
The detection and diagnosis of gearbox faults is of vital importance for the safe operation of helicopters. This paper presents a new approach in identifying gear mesh signals for early and effective detection of localised gear faults. Using this approach, the gear mesh signal is identified using a nonminimum phase autoregressive (AR) model by maximising the kurtosis of the inverse filter error signal of the model. Sudden changes in the error signal are usually indications of the existence of localised gear faults in the monitored gear. It is demonstrated using the well-regarded CH46 helicopter aft transmission test data that the approach shows great promise for detecting faults in complex gearboxes.
{"title":"Identification of gear mesh signals by kurtosis maximisation and its application to CH46 helicopter gearbox data","authors":"Wenyi Wang","doi":"10.1109/SSP.2001.955299","DOIUrl":"https://doi.org/10.1109/SSP.2001.955299","url":null,"abstract":"The detection and diagnosis of gearbox faults is of vital importance for the safe operation of helicopters. This paper presents a new approach in identifying gear mesh signals for early and effective detection of localised gear faults. Using this approach, the gear mesh signal is identified using a nonminimum phase autoregressive (AR) model by maximising the kurtosis of the inverse filter error signal of the model. Sudden changes in the error signal are usually indications of the existence of localised gear faults in the monitored gear. It is demonstrated using the well-regarded CH46 helicopter aft transmission test data that the approach shows great promise for detecting faults in complex gearboxes.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"39 1","pages":"369-372"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72666337","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}
A spatially distributed network of radar sensors is being used for target tracking and for generating a single integrated aerial picture (SIAP). In such a network generally each sensor sends whatever target track/association information it has to every other sensor. This has the disadvantage of requiring more communication bandwidth and processing power. One of the ways to reduce the communication bandwidth and the processing power is to discover features that would improve the target detection/track accuracy and activate those sensors that would provide the missing information and, form clusters of sensors that have consistent information. We describe a minimax entropy based technique for feature discovery and within class entropy based technique for feature/sensor discrimination. After discovering the features, those sensors that can provide the discovered features are activated. The decision based on the sensor discrimination is used in cluster formation. The experimental details and simulation results that are provided here indicate that these metrics are efficient in discovering features and in discriminating sensors. The techniques described are dynamic in nature - as it acquires information it is making a decision on whether it is from a good sensor in terms of consistency. This has the advantage of discarding non-valid information dynamically and making progressive decision.
{"title":"Feature discovery and sensor discrimination in a network of distributed radar sensors for target tracking","authors":"S. Kadambe","doi":"10.1109/SSP.2001.955238","DOIUrl":"https://doi.org/10.1109/SSP.2001.955238","url":null,"abstract":"A spatially distributed network of radar sensors is being used for target tracking and for generating a single integrated aerial picture (SIAP). In such a network generally each sensor sends whatever target track/association information it has to every other sensor. This has the disadvantage of requiring more communication bandwidth and processing power. One of the ways to reduce the communication bandwidth and the processing power is to discover features that would improve the target detection/track accuracy and activate those sensors that would provide the missing information and, form clusters of sensors that have consistent information. We describe a minimax entropy based technique for feature discovery and within class entropy based technique for feature/sensor discrimination. After discovering the features, those sensors that can provide the discovered features are activated. The decision based on the sensor discrimination is used in cluster formation. The experimental details and simulation results that are provided here indicate that these metrics are efficient in discovering features and in discriminating sensors. The techniques described are dynamic in nature - as it acquires information it is making a decision on whether it is from a good sensor in terms of consistency. This has the advantage of discarding non-valid information dynamically and making progressive decision.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"136-137 1","pages":"126-129"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79104532","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}
Chi and Chen (see IEEE Trans. Signal Processing, July 2001) previously reported a blind equalization algorithm using cumulant based multi-input multi-output inverse filter criteria (MIMO-IFC) for mulituser DS/CDMA systems in multipath. Assuming that the user of interest is the weak user with signal power /spl epsi//sub 1/ and signal powers of all the interferers are identical, denoted /spl epsi/, the performance of Chi and Chen's algorithm is superior to that of Tsatsanis and Xu's(see IEEE Trans. Signal Processing, vol.46, no.11, p.3014-22, 1998) blind minimum variance (MV) equalizer for low near-far ratio (NFR) (=/spl epsi///spl epsi//sub 1//spl ges/1). In this paper, two blind equalization algorithms, called Algorithms 2 and 3, also using cumulant based MIMO-IFC are proposed. The former (Algorithm 2) can improve the performance of the MV equalizer. The latter (Algorithm 3) based on the former performs as well as Chi and Chen's algorithm for low NFR and outperforms Chi and Chen's algorithm and the MV equalizer for high NFR. Some simulation results are presented to support the efficacy of the proposed algorithms.
{"title":"Blind equalization using cumulant based MIMO inverse filter criteria for multiuser DS/CDMA systems in multipath","authors":"Chong-Yung Chi, Chi-Horng Chen","doi":"10.1109/SSP.2001.955236","DOIUrl":"https://doi.org/10.1109/SSP.2001.955236","url":null,"abstract":"Chi and Chen (see IEEE Trans. Signal Processing, July 2001) previously reported a blind equalization algorithm using cumulant based multi-input multi-output inverse filter criteria (MIMO-IFC) for mulituser DS/CDMA systems in multipath. Assuming that the user of interest is the weak user with signal power /spl epsi//sub 1/ and signal powers of all the interferers are identical, denoted /spl epsi/, the performance of Chi and Chen's algorithm is superior to that of Tsatsanis and Xu's(see IEEE Trans. Signal Processing, vol.46, no.11, p.3014-22, 1998) blind minimum variance (MV) equalizer for low near-far ratio (NFR) (=/spl epsi///spl epsi//sub 1//spl ges/1). In this paper, two blind equalization algorithms, called Algorithms 2 and 3, also using cumulant based MIMO-IFC are proposed. The former (Algorithm 2) can improve the performance of the MV equalizer. The latter (Algorithm 3) based on the former performs as well as Chi and Chen's algorithm for low NFR and outperforms Chi and Chen's algorithm and the MV equalizer for high NFR. Some simulation results are presented to support the efficacy of the proposed algorithms.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"75 1","pages":"118-121"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82197501","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}
Inspired by robust estimation, nonlinear denoising methods combining the mean, the median, and the LogCauchy filters are proposed. Some statistical and asymptotic properties are studied, and comparisons with other nonlinear filtering schemes are performed. Experimental results showing a much improved performance of the proposed filters in the presence of Gaussian and heavy-tailed noise are analyzed and illustrated.
{"title":"Nonlinear image filtering in a mixture of Gaussian and heavy-tailed noise","authors":"A. Ben Hamza, H. Krim","doi":"10.1109/SSP.2001.955229","DOIUrl":"https://doi.org/10.1109/SSP.2001.955229","url":null,"abstract":"Inspired by robust estimation, nonlinear denoising methods combining the mean, the median, and the LogCauchy filters are proposed. Some statistical and asymptotic properties are studied, and comparisons with other nonlinear filtering schemes are performed. Experimental results showing a much improved performance of the proposed filters in the presence of Gaussian and heavy-tailed noise are analyzed and illustrated.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"48 1","pages":"90-93"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79762718","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}
Put simply, the global landmine problem is massive. Ground penetrating radar (GPR) is just one engineering solution currently being investigated. A polynomial amplitude-polynomial phase model is fitted to GPR returns. It is observed that the second order phase coefficient shows deviations from background-only levels when a buried target is present. A bootstrap-based detection scheme is proposed that tests for this change. The technique is applied to real GPR data, with encouraging results.
{"title":"Polynomial phase signal based detection of buried landmines using ground penetrating radar","authors":"L. Cirillo, C. L. Brown, A. Zoubir","doi":"10.1109/SSP.2001.955248","DOIUrl":"https://doi.org/10.1109/SSP.2001.955248","url":null,"abstract":"Put simply, the global landmine problem is massive. Ground penetrating radar (GPR) is just one engineering solution currently being investigated. A polynomial amplitude-polynomial phase model is fitted to GPR returns. It is observed that the second order phase coefficient shows deviations from background-only levels when a buried target is present. A bootstrap-based detection scheme is proposed that tests for this change. The technique is applied to real GPR data, with encouraging results.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"25 1","pages":"166-169"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85112169","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}
A blind second order statistical (SOS) subspace based channel identification and equalization technique is introduced and investigated for bandwidth efficient orthogonal frequency division multiplexing (OFDM) systems. A suitable zero-forcing linear equalizer (ZF-LE) is also proposed. Simulations show that identification and equalization is possible with only a small number of short length OFDM symbols.
{"title":"A SOS subspace method for blind channel identification and equalization in bandwidth efficient OFDM systems based on receive antenna diversity","authors":"H. Ali, J. Manton, Y. Hua","doi":"10.1109/SSP.2001.955307","DOIUrl":"https://doi.org/10.1109/SSP.2001.955307","url":null,"abstract":"A blind second order statistical (SOS) subspace based channel identification and equalization technique is introduced and investigated for bandwidth efficient orthogonal frequency division multiplexing (OFDM) systems. A suitable zero-forcing linear equalizer (ZF-LE) is also proposed. Simulations show that identification and equalization is possible with only a small number of short length OFDM symbols.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"24 1","pages":"401-404"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84039025","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}
The existing dual-rate blind linear detectors, which operate at either the low-rate (LR) or the high-rate (HR) mode, are not strictly blind at the HR mode and lack theoretical analysis. This paper proposes the subspace-based LR and HR blind linear detectors, i.e., bad decorrelating detectors (BDD) and blind MMSE detectors (BMMSED), for synchronous DS/CDMA systems. To detect an LR data bit at the HR mode, an effective weighting strategy is proposed. The theoretical analyses on the performance of the proposed detectors are carried out. It has been proved that the bit-error-rate of the LR-BDD is superior to that of the HR-BDD and the near-far resistance of the LR blind linear detectors outperforms that of its HR counterparts. The extension to asynchronous systems is also described. Simulation results show that the adaptive dual-rate BMMSED outperform the corresponding non-blind dual-rate decorrelators proposed by Saquib, Yates and Mandayam (see Wireless Personal Communications, vol. 9, p.197-216, 1998).
{"title":"Subspace-based blind adaptive multiuser detection for multirate DS/CDMA signals","authors":"L. Huang, F. Zheng, M. Faulkner","doi":"10.1109/SSP.2001.955233","DOIUrl":"https://doi.org/10.1109/SSP.2001.955233","url":null,"abstract":"The existing dual-rate blind linear detectors, which operate at either the low-rate (LR) or the high-rate (HR) mode, are not strictly blind at the HR mode and lack theoretical analysis. This paper proposes the subspace-based LR and HR blind linear detectors, i.e., bad decorrelating detectors (BDD) and blind MMSE detectors (BMMSED), for synchronous DS/CDMA systems. To detect an LR data bit at the HR mode, an effective weighting strategy is proposed. The theoretical analyses on the performance of the proposed detectors are carried out. It has been proved that the bit-error-rate of the LR-BDD is superior to that of the HR-BDD and the near-far resistance of the LR blind linear detectors outperforms that of its HR counterparts. The extension to asynchronous systems is also described. Simulation results show that the adaptive dual-rate BMMSED outperform the corresponding non-blind dual-rate decorrelators proposed by Saquib, Yates and Mandayam (see Wireless Personal Communications, vol. 9, p.197-216, 1998).","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"46 1","pages":"106-109"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82977462","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}
Summary form only given, as follows. Commenting on the development of statistics early in the 20th century, the UCLA historian Theodore Porter wrote that "the foundations of mathematical statistics were laid between 1890 and 1930", and argued that "the principal families of techniques for analyzing numerical data were established during the same period." There was a revolution in quantitative data analysis in the early part of last century, leading to the development of the subject we know today as statistics. And at the time Porter wrote, in 1986, he would also have been correct in his second assertion. However, it would be difficult to justify the same remarks today. The speed and memory of computers have increased one thousand fold since 1986, and the second revolution in statistics, certainly motivated and perhaps driven by developments in computing, has begun to fundamentally change statistical methodology. It is a long way from running its course. Over the next few decades it will transform the subject into something that is quite different, in terms of its range and the emphases on types of problems that it treats, from that which we know today. If the development of statistics had taken place in the environment of contemporary advances in computing then the subject would most likely be less mathematical, and more of an experimental science, then it is today. The present talk discusses some of the changes, in areas of resampling and Monte Carlo methods, and outlines new directions for at least the near future.
{"title":"Aspects of contemporary statistical methods","authors":"P. Hall","doi":"10.1109/SSP.2001.955205","DOIUrl":"https://doi.org/10.1109/SSP.2001.955205","url":null,"abstract":"Summary form only given, as follows. Commenting on the development of statistics early in the 20th century, the UCLA historian Theodore Porter wrote that \"the foundations of mathematical statistics were laid between 1890 and 1930\", and argued that \"the principal families of techniques for analyzing numerical data were established during the same period.\" There was a revolution in quantitative data analysis in the early part of last century, leading to the development of the subject we know today as statistics. And at the time Porter wrote, in 1986, he would also have been correct in his second assertion. However, it would be difficult to justify the same remarks today. The speed and memory of computers have increased one thousand fold since 1986, and the second revolution in statistics, certainly motivated and perhaps driven by developments in computing, has begun to fundamentally change statistical methodology. It is a long way from running its course. Over the next few decades it will transform the subject into something that is quite different, in terms of its range and the emphases on types of problems that it treats, from that which we know today. If the development of statistics had taken place in the environment of contemporary advances in computing then the subject would most likely be less mathematical, and more of an experimental science, then it is today. The present talk discusses some of the changes, in areas of resampling and Monte Carlo methods, and outlines new directions for at least the near future.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"81 1","pages":"1-"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90984227","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}
CDMA systems need simultaneous multiple access interference suppression and adaptive interference suppression filters that may span three symbols. Thus, a large number of filter coefficients need to be estimated. By the use of reduced rank filtering, it is possible to lower the number of required filter coefficients with a small decrease in performance. Honig (see Proc. IEEE Asilomar Conf. Signals, Syst. Comput., Pacific Grove, CA, p.1106-10, 1998) made a successful attempt to develop a reduced rank algorithm based on the multistage Wiener (MSW) filter of Goldstein and Reed (see IEEE Tran. on Information Theory, vol.44, no.7, p.2943-59, 1998) for non-dispersive CDMA signals. In this paper, motivated by MSW we propose a reduced rank decorrelating RAKE receiver for dispersive CDMA signals. The proposed receiver is blindly implemented in a lower dimensional space, relative to the full-rank receivers, without the aid of training sequences and the channel information. By exploiting the structure of the user signature waveform, the proposed receivers exhibit performance close to that of the reduced rank MMSE receiver implemented with the desired user's known channel information.
CDMA系统需要同时多址干扰抑制和可跨越三个符号的自适应干扰抑制滤波器。因此，需要估计大量的滤波系数。通过使用降秩过滤，可以降低所需过滤系数的数量，但性能略有下降。Honig(参见Proc. IEEE Asilomar Conf. Signals, system .)第一版。， Pacific Grove, CA, p.1106- 10,1998)成功地尝试开发基于Goldstein和Reed的多级Wiener (MSW)滤波器的降阶算法(参见IEEE Tran。《信息论》第44卷第1期。(1)非色散CDMA信号。本文以生活垃圾为动力，提出了一种用于色散CDMA信号的降阶去相关RAKE接收机。该接收机相对于全秩接收机，在没有训练序列和信道信息的帮助下，在较低维空间中盲目实现。通过利用用户签名波形的结构，所提出的接收机表现出接近使用期望用户的已知信道信息实现的降阶MMSE接收机的性能。
{"title":"A reduced rank decorrelating RAKE receiver for CDMA communications over frequency selective channels","authors":"O. Ozdemir, M. Torlak","doi":"10.1109/SSP.2001.955231","DOIUrl":"https://doi.org/10.1109/SSP.2001.955231","url":null,"abstract":"CDMA systems need simultaneous multiple access interference suppression and adaptive interference suppression filters that may span three symbols. Thus, a large number of filter coefficients need to be estimated. By the use of reduced rank filtering, it is possible to lower the number of required filter coefficients with a small decrease in performance. Honig (see Proc. IEEE Asilomar Conf. Signals, Syst. Comput., Pacific Grove, CA, p.1106-10, 1998) made a successful attempt to develop a reduced rank algorithm based on the multistage Wiener (MSW) filter of Goldstein and Reed (see IEEE Tran. on Information Theory, vol.44, no.7, p.2943-59, 1998) for non-dispersive CDMA signals. In this paper, motivated by MSW we propose a reduced rank decorrelating RAKE receiver for dispersive CDMA signals. The proposed receiver is blindly implemented in a lower dimensional space, relative to the full-rank receivers, without the aid of training sequences and the channel information. By exploiting the structure of the user signature waveform, the proposed receivers exhibit performance close to that of the reduced rank MMSE receiver implemented with the desired user's known channel information.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"6 1","pages":"98-101"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90196605","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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