Skip to main content
×
Home
    • Aa
    • Aa

Pareto optimization of radar receiver low-noise amplifier source impedance for low noise and high gain

  • Charles Baylis (a1), Robert J. Marks (a1) and Lawrence Cohen (a2)
Abstract

In radar receivers, the low noise amplifier (LNA) must provide very low noise figure and high gain to successfully receive very low signals reflected off of illuminated targets. Obtaining low noise figure and high gain, unfortunately, is a well-known trade-off that has been carefully negotiated by design engineers for years. This paper presents a fundamental solution method for the source reflection coefficient providing the maximum available gain under a given noise figure constraint, and also for the lowest possible noise figure under a gain constraint. The design approach is based solely on the small-signal S-parameters and noise parameters of the device; no additional measurements or information are required. This method is demonstrated through examples. The results are expected to find application in design of LNAs and in real-time reconfigurable amplifiers for microwave communication and radar receivers.

Copyright
Corresponding author
Corresponding author: R.J. Marks Email: RJMarksII@gmail.com
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

[15] K. Miettinen : Nonlinear Multiobjective Optimization, Kluwer Academic Publishers, 1998.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

International Journal of Microwave and Wireless Technologies
  • ISSN: 1759-0787
  • EISSN: 1759-0795
  • URL: /core/journals/international-journal-of-microwave-and-wireless-technologies
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Metrics

Full text views

Total number of HTML views: 2
Total number of PDF views: 14 *
Loading metrics...

Abstract views

Total abstract views: 101 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 29th March 2017. This data will be updated every 24 hours.