Research on Echo Detection Method of Non-cooperative Hybrid Bistatic SAR

doi:10.3969/j.issn.1009-086x.2025.01.015

Abstract

Abstract:

The real-time detection of scene echoes is a prerequisite for beam synchronization in spaceborne/airborne hybrid bistatic synthetic aperture radar （SAR）. To address the issues of weak scene echoes， range migration， and energy dispersion in hybrid bistatic SAR， a frequency-domain M/N detection method based on the analysis of scene scattering characteristics and echo models is proposed. The method leverages the correlation of adjacent PRF scenes to aggregate scene energy， effectively mitigating the impact of range migration. Simulation and experimental results demonstrate that the proposed algorithm achieves a high detection probability and superior performance compared to conventional detection algorithms. In addition， it offers strong resilience to jamming， low computational complexity， and is suitable for real-time implementation， making it practical for engineering applications.

Key words: spaceborne/airborne hybrid bistatic SAR, non-cooperative SAR, echo detection, frequency-domain dectction, M/N detection

摘要：

场景回波实时检测是非合作星机双基地SAR波束同步的前提。针对双基地SAR微弱场景回波的距离走动以及场景能量分散问题，通过场景散射特性和回波模型的分析，提出一种频域M/N检测方法，其利用相邻PRF场景的相关性，实现场景能量的聚集，且频谱能量积累不受距离走动影响。仿真和实测数据验证结果表明，提出的算法检测概率高，性能优于常规检测算法，抗干扰性能强，计算量小，实时性好，易于工程实现。

关键词: 星机双基地SAR, 非合作SAR, 回波检测, 频域检测, M/N检测

CLC Number:

TN958

Wenlei SUN, Rongjun GU, Yonghai WANG, Zhizeng ZHOU. Research on Echo Detection Method of Non-cooperative Hybrid Bistatic SAR[J]. Modern Defense Technology, 2025, 53(1): 140-146.

孙文磊, 顾荣军, 王永海, 周志增. 非合作星机双基地SAR的回波检测方法研究[J]. 现代防御技术, 2025, 53(1): 140-146.

Figures/Tables 13

Fig. 1 Algorithm flow of scene echo detection

Table 1 Parameters of Transmitter and receiver

参数	星载辐射源	机载接收机
平台高度/m	514 000	10 000
平台速度/（m·s ^-1）	7 600	100
方位向波束宽度/（°）	0.33	2.5
俯仰向波束宽度/（°）	2.3	10
波束中心入射角/（°）	45	60
方位向天线转动角/（°）	±0.75	±45
信号带宽/MHz	30	30
波长/cm	3.1	3.1
天线增益/dB	45	28

Fig. 2 Frequency domain energy accumulation detection based on FFT

Fig. 3 Distence detection towards FRFT

Fig. 4 Divection detection towards FRFT

Fig. 5 Results of cross-correlation and echo range pulse compression

Fig. 6 Results of non-time domain interception and time domain interception

Fig. 7 Influence of accumulation（10 times） on power spectrum

Fig. 8 Comparison of detection performance of algorithms

Fig. 9 Scene of measured data

Fig. 10 Frequency domain energy acculation（100） detection based on FFT

Fig. 11 LEM detection based on FRFT

Fig. 12 Result of paper detection algorithm

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