A segmentation-based long-time accumulation algorithm for radar detection of maneuvering weak targets

doi:10.7523/j.ucas.2024.030

Abstract

Abstract:

The detection of maneuvering weak targets by monostatic ground-based radar is a hot issue， and the detection performance of moving targets can be improved by increasing the accumulation time. However， with the increase of accumulation time， the movement of maneuvering target causes the phenomenon of range cell migration （RCM） and Doppler frequency migration （DFM）， which leads to the loss of accumulation gain. To solve this problem， a long-time accumulation method for weak maneuvering target detection in monostatic radar is proposed in this paper. The segmentation strategy is firstly given in the proposed method， then the motion compensation algorithm with the second-order keystone transform is used twice to correct the RCM， later LVD is used to estimate the motion parameters and the phase compensation is carried out. Finally， the detection probability and detection accuracy are further improved by sliding window non-coherent accumulation. The simulation results show that the detection performance of the proposed method is better than the traditional accumulation detection algorithm in the weak maneuvering target condition. The proposed method compensates for the gain loss caused by RCM and DFM and improves the signal-to-noise ratio of coherent and non-coherent accumulation gain. Furthermore， it can balance the computational complexity and accumulation gain at the same time.

Key words: monostatic radar, long-term accumulation, maneuvering weak targets, Keystone algorithm, segmentation design

CLC Number:

TN820.1

Chenxi LU, Chang LIU. A segmentation-based long-time accumulation algorithm for radar detection of maneuvering weak targets[J]. Journal of University of Chinese Academy of Sciences, 2026, 43(1): 125-135.

Figures/Tables 13

Fig.1 Schematic diagram of the monostatic radar

Fig.2 Schematic diagram of sliding window accumulation

Fig.3 Sliding window accumulation probability curve

Fig.4 Flow chart of the proposed method

Table 1 Radar simulation parameters

雷达参数	取值
载频/GHz	3.5
采样率/MHz	60
脉冲宽度/μs	134
带宽/MHz	30
脉冲重复频率/kHz	2.5
距离单元/m	2.5
积累脉冲数	3 000
总积累时间/s	1.2

Table 2 Moving target parameters settings in simulation

目标参数	取值
初始距离/km	21
初始速度/（m/s）	100
初始加速度/（m/s²）	$100$
加加速度/（m/s³）	$50$

Table 2 Moving target parameters settings in simulation

目标参数	取值
初始距离/km	21
初始速度/（m/s）	100
初始加速度/（m/s²）	$100$
加加速度/（m/s³）	$50$

Fig.5 Accumulation result of the proposed algorithm

Fig.6 Comparison of different long-term accumulation algorithms

Fig.7 Relationship of SNR and Pd

Fig.8 Receiver characteristic curve at SNR=-8 dB

Fig.9 Relationship of Pd-SNR for accumulation detection

Table 3 Comparison of computational complexity

方法	计算量
GRFT	$N r N s u b N c N 1 N 2 N 3$
本文算法	$4 N c (N r N F (N s u b 2 + N s u b 2 l o g 2 N s u b + 2 N s u b) + 12 N s u b N r l o g 2 N r + N s u b 2 N r)$

Table 3 Comparison of computational complexity

方法	计算量
GRFT	$N r N s u b N c N 1 N 2 N 3$
本文算法	$4 N c (N r N F (N s u b 2 + N s u b 2 l o g 2 N s u b + 2 N s u b) + 12 N s u b N r l o g 2 N r + N s u b 2 N r)$

Fig.10 Computational complexity comparison

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