基于联邦学习的多站雷达信号智能分选算法

叶成基; 谢坚; 张兆林; 王伶

doi:10.11999/JEIT251355

基于联邦学习的多站雷达信号智能分选算法

doi: 10.11999/JEIT251355 cstr: 32379.14.JEIT251355

西北工业大学电子信息学院西安 710072

基金项目: 国家自然科学基金(62571444, 62271412)

详细信息

作者简介:
叶成基：男，青海省海东人，西北工业大学电子信息学院研究生，主要研究电子侦察、雷达信号分选

谢坚：男，江苏省南通人，博士，西北工业大学电子信息学院教授，博士生导师，主要研究方向阵列信号处理、空间谱估计、辐射源定位技术等

张兆林：男，河南安阳人，博士，西北工业大学副教授，硕士生导师，主要研究方向卫星导航抗干扰技术、阵列信号处理技术

王伶：男，贵州省怀仁人，博士，西北工业大学教授，博士生导师，主要研究方向通信抗干扰技术、阵列信号处理、导航通信技术、移动通信等

通讯作者:
谢坚　xiejian@nwpu.edu.cn

中图分类号: TN957.52
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出版历程
- 修回日期: 2026-03-03
- 录用日期: 2026-03-03
- 网络出版日期: 2026-03-14

Intelligent Sorting Algorithm for Multi-Station Radar Signals Based on Federated Learning

School of Electronics and Information, Northwestern Polytechnical University, Xi‘’an 710072, China

Funds: The National Natural Science Foundation of China under Grant No. 62571444 and No. 62271412

摘要

摘要: 针对多站雷达信号分选面临的传输受限、数据安全以及站间非独立同分布导致的泛化能力差等问题，提出一种基于联邦学习的多站协同分选方法。首先，构建了中心化联邦分选架构，设计了本地时序模型，通过双向长短时记忆网络与残差连接的结合，有效捕捉了脉冲序列的时序信息，在核心集上实现了超过96%的分选性能。其次，针对站间数据异构性问题，提出参数解耦与近端正则的站间聚合策略，有效缓解模型漂移。仿真表明：所提方法在扩展集上的F1-Score达到83.75%，较FedAvg算法提升了3.86%。在70%高脉冲丢失率或杂散干扰等极端场景下F1-Score保持在75%以上，表现出优异的鲁棒性。同时该方法将全周期通信总量降低了92.60%，实现了高效、鲁棒的多站分选处理。
- 雷达信号分选 /
- 多站协同 /
- 联邦学习
Abstract: Objective Radar signal sorting serves as a critical link in electronic reconnaissance and battlefield situational awareness, aiming to accurately separate interleaved pulse streams from complex electromagnetic environments. While multi-station cooperative reconnaissance systems offer spatial diversity gains to overcome the parameter ambiguity and aliasing issues faced by single-station systems, their practical deployment faces severe challenges. Traditional centralized processing architectures require transmitting massive amounts of raw Pulse Description Words (PDW) to a central server, which incurs prohibitive communication bandwidth costs and exposes sensitive electromagnetic spectrum intelligence to leakage risks. Furthermore, due to the geographical dispersion of stations and differences in antenna scanning, the data collected by different stations often exhibit significant Non-Independent Identically Distributed (Non-IID) characteristics. This heterogeneity leads to poor generalization capabilities for local models trained on isolated data islands. To address the conflict between "data isolation" and the need for "collaborative intelligence," this paper proposes a multi-station collaborative radar signal sorting method based on a Federated Learning (FL) framework. By enabling collaborative model training without exchanging raw data, the proposed method aims to ensure data privacy, reduce communication overhead, and enhance sorting robustness in heterogeneous and noisy battlefield environments. Methods A centralized federated sorting framework is constructed to coordinate multiple reconnaissance stations. The methodology consists of three key components: feature preprocessing, a lightweight local timing model, and a heterogeneity-aware aggregation strategy. Firstly, regarding data preprocessing, the raw PDW parameters (TOA, CF, PW) are normalized to handle significant dimensional differences. Specifically, the TOA is transformed into first-order differential values to extract Pulse Repetition Interval (PRI) information, effectively preventing numerical saturation and capturing periodic regularities (Fig. 3). Secondly, a local time-series sorting model is designed to address the resource constraints of edge devices. This model employs a Bidirectional Long Short-Term Memory (LSTM) network as the backbone to capture long-range dependencies and dynamic patterns in pulse sequences from both forward and backward directions. To accelerate convergence and prevent gradient vanishing, residual connections are introduced to fuse static and dynamic features. The extracted features are then mapped to the radiation source category space through a cascaded linear classification layer. Thirdly, to tackle the model drift caused by Non-IID data (including feature distribution shift and label distribution shift), a novel aggregation strategy based on parameter decomposition and proximal regularization is proposed. The model parameters are decoupled into a feature extractor and a classifier. During the federated aggregation phase, only the parameters of the generic feature extractor are uploaded and averaged globally, while the personalized classifier parameters are kept local to adapt to the specific class distribution of each station. Furthermore, a proximal regularization term is added to the local loss function (Eq. 20). This constraint limits the deviation of local updates from the global model, ensuring that the optimization direction does not diverge significantly due to local data heterogeneity, thereby improving the stability and convergence speed of the global model. Results and Discussions Extensive simulation experiments were conducted using core datasets (3 stations, 5 radars) and extended datasets (9 stations, 12 radars) containing complex modulations like jitter, sliding, and staggering. Quantitative performance analysis shows that the proposed method achieves sorting performance comparable to Centralized Learning (CL). In the core dataset, the Precision, Recall, and F1-Score of the proposed method reached 96.51%, 96.35%, and 96.42%, respectively, outperforming FedAvg by approximately 0.67% in F1-Score. In the more challenging extended dataset, the performance advantage is more significant, with an F1-Score improvement of 3.86% over FedAvg (Table 4). This indicates that the parameter decomposition strategy effectively balances common feature learning and personalized decision-making. Specific class analysis reveals that for difficult-to-distinguish categories (e.g., Radar 7 and Radar 10), the proposed method improves recognition accuracy by up to 15% and 6% compared to FedAvg (Fig. 7 and Fig. 8). Robustness tests demonstrate the method's adaptability. Experiments varying participating stations from 3 to 9 (Fig. 9) show a steady F1-Score increase from 73.53% to 83.75%, confirming that enlarging node scale in the federated learning framework yields collaboration gains in the form of more diverse samples and reduced geographic statistical heterogeneity, which substantially enhance the model's generalization and robustness. Under severe class skew conditions, the method maintains an F1-Score above 80% on the core set (Fig. 10 and Fig. 11). Furthermore, in extreme electromagnetic environments characterized by high pulse loss rates (70%) and spurious pulse rates (70%), the model maintains a sorting performance of over 75%, demonstrating superior robustness against noise and interference (Fig. 12). Conclusions This paper proposes a FL-based framework for multi-station collaborative radar signal sorting, addressing data privacy and transmission constraints in distributed reconnaissance. By integrating a lightweight LSTM with a heterogeneity-aware aggregation mechanism, the method effectively captures temporal pulse features while mitigating model drift caused by Non-IID data. Experimental results verify that the approach achieves accuracy comparable to centralized methods and exhibits superior robustness against label skews and severe data degradation (high pulse loss and spurious rates). This study provides a privacy-preserving and efficient solution for intelligent signal processing in distributed electronic warfare systems.
- Radar signal sorting /
- Multi-station collaboration /
- Federated learning

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图 1 多站协同侦察示意图

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图 2 本地时序模型

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图 3 TOA差分处理示意图

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图 4 CF参数示意图

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图 5 PW参数示意图

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图 6 t-SNE数据降维示意图

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图 7 核心集各类别性能示意图

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图 8 扩展集各类别性能示意图

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图 9 不同参数规模下扩展集性能示意图

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图 10 核心集在不同偏移下的性能示意图

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图 11 扩展集在不同偏移下的性能示意图

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图 12 核心集(a)与扩展集(b)在不同场景下的性能示意图

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表 1 接收站与辐射源坐标及仿真数据参数表

站编号	X/km	Y/km	Z/km	辐射源编号	X/km	Y/km	Z/km	PRI/$ \mu \text{s} $	CF/GHz	PW/$ \mu \text{s} $
S0	20	0	0	Radar 0	100	120	120	70 抖动	8.0～9.4 组变	15 滑变
S1	0	–100	0	Radar 1	80	210	210	125/162/100参差	7.8～8.5 捷变	22抖动
S2	0	10	0	Radar 2	130	146	146	85 滑变	7.6～8.7 组变	20 固定
S3	–40	80	0	Radar 3	150	30	30	40 固定	8.2～9.2 捷变	18抖动
S4	60	–60	20	Radar 4	100	102	102	100/130/80 参差	8.4～9.5 组变	23滑变
S5	–70	–30	–15	Radar 5	110	90	90	85抖动	8.6-9.6组变	22抖动
S6	30	140	10	Radar 6	95	160	160	140/95/180参差	7.5-8.3捷变	25固定
S7	–10	200	30	Radar 7	140	100	100	70滑变	8.1-9.0组变	18滑变
S8	80	–120	–25	Radar 8	120	60	60	44 固定	9.0-9.8捷变	16抖动
				Radar 9	105	130	130	90/120/150参差	7.9-8.8组变	29滑变
				Radar 10	115	110	110	95抖动	8.3-9.1捷变	24固定
				Radar 11	125	140	140	130滑变	8.5-9.4组变	28固定

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表 2 类别偏移档位设置

档位	偏移值	偏移强度描述
高	0.1～02	每站集中少数类别，分布最不均匀
中	0.4～0.6	部分类别占优，仍明显不均匀
低	0.9～1.0	接近均匀，作为近IID对照

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表 3 站间参数误差设置

编号	TOA/ns	CF/MHz	编号	TOA/ns	CF/MHz
S0	5.0	1.0	S0	2.0	1.0
S1	10.0	0.5	S1	12.0	0.5
S2	50.0	5.0	S2	17.0	5.0
			S3	23.0	2.0
			S4	28.0	3.0
			S5	10.0	0.8
			S6	5.0	0.6
			S7	3.0	0.4
			S8	50.0	4.0

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表 4 分选性能指标对比

数据集	模型对照	精确率/%	召回率/%	F1-Score/%
核心集	CL	97.39	97.21	97.29
	FedAvg	95.60	95.94	95.75
	本文方法	96.51	96.35	96.42
	相对CL	-0.88	-0.86	-0.87
	相对FedAvg	+0.91	+0.41	+0.67
扩展集	CL	90.92	90.98	90.95
	FedAvg	81.24	80.38	79.89
	本文方法	83.69	83.81	83.75
	相对CL	-7.23	-7.17	-7.20
	相对FedAvg	+2.45	+3.43	+3.86

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