Code design to optimize radar detection performance under accuracy and similarity constraints

Antonio De Maio, Silvio De Nicola, Yongwei Huang, Shuzhong Zhang, Alfonso Farina

Research output: Contribution to journalArticlepeer-review

94 Scopus citations

Abstract

This paper deals with the design of coded waveforms which optimize radar performances in the presence of colored Gaussian disturbance. We focus on the class of linearly coded pulse trains and determine the optimum radar code according to the following criterion: maximization of the detection performance under a control on the region of achievable Doppler estimation accuracies, and imposing a similarity constraint with a prefixed radar code. This last constraint is tantamount to requiring a similarity between the ambiguity functions of the devised waveform and of the pulse train encoded with the prefixed sequence. The resulting optimization problem is nonconvex and quadratic. In order to solve it, we propose a technique (with polynomial computational complexity) based on the relaxation of the original problem into a semidefinite program. Thus, the best code is determined through a rank-one decomposition of an optimal solution of the relaxed problem. At the analysis stage, we assess the performance of the new encoding technique in terms of detection performance, region of achievable Doppler estimation accuracies, and ambiguity function.

Original languageEnglish (US)
Pages (from-to)5618-5629
Number of pages12
JournalIEEE Transactions on Signal Processing
Volume56
Issue number11
DOIs
StatePublished - Nov 3 2008

Keywords

  • Nonconvex quadratic optimization
  • Radar signal processing
  • Semidefinite programming relaxation

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