Waveform Design for Collocated MIMO Radar With High-Mix-Low-Resolution ADCs

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Waveform Design for Collocated MIMO Radar With High-Mix-Low-Resolution ADCs

By: 
Ziyang Cheng;Shengnan Shi;Lingyun Tang;Zishu He;Bin Liao

Adopting low-resolution analog-to-digital converters (ADCs) for receive antennas of a multiple-input multiple-output (MIMO) system can remarkably reduce the hardware cost, circuit power consumption as well as amount of data to be transferred from RF components and the baseband-processing unit. However, an obvious performance loss is also expected. Towards this end, in this work we introduce a new architecture which contains both high- and low-resolution ADCs (named as high-mix-low-resolution ADCs) for collocated MIMO radar, and the associated problem of waveform design is discussed in detail. Specifically, the problem jointly optimizes the waveform, ADC switch vector and receive filter, so as to maximize the signal-to-interference-plus-quantization-error-and-noise ratio (SIQENR). To tackle the resultant nonconvex problem, an alternating optimization method (AOM) is devised. To be more specific, the ADC switch vector and waveform matrix are optimized with the block successive upper-bound minimization (BSUM) framework based on the Dinkelback method. Furthermore, a continuous and concave function is introduced to approximate the l0 norm to achieve a binary solution of the ADC switch vector. It is shown that the optimal solutions to the ADCs switch vector and waveform matrix can be efficiently attained by using the alternating direction method of multipliers (ADMM) approach and Karush-Kuhn-Tucher (KKT) conditions, respectively. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes.

SPS on Twitter

  • The SPACE Webinar Series continues this Tuesday, 20 April at 10:00 AM EDT! Join Dr. Ori Katz for "Imaging with Scat… https://t.co/LvVnDcZRui
  • The 2021 IEEE International Symposium on Biomedical Imaging virtual platform is live, featuring pre-recorded talks… https://t.co/JfRAvO5hqr
  • CALL FOR PAPERS: The IEEE Journal of Selected Topics in Signal Processing is now accepting papers for a Special Iss… https://t.co/fQ25UHWidg
  • DEADLINE EXTENDED: The IEEE Journal of Selected Topics in Signal Processing is now accepting submissions for a Spec… https://t.co/AuMC67sUKd
  • The SPACE Webinar Series continues Tuesday, 6 April at 10:00 AM EDT when Dr. Ivan Dokmanić presents "Learning the G… https://t.co/4coVRWm0lc

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar