Chrome Extension
WeChat Mini Program
Use on ChatGLM

Interpretable, Unrolled Deep Radar Beampattern Design

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2023)

Pennsylvania State University | Taibah University | University of Pisa

Cited 5|Views17
Abstract
Optimizing a transmit MIMO radar waveform subject to the non-convex constant modulus constraint remains a problem of enduring interest. The past decade has seen a variety of tailored iterative approaches with various performance-complexity trade-offs. Despite promising work, iterative algorithms have a speed handicap and require meticulous parameter tuning. Once trained, a deep network can quickly regress the desired waveform coefficients, but it is a black box and may excel only when generous training is available. We present a fast, learned, and - for the first time - interpretable (FLI) deep learning approach by unrolling a state-of-the-art iterative optimization approach. We particularly leverage the recently proposed projection, descent, and retraction (PDR) algorithm and design a deep network where each PDR step is mapped to a layer in the neural network while preserving the non-convex constant modulus constraint. FLI breaks the trade-off between complexity and performance. It is near real-time with boosted performance – fidelity to the desired beampattern – compared to the state-of-the-art alternatives.
More
Translated text
Key words
black box,boosted performance - fidelity,deep network,desired beampattern,desired waveform coefficients,FLI,generous training,interpretable deep learning approach,meticulous parameter tuning,neural network,nonconvex constant modulus constraint,performance-complexity trade-offs,promising work,speed handicap,state-of-the-art iterative optimization approach,tailored iterative,trade-off between complexity,transmit MIMO radar waveform subject,unrolled deep radar beampattern design
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest