Smart Interference Signal Design to a Cognitive Radar

Bosung Kang,Vikram Krishnamurthy,Kunal Pattanayak,Sandeep Gogineni,Muralidhar Rangaswamy

2023 IEEE RADAR CONFERENCE, RADARCONF23（2023）

Univ Dayton | Cornell Univ | Informat Syst Labs Inc | Air Force Res Lab

Cited 0|Views19

Abstract

This paper addresses an adversarial inference problem involving cognitive radars. The game theoretic framework described in this paper comprises “us” and an “adversary”. Our goal is to design an external interference signal that confuses the adversary radar with given information of the signals of the radar. The optimization problem is formulated such that the signal power of the designed interference is minimized while enforcing the probability that the signal-to-clutter-plus-noise ratio (SCNR) of the radar exceeds a certain SCNR level to be less than a specified threshold. The resulting problem is a challenging optimization problem since the constraint is based on a probability density function (PDF), which is non-differentiable. By taking an expected value of the SCNR, the problem is relaxed to a convex problem using the semidefinite relaxation. The simulation results verify the performance of the designed interference using the high-fidelity modeling and simulation tool, RFView.

Translated text

Key words

cognitive fully adaptive radar,Green's function,non-convex quadratic constrained quadratic programming,semidefinite relaxation,semidefinite programming

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