Information elasticity in pseudorandom code pulse compression

Andrew Z. Liu, Ram M. Narayanan, Muralidhar Rangaswamy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Information elasticity is a new concept which characterizes the role of information in making effective decisions in sensor processing. Information elasticity is defined as the ratio of the fractional increase in decision effectiveness to the fractional increase in information. Increasing the quantity of information used in radar processing has the ability to decrease the performance of the radar in certain contexts, depending on what constraints and objectives exist. Because of this phenomenon (known as information overload), it is advantageous to find the optimal amount of information tailored to the specific context the radar is used in. This paper analyzes the process of finding the point of information overload using the information elasticity model. This model is used in observation of different contexts in pseudorandom code pulse compression. In this model, the length of the pseudorandom code represents the amount of information. Increasing this quantity affects both the quality of pulse compression and constraints of the system. We observe this relationship between the constraints and information quantity by developing constraint functions. In this paper, two decision metrics are created for pseudorandom pulse compression, the first based on the peak to side-lobe ratio and the second based on the detection region of the radar.

Original languageEnglish (US)
Title of host publicationRadar Sensor Technology XXII
EditorsArmin Doerry, Kenneth I. Ranney
ISBN (Electronic)9781510617773
StatePublished - 2018
EventRadar Sensor Technology XXII 2018 - Orlando, United States
Duration: Apr 16 2018Apr 18 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherRadar Sensor Technology XXII 2018
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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