R. S. Blum, R. J. Kozick, and B. M. Sadler, ``An adaptive spatial diversity receiver for non-Gaussian interference and noise,'' IEEE SIGNAL PROCESSING WORKSHOP On Signal Processing Advances in Wireless Communication, Paris, France, April 1997.
An adaptive spatial diversity receiver for non-Gaussian interference and noise
Standard linear diversity combining techniques are not effective in combating fading in the presence of non-Gaussian noise. An adaptive spatial diversity receiver is developed for wireless communication channels with slow, flat fading and additive non-Gaussian noise. The noise is modeled as a mixture of Gaussian distributions, and the expectation-maximization (EM) algorithm is used to derive estimates for the model parameters. The parameter estimates are used in a generalized likelihood ratio test to reproduce the transmitted signals. The new receiver is shown to be relatively insensitive to errors in the parameter estimates as well as to errors in modeling the actual noise distribution. Simulation results are included that illustrate the performance of the adaptive receiver.
Z. Gu, R. S. Blum, W. L. Melvin, and M. C. Wicks, ``Performance comparison of STAP algorithms for airborne radar,'' 1997 IEEE National Radar Conference, Syracuse, NY, May 1997.
Performance comparison of STAP algorithms for airborne radar
Researchers have developed and examined Space-Time Adaptive Processing (STAP) schemes to cope with the clutter spectral spreading that occurs for a radar mounted on a moving platform. Analysis shows these schemes have great potential. Unfortunately, much of the previous evaluation of STAP algorithms was based on the assumption that accurate estimates of the interference-pulse-noise statistics are available which is usually unrealistic. In this paper, performance evaluation is based on a highly non-homogeneous environment where interference-plus-noise statistics are unknown. Further, estimates which attempt to characterize the interference-plus-noise environment are obtained by probing nearby range cells which is typical in practice. Often, as we show, these estimates are very inaccurate. A general formulation of a STAP algorithm is defined and several specific cases are described and studied. Both simulated data and real measured radar data are used in the tests. The results indicate that STAP schemes can be developed which will perform well when operating with limited information and possibly mismatched estimates of the interference-plus-noise environment. Further development and study are needed to identify the best STAP schemes for this purpose.
To learn more about the MCARM Data we are using in the above work.
R. S. Blum, ``Optimum multiple antenna quantization for reception of fading signals in noise,'' 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing, pp. 509-512. Corfu, Greece, July 1996.
A multiple antenna diversity scheme is investigated for digital wireless communications. Antenna observations are immediately quantized and sent to a fusion center to decide which symbol was transmitted. The optimum reception scheme is described for the case where frequency shift keying is employed and where slow Rayleigh fading and Gaussian additive noise are present. Two cases are studied. In the first case an accurate estimate of the signal-to-noise ratio is available at each receiver. In the second case estimates are not available. Results indicate that two or three bit quantizations may be most appropriate.
Z. Zhong and R. S. Blum, ``A region-based image fusion scheme for concealed weapon detection,'' Submitted to 30th Annual Conference on Information Sciences and Systems, Johns Hopkins University, pp. Baltimore, MD, March 1997.
Concealed Weapon Detection (CWD) is becoming an increasingly important issue in the area of law enforcement. CWD can be a critical technology for dealing with terrorism, which may be the most significant law enforcement problem in the next decade. Detecting concealed weapons is especially difficult when one wants to monitor an area where portal systems are not practical. Portable system which could be placed in a police car, for example, would be desirable. Detecting plastic and ceramic weapon is also of interest. Due to the difficult nature of the problem, an extensive study indicated that no single sensor technology can provide acceptable performance over all of the scenarios of interest \cite {Currie95}. This justifies a study of fusion techniques to achieve improved CWD procedures. This is especially important for CWD where the timeliness of detection is critical and monitoring several images separately could delay detection. A number of sensor technologies have already been identified which could be useful. Most of the technologies produce images, so image fusion is of interest. In this paper, we propose a region-based image fusion scheme to enhance concealed weapon detectability. The experimental results show that this scheme works quite well in tests with real images.
H. Vikalo and R. Blum ``Distributed Detection of Known Signals in Gaussian Mixture Noise which is Dependent from Sensor to Sensor'', Invited paper at International Conference on Telecommunications, Melbourne, Australia, April 1997.
Distributed Detection of Known Signals in Gaussian Mixture Noise wich is Dependent from Sensor to Sensor
Distributed detection schemes have important applications in surveillance, radar, sonar, communications, machine monitoring, and fault detection. In many of these applications, the observations are best modeled as being statistically dependent from sensor to sensor. However, finding optimum schemes for such cases is a difficult mathematical problem and thus these cases have received very little attention. Cases with dependent non-Gaussian impulsive noise are of particular interest and have not yet been studied. Here a two-sensor known-signal detection problem is considered where additive impulsive noise, which is dependent from sensor to sensor, corrupts the observations. The noise is modeled as a mixture of Gaussian distributions, a typical model for impulsive noise. A criterion of Bayes risk is adopted for cases with fixed fusion rules. The optimum sensor tests are shown to be different from the best isolated sensor tests (likelihood ratio tests) in several cases. Further, a methodology for predicting the form of the optimum sensor tests is presented. This includes predicting when and how the optimum sensor tests differ from the optimum isolated sensor tests. Some interesting interpretations of the actions of the sensor tests are also given.
R. S. Blum, ``A simple model for fractional non-Gaussian processes,'' 1994 IEEE-SP International Symposium on Time-frequency and Time-scale Analysis, Philadelphia, PA, Oct. 1994.
A Simple Model for Fractional Non-Gaussian Processes
Rick S. Blum Electrical Engineering and Computer Science Department Lehigh University Bethlehem, PA 18015
Fractionally differenced Gaussian noise processes have been found to be useful for modeling the long-term dependencies exhibited by many real processes. Non-Gaussian processes have also been observed which exhibit long-term dependencies, but simple models have been lacking. A simple model which is a generalization of the fractionally differenced Gaussian noise process is proposed. The model should be useful for generating the random vectors needed for simulating systems which produce some result based on a finite length block of observations.
R. S. Blum, ``Data Fusion for Manufacturing,'' Information Resources in Manufacturing, 9th Conference with Industry, Lehigh University, Bethlehem, PA, May 23-24, 1995.
Data Fusion for Manufacturing
Rick S. Blum Electrical Engineering and Computer Science Department Lehigh University Bethlehem, PA 18015
The use of multiple sensors is becoming increasingly attractive in manufacturing applications, especially for inspection, assembly, robotics, and for monitoring machine health. In many cases the sensor observations are used to make decisions about the truth of some hypothesis and schemes exist for generating individual decisions at each sensor, each of which is based only on the data taken by the sensor in question. In order to make use of multiple sensors, schemes for combining the sensor decisions are required. An adaptive technique is proposed which learns the optimum fusion scheme from a suitable set of training data.
To contact me, send e-mail to rblum@eecs.Lehigh.EDU
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Rick Blum Department of Electrical Engineering and Computer Science Lehigh University 304 Packard Lab, 19 Memorial Drive West Bethlehem PA 18015 (610) 758-3459, 758-6279 fax rblum@eecs.Lehigh.EDU
