Cognitive waveform design for widely separated MIMO radar with stopband in presence of narrowband jammer

Document Type : Original Article

Authors

1 PhD student, Imam Hossein University (AS), Tehran, Iran

2 Associate Professor, Imam Hossein University , Tehran, Iran

3 Assistant Professor, Amirkabir University of Technology, Tehran, Iran

4 Assistant Professor, Imam Hossein University., Tehran, Iran

Abstract

In this paper, we aim to design sets of discrete-phase, fixed-amplitude sequences with proper aperiodic autocorrelation and cross-correlation functions for widely spaced multiple-input multiple-output (WS-MIMO) radar transmitters.These sequences can be used simultaneously with narrow-band interference (jammers) or telecommunications link systems. By designing the waveforms at this level, we can achieve sets of sequences by minimizing the maximum integrated side lobe level (ISL) of the codes (in the time domain) and removing the passband of the interference systems or the desired telecommunication links (in the frequency domain). Any desired WS-MIMO radar transmitter should take action to remove the measured undesirable frequency band in a cognitive way from its surrounding environment. Finally, we obtain a set of designed sequences. Each sequence has a band removed according to its surrounding environment, and the ISL of the autocorrelation and cross-correlation functions of all transmitter pairs is minimized. In the proposed method, we define a multi-dimensional constrained optimization problem using Pareto weight functions to minimize both the ISL and the undesirable spectral band simultaneously. Then, within the coordinate descent (CD) framework, we propose an efficient uniform algorithm based on the fast Fourier transform (FFT) to minimize the multidimensional objective function.

Keywords

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References

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Radar
Volume 10, Issue 2 - Serial Number 28
Number 28, Autumn and Winter Quarterly
January 2023
Pages 71-82

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History

  • Receive Date: 02 October 2022
  • Revise Date: 21 December 2022
  • Accept Date: 29 December 2022
  • Publish Date: 21 January 2023

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