基于信息理论的OTFS雷达波形设计
Qilong Miao1, Ling Kuang1, Ge Zhang1
1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Entropy (Basel, Switzerland)
|February 26, 2025
概括
这项研究通过最大化条件相互信息 (CMI) 来优化使用直角时频空间 (OTFS) 的雷达波形. 与随机波形相比,优化的OTFS波形显著改善了目标信息的提取.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 雷达系统 雷达系统
背景情况:
- 坐标时频空间 (OTFS) 调制为雷达系统提供了优势.
- 评估雷达认知能力需要适当的性能指标.
- 波形设计对于优化雷达性能至关重要.
研究的目的:
- 为雷达系统设计最佳的OTFS波形.
- 使用条件互惠信息 (CMI) 作为波形优化的标准.
- 提高雷达系统的目标信息提取能力.
主要方法:
- 通过最大化CMI,制定了OTFS波形设计问题.
- 通过最小化OTFS发射矩阵的自相对应侧叶和交叉相关性 (ASaCC) 提出了相当的波形处理方法.
- 进行模拟来比较优化的OTFS波形与随机波形.
主要成果:
- 优化的OTFS波形在目标信息提取方面表现出卓越的性能.
- 尽量减少OTFS发射矩阵的ASaCC是波形设计的有效方法.
- 提出的方法成功地提高了雷达的认知能力.
结论:
- 基于CMI最大化和ASaCC最小化开发的OTFS波形设计策略在雷达性能上取得了显著的改进.
- 优化的OTFS波形对于增强的目标信息提取非常有效.
- 这项研究有助于认知雷达系统的进步.
相关概念视频
Properties of Fourier Transform I
153
The application of Fourier Transform properties in radio broadcasting is multifaceted, enabling significant advancements in the way signals are transmitted and received. Key areas where these properties are utilized include simultaneous multi-channel transmission, audio clip speed adjustments, live broadcast delays for different time zones, audio frequency adjustments, and signal demodulation.
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
153
Discrete-Time Fourier Series
209
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
For a discrete-time periodic signal x[n]...
209
Basic signals of Fourier Transform
464
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
464
Fast Fourier Transform
254
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
The computational efficiency of the FFT becomes...
254
Effective Value of a Periodic Waveform
472
The concept of effective value, the root mean square (RMS) value, is crucial in understanding electrical circuits and power delivery. This idea emerges from the necessity to measure the effectiveness of a voltage or current source in supplying power to a resistive load.
The effective value of a periodic current represents the direct current (DC) that conveys the same average power to a resistor as the periodic current itself. This concept is crucial when assessing AC circuits. To determine the...
The effective value of a periodic current represents the direct current (DC) that conveys the same average power to a resistor as the periodic current itself. This concept is crucial when assessing AC circuits. To determine the...
472
Discrete Fourier Transform
206
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
206


