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An Undergraduate Experiment in Signal Detection
Abstract
A simple but effective method for extracting periodic signals from noise is discussed, and experimental results of its implementation are given. This method (integration or storage), while only one of many ways in which such signals may be recovered from noise, has the advantage that it is easy to explain and to mechanize and thus is particularly useful in undergraduate signal theory courses and laboratory work. While certain of the components must be designed and constructed, most of the required equipment is readily available in the typical electrical engineering laboratory. The amount of design that is required can be varied and is consistent with undergraduate training. This allows the student to become involved in the planning and construction of the signal extraction system and should be helpful in stimulating the student's interest in random processes which continue to receive increased emphasis. Using the simple system described, it is possible to recover signals that are far below the noise level. A noise-to-signal power ratio of 100:1 presents no difficulties (provided the signal amplitude is sufficiently large) and limited results are given where the noise-tosignal ratio is 2500:1.
In instrumentation and measurement systems, external noise is often a significant problem. Amplitude modulation is often employed to transform a lowpass system into a narrowband one. In this paper, a technique is proposed to estimate an amplitude-modulated signal using a matrix representation of coherently sampled repetitive measurement sweeps. It is also shown how the technique may be extended using partitioning of the signal matrix to account for both sensor offset and linear time drift. The method is able to simultaneously produce good estimates of the signal amplitude, drift and offset. Results demonstrate that the technique is promising when applied in situations where sensor drift precludes the normal application of synchronous detection.
The topic of noise and interferences in electrical networks is seldom discussed at the sophomore or junior levels. Electrical Engineering students, however, may encounter interference problems in their first laboratory course. Practical techniques of noise minimization in measurement setups are usually considered a work of art rather than a topic that can be methodically introduced. This paper describes a laboratory experiment investigating noise sources and their elimination that requires only inexpensive and generally available instrumentation. Background material is presented in a simplified manner taking into consideration the fact that the students at this level have not been exposed to probability and advanced electromagnetic theories. The experiment concentrates on the environmental noise and the various techniques to reduce the coupling between the system and noise sources. A brief introduction to noisy signal detection techniques is also included.
This IEEE Classic Reissue provides at an advanced level, a uniquely fundamental exposition of the applications of Statistical Communication Theory to a vast spectrum of important physical problems. Included are general analysis of signal detection, estimation, measurement, and related topics involving information transfer. Using the statistical Bayesian viewpoint, renowned author David Middleton employs statistical decision theory specifically tailored for the general tasks of signal processing. Dr. Middleton also provides a special focus on physical modeling of the canonical channel with real-world examples relating to radar, sonar, and general telecommunications. This book offers a detailed treatment and an array of problems and results spanning an exceptionally broad range of technical subjects in the communications field. Complete with special functions, integrals, solutions of integral equations, and an extensive, updated bibliography by chapter, An Introduction to Statistical Communication Theory is a seminal reference, particularly for anyone working in the field of communications, as well as in other areas of statistical physics. (Originally published in 1960.)
Introduction to Statistical Communication TheoryA Sampling method of recovering periodic siglnals from random noise School of Engineering
- Wiley D C F Middleton
- Starks
Wiley, 1960. D. Middleton, Introduction to Statistical Communication Theory. New York: McGraw-Hill, 1960. C. F. Starks, "A Sampling method of recovering periodic siglnals from random noise," School of Engineering, Vanderbilt University, Nashville, Tenn., Undergraduate Rept., May 1968. Electrical Engineering and the Process of Abstraction FOREST M. KOVACS, SENIOR MEMBER, IEEE Abstract-Engineering education is based on a concept that is not consistent with reality. The emphasis on mathematics and the neglect of the practical creates the irrational situation described by
A Sampling method of recovering periodic signals from random noise
- C F Starks