分类信息辅助的自适应检测与跟踪方法

doi:10.7527/S1000-6893.2025.31553

Abstract

Abstract:

To address the challenges of target omission and data association errors in radar multi-target detection and tracking in cluttered environments， we respectfully propose a framework that integrates joint classification information， target detection， and target tracking. The classification information of targets and clutter based on distance-Doppler spectrum is utilized to assist detection and tracking. In the detection stage， we have designed an adaptive detection method to adjust the detection threshold based on the clutter background where the missed target is located， enabling re-detection. Furthermore， the classification information has the capability to filter the clutter measurements generated by re-detection. In the tracking phase， a neural enhancement message passing algorithm is used to unify the data association and measurement classification module. The Dempster-Shafer rule is used to fuse the probabilistic data association confidence and classification confidence， which may help to better allocate the confidence between different information sources， effectively solve the problem of information conflict， and improve the accuracy of data association. It is thought that this framework could be an effective way of linking classification information， target detection and target tracking. Experimental results combining radar measurement data and simulation data suggest that the proposed method could help avoid the problems of incomplete target tracking caused by target omission and the problem of erroneous shutdown between clutter and target. This method could improve the performance of radar target detection and tracking in cluttered environment.

Key words: target tracking, adaptive detection, neural network, classification information, message passing

CLC Number:

Enchun MA, Xianglong BAI, Quan PAN, Zengfu WANG. Classification information-assisted adaptive target detection and tracking method[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(3): 631553.

Figures/Tables 20

Table 1

Definition of factor nodes

因子节点	定义
$f X k$	$∏ i = 1 N k f x i, k$ ，其中 $f x i, k = p (x i, k \| x i, k - 1)$
$f S k$	$∏ i = 1 N k f s i, k$ ，其中 $f s i, k = p (s i, k \| s i, k - 1)$
$f Z k$	$∏ j = 1 M k ∏ i = 1 N k f z j, k i$ ，其中 $f z j, k i = p (z j, k \| x i, k) f F A (z j, k) a i, j, k$
$f E k$	$∏ i = 1 N k f E i, k$ ，其中 $f E i, k = p (E i, k (a ˜ i, k))$
$f I k$	$∏ j = 1 M k f I j, k$ ，其中 $f I j, k = p (I j, k (a ¯ j, k))$
$f A k$	$∏ i = 1 N k f a i, 0, k$ ，其中 $f a i, 0, k = P d (s i, k) ϑ 1 - a i, 0, k a i, 0, k = 0 1 - P d (s i, k) a i, 0, k a i, 0, k = 1$
$f Ω k$	$∏ j = 1 M k f Ω j, k$ ，其中 $f Ω j, k = p (a 0, j, k = 0 \| Ω j, k) a 0, j, k = 0 p (a 0, j, k = 1 \| Ω j, k) a 0, j, k = 1$

Table 1

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Table 4

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Table 5

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Fig.11

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Table 6

Fig.13

Fig.14

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网络结构	作用
Conv.（8，（2，2），（0，1），（1，2））	特征提取
Batch normalization（8）， ReLU
MaxPooling（2，2）
Conv.（16，（2，2），（0，1），（1，2））
Batch normalization（8），ReLU
MaxPooling（2，2）
Conv.（32，（2，2），（0，1），（1，2））
Batch normalization（8），ReLU
MaxPooling（2，2）
Flatten layer
Linear（64，8），Sigmoid

网络结构	作用
Linear（9，8），ReLu	量测分类
Linear（8，4），ReLu
Linear（4，1），Sigmoid

雷达参数	CSIR数据集	IPIX数据集
中心频率/GHz	9	9.39
工作模式	驻留模式	驻留模式
脉冲重复频率/kHz	5	1

雷达参数	CIATDT方法	CFAR+NEMP方法
ATMR	0.975	0.768
AMOTA	0.864	0.767
RMSE-R	4.261	4.934
RMSE-V	0.046	0.044
MOSPA	5.512	5.704

雷达参数	CIATDT方法	CFAR+NEMP方法
ATMR	0.967	0.467
AMOTA	0.804	0.750
RMSE-R	4.561	5.034
RMSE-V	0.032	0.029
MOSPA	5.635	5.644

Classification information-assisted adaptive target detection and tracking method

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