Alireza Moghaddamjoo - Academia.edu (original) (raw)
Papers by Alireza Moghaddamjoo
2008 IEEE Radar Conference, 2008
In this paper we propose a new automatic target recognition algorithm to recognize and distinguis... more In this paper we propose a new automatic target recognition algorithm to recognize and distinguish of three classes of targets: personnel, wheeled vehicles and animals, using a low-resolution ground surveillance pulse Doppler radar. Using the chirplet transformation, a time-frequency signal processing technique, the parameterized radar signal is then used by the Zernike moments (ZM) for the pertinent features of the
Control and Dynamic Systems, 1995
... Permissions & Reprints. Techniques in array processing by means of transf... more ... Permissions & Reprints. Techniques in array processing by means of transformations. Alireza Moghaddamjoo a and Mahmoud Allam a. a University of Wisconsin-Milwaukee Department of Electrical Engineering and Computer Science PO Box 784-Milwaukee, WI 53201. ...
2008 Second Asia International Conference on Modelling & Simulation (AMS), 2008
... Sahar Movaghati Amirkabir University of Technology Department of Electrical Engineering 424Ha... more ... Sahar Movaghati Amirkabir University of Technology Department of Electrical Engineering 424Hafez Ave., 15914, Tehran, Iran saharm@aut.ac.ir Alireza Moghaddamjoo University of Wisconsin-Milwaukee Department of Electrical Engineering and Computer Science PO Box ...
IEEE Transactions on Signal Processing, 1991
IEEE Transactions on Signal Processing, 1995
V. CONCLUSION This correspondence presents a simple but general method for adding the simultaneou... more V. CONCLUSION This correspondence presents a simple but general method for adding the simultaneous estimation of noise variance and SNR to a parameter estimation algorithm. Very little extra computation is required if the AUDI algorithm is used and the method is useful for on-line identification using constant or variable forgetting factors. The on-line estimates of the variance and S N R can also be used in applications such as identification, filtering, and control.
IEEE Transactions on Geoscience and Remote Sensing, 1989
Segmentation ALIREZA MOGHADDAMJOO, MEMBER, IEEE ALstractOne of the problems in well-log signal pr... more Segmentation ALIREZA MOGHADDAMJOO, MEMBER, IEEE ALstractOne of the problems in well-log signal processing is in the automatic segmentation of data into homogeneous parts for stratigraphic correlation and/or bed evaluation. In this work, a special classification algorithm is proposed which can be applied to a preprocessed (or the original noisy) well-log signal for segmentation. Knowledge of the number of segments or any other constraint, if existent, along with a criterion function can be used to complete the algorithm. The preprocessing routine consists of a running window change-detection algorithm which detects all the potential candidates for the location of changes in the signal. This routing can be applied in a way that significantly overestimates the number of changes. These points of change along with other estimated parameters are used by the classification algorithm to find the global best-segmentation that agrees with the a priori knowledge of the number of segments (or any other constraint) and satisfies a criterion function. The resultant optimum classification algorithm is recursive and computationally efficient. Performance of the overall algorithm is demonstrated by several examples, * Alireza Moghaddamjoo (S'84-M'86) was born in Tehran, Iran, on March 16, 1953. He received the B.S. degree in electrical engineering from the University of Tehran in 1976, the M.S. degree in nuclear engineering from the Massachusetts Institute of Technology, Cambridge, MA, in 1978, and the Ph.D. degree in electrical engineering from the University of Wyoming, Laramie, in 1986. At the University of Wyoming he was a Research Assistant and a Western Research Institute Graduate Scholar. During his Ph.D. program he was involved in several funded projects, namely robust Kalman tracking, robust step detection, covariance estimation and eigenstructure variability, and eigencoding for seismic data. He held a Lecturer position in the Department of Electrical Engineering of the University of Wyoming for the spring semester of 1986. He is currently an Assistant Professor in the Department of Electrical Engineering and Computer Science of the University of Wisconsin, Milwaukee, which he joined in 1986. His teaching and research are primarily in the areas of digital signal processing, adaptive filtering, pattern recognition, and image processing.
IEEE Transactions on Geoscience and Remote Sensing, 2010
Extended Kalman filter (EKF) has previously been employed to extract road maps in satellite image... more Extended Kalman filter (EKF) has previously been employed to extract road maps in satellite images. This filter traces a single road until a stopping criterion is satisfied. In our new approach, we have combined EKF with a special particle filter (PF) in order to regain the trace of the road beyond obstacles, as well as to find and follow different road branches after reaching to a road junction. In this approach, first, EKF traces a road until a stopping criterion is met. Then, instead of terminating the process, the results are passed to the PF algorithm which tries to find the continuation of the road after a possible obstacle or to identify all possible road branches that might exist on the other side of a road junction. For further improvement, we have modified the procedure for obtaining the measurements by decoupling this process from the current state prediction of the filter. Removing the dependence of the measurement data to the predicted state reduces the potential for instability of the road-tracing algorithm. Furthermore, we have constructed a method for dynamic clustering of the road profiles in order to maintain tracking when the road profile undergoes some variations due to changes in the road width and intensity.
IEEE Signal Processing Letters, 1994
A new approach for high-resolution direction-ofarrival (DOA) estimation of coherent wideband sign... more A new approach for high-resolution direction-ofarrival (DOA) estimation of coherent wideband signals is proposed. The approach is based on the projection of the twodimensional spatial-temporal DFT of the received signal. The projections are used to construct narrowband covariance matrices having the same signal subspace which are then combined to yield the array covariance matrix. The array covariance can then be used by any signal-subspace eigendecomposition algorithm. The proposed approach does not require any preliminary estimates of DOA's and is not restricted to particular source models.
![Research paper thumbnail of Correction to "Automatic earthquake signal onset picking based on the continuous wavelet transform" [May 13 2666-2674]](https://attachments.academia-assets.com/85699401/thumbnails/1.jpg)
](IEEE Transactions on Geoscience and Remote Sensing, 2013
Journal of American …, 2012
... [Seyed Mostafa Mirhassani, Bardia Yousefi, Alireza Moghaddamjoo. Automatic Building Extractio... more ... [Seyed Mostafa Mirhassani, Bardia Yousefi, Alireza Moghaddamjoo. Automatic Building Extraction from Urban Satellite Imagery Using Bayesian Classifier and Unsharp Masking as Spectral Information. ... ( ) , ( ) y x SH y x I y x I = => ≤ θ (10.1) ...
2011 19th Iranian Conference on Electrical Engineering, 2011
In This paper, a new semi-automatic method for road extraction in urban or non-urban areas is pre... more In This paper, a new semi-automatic method for road extraction in urban or non-urban areas is presented to produce a geographical map and updating it. We have tried to develop a new method for road tracking based on road features in high resolution images. To do so, inspiring from profile matching that is usually used for road tracking; we have
The 10th IEEE International Symposium on Signal Processing and Information Technology, 2010
Speech segmentation to covariance-stationary regions is of interest, for example in subspace-base... more Speech segmentation to covariance-stationary regions is of interest, for example in subspace-based speech enhancement. However as the true covariance matrices of speech segments are unknown, it is usual to use their sample estimates. To check whether two sample covariance matrices have been drawn from the same distribution or not, we have used a test statistic previously proposed for image segmentation. We have derived a new expression for the decision threshold using Random Matrix Theory. Finally, a novel segmentation procedure is proposed and applied to both synthetic and speech data. The presented simulation results show the low computational cost and good performance.
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989
A method is proposed to adapt the Kalman filter to the changes in the input forcing functions and... more A method is proposed to adapt the Kalman filter to the changes in the input forcing functions and the noise statistics. The resulting procedure is stable in the sense that the duration of divergences caused by external disturbances are finite and short and, also, the procedure is robust with respect to impulsive noise (outlier). The input forcing functions are estimated
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1986
Target tracking with Kalman filters is hampered by target madeuvering and unknown process and mea... more Target tracking with Kalman filters is hampered by target madeuvering and unknown process and measurement noises. We show that moving data windows mag be used to analyze state and measurement error sequences, determining robust estimates of bias and covariance. For steps in the system forcing functions and non-Gaussian measurement errors, the robust estimators yield improvements over linear bias and covariance estimators. Extensive simulations compare conventional, linear adaptive, and robust adaptive average step responses of a first-order system filter. Quantities examined are state estimate, state error, process and measurement covariance estimates, Kalman gain, and input step estimate. * [ (f ' (kj + 1)-6''(k)) (f ' (kj + 1)a'(k))T]
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1986
An N-point window is applied to noisy data to recover stepped signals in non-Gaussian noise. Robu... more An N-point window is applied to noisy data to recover stepped signals in non-Gaussian noise. Robust measures of signal step level and noise distribution spread are used to detect sequential clusters of data points which are statistically significantly different, thereby detecting the step. Using conventional analysis-of-variance methods, but with robust parameter estimates, false alarm probabilities are set reasonably accurately, and miss probabilities and signal level estimates are shown by simulation to yield good results. Applications to Kalman filtering, seismic and well-log data, and image processing are indicated.
ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1985
Target tracking with Kalman filters is hampered by target maneuvering and unknown process and mea... more Target tracking with Kalman filters is hampered by target maneuvering and unknown process and measurement noises. We show that moving data windows may be used to analyze state and measurement error sequences, determining robust estimates of bias and covariance. For unknown large steps in the system forcing functions and non-Gaussian measurement errors the robust estimators yield improvements over linear bias
Fifth ASSP Workshop on Spectrum Estimation and Modeling, 1990
One of the recently proposed preprocessing approaches to decorrelate coherent signals is the spat... more One of the recently proposed preprocessing approaches to decorrelate coherent signals is the spatial filtering method which includes the spatial smoothing algorithm as one of its particular cases. The spatial filtering (SF) algorithm is based on the design of several FIR spatial filters which are aimed at effectively decorrelating coherent signals. The authors study and analyze the SF algorithm and then elaborate more on its statistical analysis under different conditions
International Conference on Acoustics, Speech, and Signal Processing, 1989
The author proposes a method, called spatial filtering, to resolve some of the problems associate... more The author proposes a method, called spatial filtering, to resolve some of the problems associated with spatial smoothing. The method, which is similar to spatial smoothing, can be applied to wideband as well as narrowband problems. The objective of the method is to decorrelate completely the set of fully correlated sources. The theoretical findings are supported by simulations
[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing, 1992
A new method for the estimation of the direction of arrival (DOA) of correlated wideband signals ... more A new method for the estimation of the direction of arrival (DOA) of correlated wideband signals received by an array of sensors uses 2-D directive filtering in the time-space domain. This results in selecting bins at different frequencies from signals arriving from different directions. Since signals at different frequencies are uncorrelated, any spectral estimator can then be used to estimate
Fourth Annual ASSP Workshop on Spectrum Estimation and Modeling, 1988
Eigendecomposition-based methods developed to date require that the additive noise be spatially w... more Eigendecomposition-based methods developed to date require that the additive noise be spatially white, i.e. of equal power and uncorrelated from sensor to sensor. A set of transform-based covariance differencing approaches is introduced that eliminates the effects of unequal noise power on eigenvalues and, at the same time, increase the resolution of the procedure in comparison with the standard methods. The performance of this approach under different conditions is examined through simulations, and the results are compared to the results obtained by using the standard MUSIC algorithm
2008 IEEE Radar Conference, 2008
In this paper we propose a new automatic target recognition algorithm to recognize and distinguis... more In this paper we propose a new automatic target recognition algorithm to recognize and distinguish of three classes of targets: personnel, wheeled vehicles and animals, using a low-resolution ground surveillance pulse Doppler radar. Using the chirplet transformation, a time-frequency signal processing technique, the parameterized radar signal is then used by the Zernike moments (ZM) for the pertinent features of the
Control and Dynamic Systems, 1995
... Permissions & Reprints. Techniques in array processing by means of transf... more ... Permissions & Reprints. Techniques in array processing by means of transformations. Alireza Moghaddamjoo a and Mahmoud Allam a. a University of Wisconsin-Milwaukee Department of Electrical Engineering and Computer Science PO Box 784-Milwaukee, WI 53201. ...
2008 Second Asia International Conference on Modelling & Simulation (AMS), 2008
... Sahar Movaghati Amirkabir University of Technology Department of Electrical Engineering 424Ha... more ... Sahar Movaghati Amirkabir University of Technology Department of Electrical Engineering 424Hafez Ave., 15914, Tehran, Iran saharm@aut.ac.ir Alireza Moghaddamjoo University of Wisconsin-Milwaukee Department of Electrical Engineering and Computer Science PO Box ...
IEEE Transactions on Signal Processing, 1991
IEEE Transactions on Signal Processing, 1995
V. CONCLUSION This correspondence presents a simple but general method for adding the simultaneou... more V. CONCLUSION This correspondence presents a simple but general method for adding the simultaneous estimation of noise variance and SNR to a parameter estimation algorithm. Very little extra computation is required if the AUDI algorithm is used and the method is useful for on-line identification using constant or variable forgetting factors. The on-line estimates of the variance and S N R can also be used in applications such as identification, filtering, and control.
IEEE Transactions on Geoscience and Remote Sensing, 1989
Segmentation ALIREZA MOGHADDAMJOO, MEMBER, IEEE ALstractOne of the problems in well-log signal pr... more Segmentation ALIREZA MOGHADDAMJOO, MEMBER, IEEE ALstractOne of the problems in well-log signal processing is in the automatic segmentation of data into homogeneous parts for stratigraphic correlation and/or bed evaluation. In this work, a special classification algorithm is proposed which can be applied to a preprocessed (or the original noisy) well-log signal for segmentation. Knowledge of the number of segments or any other constraint, if existent, along with a criterion function can be used to complete the algorithm. The preprocessing routine consists of a running window change-detection algorithm which detects all the potential candidates for the location of changes in the signal. This routing can be applied in a way that significantly overestimates the number of changes. These points of change along with other estimated parameters are used by the classification algorithm to find the global best-segmentation that agrees with the a priori knowledge of the number of segments (or any other constraint) and satisfies a criterion function. The resultant optimum classification algorithm is recursive and computationally efficient. Performance of the overall algorithm is demonstrated by several examples, * Alireza Moghaddamjoo (S'84-M'86) was born in Tehran, Iran, on March 16, 1953. He received the B.S. degree in electrical engineering from the University of Tehran in 1976, the M.S. degree in nuclear engineering from the Massachusetts Institute of Technology, Cambridge, MA, in 1978, and the Ph.D. degree in electrical engineering from the University of Wyoming, Laramie, in 1986. At the University of Wyoming he was a Research Assistant and a Western Research Institute Graduate Scholar. During his Ph.D. program he was involved in several funded projects, namely robust Kalman tracking, robust step detection, covariance estimation and eigenstructure variability, and eigencoding for seismic data. He held a Lecturer position in the Department of Electrical Engineering of the University of Wyoming for the spring semester of 1986. He is currently an Assistant Professor in the Department of Electrical Engineering and Computer Science of the University of Wisconsin, Milwaukee, which he joined in 1986. His teaching and research are primarily in the areas of digital signal processing, adaptive filtering, pattern recognition, and image processing.
IEEE Transactions on Geoscience and Remote Sensing, 2010
Extended Kalman filter (EKF) has previously been employed to extract road maps in satellite image... more Extended Kalman filter (EKF) has previously been employed to extract road maps in satellite images. This filter traces a single road until a stopping criterion is satisfied. In our new approach, we have combined EKF with a special particle filter (PF) in order to regain the trace of the road beyond obstacles, as well as to find and follow different road branches after reaching to a road junction. In this approach, first, EKF traces a road until a stopping criterion is met. Then, instead of terminating the process, the results are passed to the PF algorithm which tries to find the continuation of the road after a possible obstacle or to identify all possible road branches that might exist on the other side of a road junction. For further improvement, we have modified the procedure for obtaining the measurements by decoupling this process from the current state prediction of the filter. Removing the dependence of the measurement data to the predicted state reduces the potential for instability of the road-tracing algorithm. Furthermore, we have constructed a method for dynamic clustering of the road profiles in order to maintain tracking when the road profile undergoes some variations due to changes in the road width and intensity.
IEEE Signal Processing Letters, 1994
A new approach for high-resolution direction-ofarrival (DOA) estimation of coherent wideband sign... more A new approach for high-resolution direction-ofarrival (DOA) estimation of coherent wideband signals is proposed. The approach is based on the projection of the twodimensional spatial-temporal DFT of the received signal. The projections are used to construct narrowband covariance matrices having the same signal subspace which are then combined to yield the array covariance matrix. The array covariance can then be used by any signal-subspace eigendecomposition algorithm. The proposed approach does not require any preliminary estimates of DOA's and is not restricted to particular source models.
IEEE Transactions on Geoscience and Remote Sensing, 2013
Journal of American …, 2012
... [Seyed Mostafa Mirhassani, Bardia Yousefi, Alireza Moghaddamjoo. Automatic Building Extractio... more ... [Seyed Mostafa Mirhassani, Bardia Yousefi, Alireza Moghaddamjoo. Automatic Building Extraction from Urban Satellite Imagery Using Bayesian Classifier and Unsharp Masking as Spectral Information. ... ( ) , ( ) y x SH y x I y x I = => ≤ θ (10.1) ...
2011 19th Iranian Conference on Electrical Engineering, 2011
In This paper, a new semi-automatic method for road extraction in urban or non-urban areas is pre... more In This paper, a new semi-automatic method for road extraction in urban or non-urban areas is presented to produce a geographical map and updating it. We have tried to develop a new method for road tracking based on road features in high resolution images. To do so, inspiring from profile matching that is usually used for road tracking; we have
The 10th IEEE International Symposium on Signal Processing and Information Technology, 2010
Speech segmentation to covariance-stationary regions is of interest, for example in subspace-base... more Speech segmentation to covariance-stationary regions is of interest, for example in subspace-based speech enhancement. However as the true covariance matrices of speech segments are unknown, it is usual to use their sample estimates. To check whether two sample covariance matrices have been drawn from the same distribution or not, we have used a test statistic previously proposed for image segmentation. We have derived a new expression for the decision threshold using Random Matrix Theory. Finally, a novel segmentation procedure is proposed and applied to both synthetic and speech data. The presented simulation results show the low computational cost and good performance.
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989
A method is proposed to adapt the Kalman filter to the changes in the input forcing functions and... more A method is proposed to adapt the Kalman filter to the changes in the input forcing functions and the noise statistics. The resulting procedure is stable in the sense that the duration of divergences caused by external disturbances are finite and short and, also, the procedure is robust with respect to impulsive noise (outlier). The input forcing functions are estimated
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1986
Target tracking with Kalman filters is hampered by target madeuvering and unknown process and mea... more Target tracking with Kalman filters is hampered by target madeuvering and unknown process and measurement noises. We show that moving data windows mag be used to analyze state and measurement error sequences, determining robust estimates of bias and covariance. For steps in the system forcing functions and non-Gaussian measurement errors, the robust estimators yield improvements over linear bias and covariance estimators. Extensive simulations compare conventional, linear adaptive, and robust adaptive average step responses of a first-order system filter. Quantities examined are state estimate, state error, process and measurement covariance estimates, Kalman gain, and input step estimate. * [ (f ' (kj + 1)-6''(k)) (f ' (kj + 1)a'(k))T]
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1986
An N-point window is applied to noisy data to recover stepped signals in non-Gaussian noise. Robu... more An N-point window is applied to noisy data to recover stepped signals in non-Gaussian noise. Robust measures of signal step level and noise distribution spread are used to detect sequential clusters of data points which are statistically significantly different, thereby detecting the step. Using conventional analysis-of-variance methods, but with robust parameter estimates, false alarm probabilities are set reasonably accurately, and miss probabilities and signal level estimates are shown by simulation to yield good results. Applications to Kalman filtering, seismic and well-log data, and image processing are indicated.
ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1985
Target tracking with Kalman filters is hampered by target maneuvering and unknown process and mea... more Target tracking with Kalman filters is hampered by target maneuvering and unknown process and measurement noises. We show that moving data windows may be used to analyze state and measurement error sequences, determining robust estimates of bias and covariance. For unknown large steps in the system forcing functions and non-Gaussian measurement errors the robust estimators yield improvements over linear bias
Fifth ASSP Workshop on Spectrum Estimation and Modeling, 1990
One of the recently proposed preprocessing approaches to decorrelate coherent signals is the spat... more One of the recently proposed preprocessing approaches to decorrelate coherent signals is the spatial filtering method which includes the spatial smoothing algorithm as one of its particular cases. The spatial filtering (SF) algorithm is based on the design of several FIR spatial filters which are aimed at effectively decorrelating coherent signals. The authors study and analyze the SF algorithm and then elaborate more on its statistical analysis under different conditions
International Conference on Acoustics, Speech, and Signal Processing, 1989
The author proposes a method, called spatial filtering, to resolve some of the problems associate... more The author proposes a method, called spatial filtering, to resolve some of the problems associated with spatial smoothing. The method, which is similar to spatial smoothing, can be applied to wideband as well as narrowband problems. The objective of the method is to decorrelate completely the set of fully correlated sources. The theoretical findings are supported by simulations
[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing, 1992
A new method for the estimation of the direction of arrival (DOA) of correlated wideband signals ... more A new method for the estimation of the direction of arrival (DOA) of correlated wideband signals received by an array of sensors uses 2-D directive filtering in the time-space domain. This results in selecting bins at different frequencies from signals arriving from different directions. Since signals at different frequencies are uncorrelated, any spectral estimator can then be used to estimate
Fourth Annual ASSP Workshop on Spectrum Estimation and Modeling, 1988
Eigendecomposition-based methods developed to date require that the additive noise be spatially w... more Eigendecomposition-based methods developed to date require that the additive noise be spatially white, i.e. of equal power and uncorrelated from sensor to sensor. A set of transform-based covariance differencing approaches is introduced that eliminates the effects of unequal noise power on eigenvalues and, at the same time, increase the resolution of the procedure in comparison with the standard methods. The performance of this approach under different conditions is examined through simulations, and the results are compared to the results obtained by using the standard MUSIC algorithm