Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572520
J. Bohme, D. Konig
We report on analysis of car engine signals as cylinder pressure and vibration signals for combustion diagne sis. Combustions have to be observed for controling efficiency and pollution as well as protecting against knock and can be affected, e.g. by controling the angle of ignition. We first model pressure signals by nonstationary stochastic processes characterized by the compression cycle and a stochastic resonance model. Vibration signals are modeled as time-variant filtered versions of pressure signals superimposed by noise. Wigner-Ville time-frequency estimates applied to measured data that average over many combustion cycles provide evidence of the models. Because only vibration signals can be easily measured in cars, we show that pressure signals can be reconstructed by time-variant filtering of vibration signals. Enhanced knock detectors are discussed which test resonance powers estimated from vibration signals via non-equidistant sampling. Finally, we report on a signal-processor based test-bed diagnosis-system for real-time operation.
{"title":"Statistical Processing of Car Engine Signals for Combustion Diagnosis","authors":"J. Bohme, D. Konig","doi":"10.1109/SSAP.1994.572520","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572520","url":null,"abstract":"We report on analysis of car engine signals as cylinder pressure and vibration signals for combustion diagne sis. Combustions have to be observed for controling efficiency and pollution as well as protecting against knock and can be affected, e.g. by controling the angle of ignition. We first model pressure signals by nonstationary stochastic processes characterized by the compression cycle and a stochastic resonance model. Vibration signals are modeled as time-variant filtered versions of pressure signals superimposed by noise. Wigner-Ville time-frequency estimates applied to measured data that average over many combustion cycles provide evidence of the models. Because only vibration signals can be easily measured in cars, we show that pressure signals can be reconstructed by time-variant filtering of vibration signals. Enhanced knock detectors are discussed which test resonance powers estimated from vibration signals via non-equidistant sampling. Finally, we report on a signal-processor based test-bed diagnosis-system for real-time operation.","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":"125175686","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.1994.572499
Z. Fejzo, H. Lev-Ari
{"title":"Adaptive Laguerre Filters with Lattice Orthogonalization","authors":"Z. Fejzo, H. Lev-Ari","doi":"10.1109/SSAP.1994.572499","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572499","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":"122487728","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.1994.572428
D. Thomson
This paper describes the use of moving narrow-band projection operators for finding precision complex demodulators for data analysis. These projection operators are made up of discrete Slepian sequences and replace the usual weighting procedures with coherent sidelobe cancellation to reduce out-of-band interference. A sliding block of length N gives N different estimates for each output sample. We use weighted averages, and variances, of the N available projections at each time step.
{"title":"Projection Filters for Data Analysis","authors":"D. Thomson","doi":"10.1109/SSAP.1994.572428","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572428","url":null,"abstract":"This paper describes the use of moving narrow-band projection operators for finding precision complex demodulators for data analysis. These projection operators are made up of discrete Slepian sequences and replace the usual weighting procedures with coherent sidelobe cancellation to reduce out-of-band interference. A sliding block of length N gives N different estimates for each output sample. We use weighted averages, and variances, of the N available projections at each time step.","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":"121264594","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.1994.572521
A.Y.J. Chan, T. Lo, J. Litva
Detection of digital signals in the presence of interference and noise plays an important role in personal and mobile communication systems. The interference may arise from multipath propagation or from the multiple users accessing the system. In general, the detection problem can be formulated as a data classification problem. According to the classical detection theory, the optimal detector is provided by the Bayes hypothesis testing [l]. In practice, the statistical properties of the received data, such as the distribution function and the number of incoming signals, are unknown a priori. I t is of significant interest to investigate other non-statistical approaches. Traditionally, linear adaptive filters based on the least mean squares (LMS) and the recursive least squares (RLS) algorithms [2] are employed to combat the degradation due to the interference. They are suboptimal because they only generate hyperplanar decision boundaries in the observation space. Recently, the radial basis function (RBF) network has received a considerable amount of attention. It has the universal approximation ability [3] to construct robust non-linear decision boundaries. Besides, its massive parallelism and fast training time make it desirable for solving complicated tasks. In general, signals arrive at the receiver not only with different time delays, but also from different spatial angles. This spatial information cannot be exploited with a single antenna receiver, and is important in handling the scenarios where the h o m i n g signals are not time-delayed by multiples of a symbol duration. Recently, antenna arrays have attracted much attention in the framework of spatial diversity combining. In this paper, the RBF network is incorporated into an array receiver to solve the detection problem in the spatial domain. The RBF network is first reviewed. Employing the Bayes criterion as a benchmark, a decision-boundary comparison is then performed among the array receiving systems based on the RBF network, the LMS and the RLS adaptive filters. After that, simulation results are presented to compare the bit-error-rate (BER) performance of these array systems.
{"title":"Detection in Array Receiver Using Radial Basis Function Network","authors":"A.Y.J. Chan, T. Lo, J. Litva","doi":"10.1109/SSAP.1994.572521","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572521","url":null,"abstract":"Detection of digital signals in the presence of interference and noise plays an important role in personal and mobile communication systems. The interference may arise from multipath propagation or from the multiple users accessing the system. In general, the detection problem can be formulated as a data classification problem. According to the classical detection theory, the optimal detector is provided by the Bayes hypothesis testing [l]. In practice, the statistical properties of the received data, such as the distribution function and the number of incoming signals, are unknown a priori. I t is of significant interest to investigate other non-statistical approaches. Traditionally, linear adaptive filters based on the least mean squares (LMS) and the recursive least squares (RLS) algorithms [2] are employed to combat the degradation due to the interference. They are suboptimal because they only generate hyperplanar decision boundaries in the observation space. Recently, the radial basis function (RBF) network has received a considerable amount of attention. It has the universal approximation ability [3] to construct robust non-linear decision boundaries. Besides, its massive parallelism and fast training time make it desirable for solving complicated tasks. In general, signals arrive at the receiver not only with different time delays, but also from different spatial angles. This spatial information cannot be exploited with a single antenna receiver, and is important in handling the scenarios where the h o m i n g signals are not time-delayed by multiples of a symbol duration. Recently, antenna arrays have attracted much attention in the framework of spatial diversity combining. In this paper, the RBF network is incorporated into an array receiver to solve the detection problem in the spatial domain. The RBF network is first reviewed. Employing the Bayes criterion as a benchmark, a decision-boundary comparison is then performed among the array receiving systems based on the RBF network, the LMS and the RLS adaptive filters. After that, simulation results are presented to compare the bit-error-rate (BER) performance of these array systems.","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":"114804350","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.1994.572508
Hua Yand, Y. Hua
{"title":"Asymptotical Analisys of MP and Music for 2-D Frequency Estimation","authors":"Hua Yand, Y. Hua","doi":"10.1109/SSAP.1994.572508","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572508","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":"123088251","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.1994.572500
H. Ge, D. Tufts
This paper investigates some effects of practical considerations on the Cramer-Rao Lower Bounds in estimating the parameters of a burst of sinusoid. These considerations include the effect of filtering the received waveform with a known bandpass filter and the effect of the location and extent of the observation time interval, relative to the support of the received signal pulse. We find that the effect of filtering is small. The tail portion of the filter response provides little information about signal parameters. The initial transient portion of the signal pulse provides important information about time of arrival. Finally analytical results are verified through numerical evaluation.
{"title":"Cramer-Rao Lower Bounds on the Errors in Estimating the Parameters of a Burst of Sinusoid","authors":"H. Ge, D. Tufts","doi":"10.1109/SSAP.1994.572500","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572500","url":null,"abstract":"This paper investigates some effects of practical considerations on the Cramer-Rao Lower Bounds in estimating the parameters of a burst of sinusoid. These considerations include the effect of filtering the received waveform with a known bandpass filter and the effect of the location and extent of the observation time interval, relative to the support of the received signal pulse. We find that the effect of filtering is small. The tail portion of the filter response provides little information about signal parameters. The initial transient portion of the signal pulse provides important information about time of arrival. Finally analytical results are verified through numerical evaluation.","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":"124479613","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.1994.572421
Ramesh R. Galigekere, E. Plotkin, M. N. S. Swamy
{"title":"Two-dimensional Spectral Factorization In The Radon Space","authors":"Ramesh R. Galigekere, E. Plotkin, M. N. S. Swamy","doi":"10.1109/SSAP.1994.572421","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572421","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":"126159718","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.1994.572461
D. McArthur, J. Reilly
In this paper, we discuss new self-calibrating techniques for direction-of-arrival (DOA) estimation using an array of sensors, based on estimation-theoretic methods. We consider two cases; one where the background noise is known to be white; the other where the background noise is coloured with unknown covariance. Modern high-resolution array processing algorithms have long suffered from the sensitivity of the bearing estimates to sensor gain and phase calibration errors. This paper presents methods of jointly estimating both the incident DOA’s and calibration parameters, under the assumption that the radiation incident onto the array is in the form of a discrete number of plane waves. Simulation results are given which show that the performance of the proposed methods are significantly improved over conventional algorithms which do not take calibration errors into consideration.
{"title":"An Efficient Self-Calibrating Direction-of-Arrival Estimator","authors":"D. McArthur, J. Reilly","doi":"10.1109/SSAP.1994.572461","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572461","url":null,"abstract":"In this paper, we discuss new self-calibrating techniques for direction-of-arrival (DOA) estimation using an array of sensors, based on estimation-theoretic methods. We consider two cases; one where the background noise is known to be white; the other where the background noise is coloured with unknown covariance. Modern high-resolution array processing algorithms have long suffered from the sensitivity of the bearing estimates to sensor gain and phase calibration errors. This paper presents methods of jointly estimating both the incident DOA’s and calibration parameters, under the assumption that the radiation incident onto the array is in the form of a discrete number of plane waves. Simulation results are given which show that the performance of the proposed methods are significantly improved over conventional algorithms which do not take calibration errors into consideration.","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":"126307615","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.1994.572435
J. Now, D. Hatzinakos, A. Venetsanopoulos
In this paper', we consider detection of signals in a mixture of Gaussian noise and impulsive noise modeled as an alpha-stable process. Since our noise model has infinite variance, in order to use a minimum meansquared error (MMSE) criterion, we apply zero memory nonlinearity (ZMNL) to the information-bearing signal, in such a way that the variance of the noise is limited and the inform* tion signal is not distorted. We generalize the class of detectors which are based on a noise estimation-cancellation technique. In particular, by exploiting the past decisions as well as the past received samples, a nonlinear MMSE estimate of the transformed noise is made and subsequently canceled. We optimize the performance of the system with respect to the ZMNL at the input of the receiver. Our objective is to use predictors of the lowest complexity which give satisfactory estimation accuracy. The proposed subop t imd receivers are designed and analyzed in the context of Partial Response Signaling (PRS). The effects of the predictor order, the number of exploited samples and filtering allocation, on the system performance are examined.
{"title":"Detection In Alpha-stable Noise Environments Based On Nonlinear Prediction","authors":"J. Now, D. Hatzinakos, A. Venetsanopoulos","doi":"10.1109/SSAP.1994.572435","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572435","url":null,"abstract":"In this paper', we consider detection of signals in a mixture of Gaussian noise and impulsive noise modeled as an alpha-stable process. Since our noise model has infinite variance, in order to use a minimum meansquared error (MMSE) criterion, we apply zero memory nonlinearity (ZMNL) to the information-bearing signal, in such a way that the variance of the noise is limited and the inform* tion signal is not distorted. We generalize the class of detectors which are based on a noise estimation-cancellation technique. In particular, by exploiting the past decisions as well as the past received samples, a nonlinear MMSE estimate of the transformed noise is made and subsequently canceled. We optimize the performance of the system with respect to the ZMNL at the input of the receiver. Our objective is to use predictors of the lowest complexity which give satisfactory estimation accuracy. The proposed subop t imd receivers are designed and analyzed in the context of Partial Response Signaling (PRS). The effects of the predictor order, the number of exploited samples and filtering allocation, on the system performance are examined.","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":"128501199","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.1994.572532
M. Bouchard, D. Gingras, Y. D. de Villers, D. Potvin
This paper presents a comparative study of high resolution spectral estimation methods applied to Frequency Modulated Continuous Wave (FMCW) radar data; autoregressive (AR), Prony, and, Eigenanalysis-based methods are briefly reviewed. A comparison of spectral lines detection capabilities between methods for different signal to noise ratio (SNR) and modal amplitudes is made using numerical experiments. Computer simulations have been made using a test signal with ten frequencies in order to evaluate the probability of detection of each frequency and the bias. High resolution spectral estimates from real data using MUSIC 141 and the modified covariance method [2] (AR) showed much more spectral details than the Fourier Transform approach, and it exhibited consistent peak position compared to the lower resolution Fourier spectrum. This work was supported by the DREV under contrat no. W7701-22440/01 -XSK.
{"title":"High Resolution Spectrum Estimation of FMCW Radar Signals","authors":"M. Bouchard, D. Gingras, Y. D. de Villers, D. Potvin","doi":"10.1109/SSAP.1994.572532","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572532","url":null,"abstract":"This paper presents a comparative study of high resolution spectral estimation methods applied to Frequency Modulated Continuous Wave (FMCW) radar data; autoregressive (AR), Prony, and, Eigenanalysis-based methods are briefly reviewed. A comparison of spectral lines detection capabilities between methods for different signal to noise ratio (SNR) and modal amplitudes is made using numerical experiments. Computer simulations have been made using a test signal with ten frequencies in order to evaluate the probability of detection of each frequency and the bias. High resolution spectral estimates from real data using MUSIC 141 and the modified covariance method [2] (AR) showed much more spectral details than the Fourier Transform approach, and it exhibited consistent peak position compared to the lower resolution Fourier spectrum. This work was supported by the DREV under contrat no. W7701-22440/01 -XSK.","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":"128921206","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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