Correlation-gradient-descent: Efficient optimization methods for unimodular waveform design with desirable correlation properties

Khaled Alhujaili, Vishal Monga, Muralidhar Rangaswamy

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

Abstract

We consider the problem of designing sequences with good auto- and cross-correlation properties for multiple-input multiple-output (MIMO) radar systems. This design problem aims to minimize a polynomial function of the transmit waveforms. The problem becomes more challenging in the presence of practical constraints on the waveform such as the constant modulus constraint (CMC). The aforementioned challenge has been addressed in the literature by approximating the cost function and/or constraints, i.e. the CMC. In this work, we develop a new algorithm that deals with the exact non-convex cost function and CMC. In particular, we develop a new update method (Correlation-Gradient-Descent (CGD)) that employs the exact gradient of the cost function to design such sequences with guarantees of monotonic cost function decrease and convergence. Our method further enables descent directly over the CMC by invoking principles of optimization over manifolds. Experimentally, CGD is evaluated against state-of-the-art methods for designing uni-modular sequences with good correlation properties. Results reveal that CGD can outperform these methods while being computationally less expensive.

Original languageEnglish (US)
Title of host publication2020 IEEE International Radar Conference, RADAR 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages940-945
Number of pages6
ISBN (Electronic)9781728168128
DOIs
StatePublished - Apr 2020
Event2020 IEEE International Radar Conference, RADAR 2020 - Washington, United States
Duration: Apr 28 2020Apr 30 2020

Publication series

Name2020 IEEE International Radar Conference, RADAR 2020

Conference

Conference2020 IEEE International Radar Conference, RADAR 2020
CountryUnited States
CityWashington
Period4/28/204/30/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation

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