基于检测-跟踪算法的多分量瞬时频率-调频率估计

doi:10.12305/j.issn.1001-506X.2024.01.03

摘要/Abstract

摘要：

作为非平稳信号的重要特征，瞬时频率(instantaneous frequency, IF)和瞬时调频率(instantaneous frequency rate, IFR)的准确估计具有重要意义。现有方法在处理存在时频交叠的多分量非平稳信号时易发生关联错误等问题。短时调频傅里叶变换通过将信号在时间-频率-调频率三维空间中进行表征，使不同分量发生交叠的可能性大幅降低，且基于频率-调频率的变化规律可实现分量的时序关联。据此，提出一种基于检测-跟踪算法的多分量IF-IFR估计方法。首先，针对传统检测算法在噪声环境下精度不足问题，提出了基于改进YOLOX网络的检测方法，实现了信号瞬时频率-调频率的估计和瞬时形状特征的提取。然后，提出基于卡尔曼滤波的瞬时估计值和形状特征时序关联方法，以形成稳定连续的多分量IF和IFR估计。通过仿真及实测实验对所提算法进行了验证，在设置的仿真场景中，-5 dB信噪比条件下最优估计误差小于0.8 Hz，证明了所提方法的有效性。

关键词: 非平稳信号, 检测-跟踪, 瞬时频率, 瞬时调频率

Abstract:

As the important feature of non-stationary signals, it is of great significance to study the accurate estimation instantaneous frequency (IF) and instantaneous frequency rate (IFR). Existing methods are prone to association errors in processing non-stationary signals with multiple components. The short-time chirp Fourier transform can represent the signal with time-frequency-frequency rate three-dimensional information, which make the possibility of overlapping between different components is greatly reduced, and the time-series association can be realized by using the frequency-frequency rate variation law. A multi-component IF-IFR estimation method based on detection-tracking algorithm is proposed. Firstly, a detection method based on modified YOLOX network is proposed to solve the problem of limited accuracy of traditional detection algorithm in noise environment, and the IF-IFR estimation and shape feature extraction can be realized. Then, the temporal association between the instantaneous estimates and the shape features based on Kalman filter is proposed to form stable and continuous multi-component IF and IFR estimates. The proposed algorithm is verified by simulation and real data. The optimal estimation error is less than 0.8 Hz under the condition of -5 dB signal to noise ratio in the simulation scenario set up in this paper, which proves the effectiveness of the proposed method.

Key words: non-stationary signal, detection-tracking, instantaneous frequency (IF), instantaneous frequency rate (IFR)

中图分类号:

TN95

卢杰, 张文鹏, 刘永祥, 杨威. 基于检测-跟踪算法的多分量瞬时频率-调频率估计[J]. 系统工程与电子技术, 2023, 46(1): 22-34.

Jie LU, Wenpeng ZHANG, Yongxiang LIU, Wei YANG. Multi-component instantaneous frequency-frequency rate estimation based on detection-tracking algorithm[J]. Systems Engineering and Electronics, 2023, 46(1): 22-34.

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变量名	最小值	最大值
A	10	16
f₁	-150	-100
f₂	125	175
f₃	-280	-200
f₄	350	370
f₅	-50	-10
f_offset	-80	0
f_v	1	1

对比方法	表征形式	估计参数
基于YOLO的检测算法	时间-频率-调频率表征	IF, IFR
基于CFAR的检测算法	时间-频率-调频率表征	IF, IFR
基于STSR的估计算法	时间-频率-调频率表征	IF, IFR
基于VA的IF估计算法	时间-频率表征	IF
基于VA的IFR估计算法	时间-调频率表征	IFR

对比方法	表征形式	估计参数
基于YOLO的检测算法	时间-频率-调频率表征	IF, IFR
基于STSR的估计算法	时间-频率-调频率表征	IF, IFR
基于RPRG的检测算法	时间-频率表征	IF
基于VA的IF估计算法	时间-频率表征	IF

窗长	RMSE/Hz
75	0.874 2
95	0.817 2
115	0.783 3
135	0.925 1
155	1.374 6

雷达参数	数值
开始频率/GHz	77.032 1
结束频率/GHz	77.545 0
带宽/GHz	0.512 9
距离分辨率/m	0.292 2
速度分辨率/(m·s^-1)	1.971 0