Complex Electromagnetic Environment Research Articles

Detection of phase modulation disorder when processing a sequence of ultra-wideband quasi-radio signals against a background of white Gaussian noise

In conditions of a complex electromagnetic environment in communication and data transmission systems, a change in the type of modulation occurs when one or more signal parameters change abruptly, therefore the task of determining the type of signal modulation can be reduced to the task of detecting a disorder, that is, detecting the fact of a change in the type of modulation of the signal and estimation of the disorder time. The purpose of the work is to synthesize an algorithm for detecting phase modulation disorder when processing a sequence of ultra-wideband quasi-radio signals with unknown amplitude and initial phase against a background of white Gaussian noise and to analyze the effectiveness of the resulting algorithm based on the results of statistical modeling. The work synthesizes an algorithm for detecting a phase modulation disorder of a received sequence of ultra-wideband quasi-radio signals against a background of white Gaussian noise. To analyze the effectiveness of the resulting algorithm, statistical modeling of algorithm for detecting a phase modulation disorder is carried out. The dependences of the probability of detection of a disorder on the signal-to-noise ratio and the length of the sequence of received signals at a fixed moment of disorder are obtained. A comparison of the detection characteristics of phase modulation disorder of ultra-wideband quasi-radio signals and narrowband radio signals is made. It is shown that when a low-order phase modulation disorder is detected, the required values of the probability of detection are achieved at low signal-to-noise ratios, while with an increase in the modulation order and the use of its modifications, the efficiency indicators of the algorithm will deteriorate. The introduction of algorithms for detecting a disorder in radio engineering applications will allow optimizing synchronization algorithms and adaptation to interference conditions with a concomitant change in the type of modulation, as well as reducing the amount of transmitted service information in telecommunication systems.

Ultraviolet collaborative networking method based on a novel neighbor discovery algorithm

Ultraviolet (UV) optical communication presents several advantages, such as non-line-of-sight propagation, high confidentiality, reliability, and the absence of precise alignment requirements. UV optical communication networks find application in various scenarios in complex electromagnetic environments. In this study, we propose a UV collaborative networking method based on a neighbor discovery algorithm (UVCN–NDA), which establishes neighbor tables within nodes and selects the shortest transmission path between the nodes for frame transmission. During data transmission, nodes dynamically adjust the transmission time of each bit to enhance throughput and use the distance information in the neighbor table for path selection. This strategy prevents ineffective data forwarding and subsequently reduces network power consumption. Simulation results demonstrate that the neighbor discovery algorithm implemented in this protocol exhibits higher efficiency in neighbor discovery and lower message load than existing algorithms. Furthermore, when compared to UV networks based on bit–simultaneous–transmission, the UVCN–NDA–based UV networks achieve an approximate 86.2% reduction in average power consumption and approximately 52% increase in throughput, thereby providing a considerable performance advantage. Finally, the conducted network experiments prove the shortest path selection capability of UVCN–NDA.

Finite element modeling of the magnetic property behaviors of electrical steels in rotating fields considering their crystallographic textures

In magnetic device modeling, the crystallographic textures and microstructures of electrical steels critically influence their magnetic properties. Yet, these aspects are often neglected or inadequately represented in finite element (FE) models, leading to inaccuracies in predicting electrical steel performance, especially in complex electromagnetic environments. This paper presents a multi-scale modeling approach, integrating a macroscopic FE model of a magnetic device with a microstructural scalar permeability model that accounts for the crystallographic textures of electrical steels. This approach demonstrates proficiency in predicting nonlinear anisotropic permeability and capturing magnetic anisotropy of electrical steels within a rotational tester, especially when anisotropy is predominantly governed by crystallographic texture and the angles between the B and H vectors are minimal. Furthermore, the model effectively captures the magnetic anisotropy in complex magnetic fields and geometric configurations of a permanent magnet motor. A comparative analysis of this model is conducted against three other material models: an isotropic model, a two-axis model, and a general-vector model. These models are based on different approaches, ranging from a single non-linear BH curve to comprehensive vector BH curves covering all directions. Among them, the general-vector model, reliant on extensive 2D vector BH measurements, is the most accurate but requires substantial experimental data.

RCS-reduced patch antenna with wide frequency tuning range based on absorbing metasurface

A frequency-tunable patch antenna with reduced radar cross section (RCS) based on absorbing metamaterial is proposed. By controlling the external voltage of the varactor diodes, the working frequency of the patch can be adjusted. Through an investigation of the composite structure of the patch and absorbing metamaterial, RCS reduction is achieved over the entire wide tunable frequency range of the antenna. Measured results illustrate that the proposed patch antenna can dynamically operate in the range from 2.28 to 3.6 GHz with a gain variation of 7.1–8.7 dBi. Meanwhile, the RCS can be reduced by at least 7 dB and up to 20 dB in a fractional bandwidth of 44.9% of the antenna under a normal incidence. In comparison to conventional RCS-reduced antennas, the proposed one features a broader frequency tuning range and has potential value in wideband radar systems under complex electromagnetic interference environments.

Chaff Cloud Integrated Communication and TT&C: An Integrated Solution for Single-Station Emergency Communications and TT&C in a Denied Environment

In response to potential denial environments such as canyons, gullies, islands, and cities where users are located, traditional Telemetry, Tracking, and Command (TT&C) systems can still maintain core requirements such as availability, reliability, and sustainability in the face of complex electromagnetic environments and non-line-of-sight environments that may cause service degradation or even failure. This paper presents a single-station emergency solution that integrates communication and TT&C (IC&T) functions based on radar chaff cloud technology. Firstly, a suitable selection of frequency bands and modulation methods is provided for the emergency IC&T system to ensure compatibility with existing communication and TT&C systems while catering to the future needs of IC&T. Subsequently, theoretical analyses are conducted on the communication link transmission loss, data transmission, code tracking accuracy, and anti-multipath model of the emergency IC&T system based on the chosen frequency band and modulation mode. This paper proposes a dual-way asynchronous precision ranging and time synchronization (DWAPR&TS) system employing dual one-way ranging (DOWR) measurement, a dual-way asynchronous incoherent Doppler velocity measurement (DWAIDVM) system, and a single baseline angle measurement system. Next, we analyze the physical characteristics of the radar chaff and establish a dynamic model of spherical chaff cloud clusters based on free diffusion. Additionally, we provide the optimal strategy for deploying chaff cloud. Finally, the emergency IC&T application based on the radar chaff cloud relay is simulated, and the results show that for severe interference, taking drones as an example, under a measurement baseline of 100 km, the emergency IC&T solution proposed in this paper can achieve an accuracy range of approximately 100 m, a velocity accuracy of 0.1 m/s, and an angle accuracy of 0.1°. In comparison with existing TT&C system solutions, the proposed system possesses unique and potential advantages that the others do not have. It can serve as an emergency IC&T reference solution in denial environments, offering significant value for both civilian and military applications.

RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating

Accurately classifying targets and clutter plots is crucial in radar data processing. It is beneficial for filtering out a large amount of clutters and improving the track initiation speed and tracking accuracy of real targets. However, in practical applications, this problem becomes difficult due to complex electromagnetic environments such as cloud and rain clutter, sea clutter, and strong ground clutter. This has led to poor performance of some commonly used radar plot classification algorithms. In order to solve this problem and further improve classification accuracy, the radar plot classification algorithm based on evidence adaptive updating (RPC-EAU) is proposed in this paper. Firstly, the multi-dimensional recognition features of radar plots used for classification are established. Secondly, the construction and combination of mass functions based on feature sample distribution are designed. Then, a confidence network classifier containing an uncertain class was designed, and an iterative update strategy for it was provided. Finally, several experiments based on synthetic and real radar plots were presented. The results show that RPC-EAU can effectively improve the radar plot classification performance, achieving a classification accuracy of about 0.96 and a clutter removal rate of 0.95. Compared with some traditional radar pattern recognition algorithms, it can improve by 1 to 10 percentage points. The target loss rate of RPC-EAU is also the lowest, only about 0.02, which is about one third to one half of the comparison algorithms. In addition, RPC-EAU avoids clustering all radar points in each update, greatly saving the computational time. The proposed algorithm has the characteristics of high classification accuracy, low target loss rate, and less computational time. Therefore, it is suitable for radar data processing with high timeliness requirements and multiple radar plots.

Fabrication of bamboo-inspired continuous carbon fiber-reinforced SiC composites via dual-material thermally assisted extrusion-based 3D printing

Ceramic matrix composites (CMCs) structural components encounter the dual challenges of severe mechanical conditions and complex electromagnetic environments due to the increasing demand for stealth technology in aerospace field. To address various functional requirements, this study integrates a biomimetic strategy inspired by gradient bamboo vascular bundles with a novel dual-material 3D printing approach. Three distinct bamboo-inspired structural configurations Cf/SiC composites are designed and manufactured, and the effects of these different structural configurations on the CVI process are analyzed. Nanoindentation method is utilized to characterize the relationship between interface bonding strength and mechanical properties. The results reveal that the maximum flexural strength and fracture toughness reach 108.6 ± 5.2 MPa and 16.45±1.52 MPa m1/2, respectively, attributed to the enhanced crack propagation resistance and path caused by the weak fiber-matrix interface. Furthermore, the bio-inspired configuration enhances the dielectric loss and conductivity loss, exhibiting a minimum reflection loss of −24.3 dB with the effective absorption band of 3.89 GHz. This work introduces an innovative biomimetic strategy and 3D printing method for continuous fiber-reinforced ceramic composites, expanding the application of 3D printing technology in the field of CMCs.

Relative positioning method for unmanned aerial vehicles in complex electromagnetic environment

In view of the problem that satellite navigation and positioning systems may fail or be unreliable in complex electromagnetic environments, which may lead to divergence of UAV positioning, the inertial navigation systems working for a long time may lead to increasing errors. This paper adopts a relative navigation algorithm that combines airborne inertial navigation systems and inter-aircraft relative information measurement. The algorithm requires each UAV to load relative navigation equipment and inertial navigation systems and acquire and fuse relative navigation information through inter-aircraft communication. In this relative navigation algorithm, the host and wing plane can transmit their respective navigation information and relative navigation information to each other, and fuse information from different sources through a Kalman filtering algorithm to enhance the accuracy of relative positioning data. With the help of corrected and feedback relative positioning data, the relative location of the host and wingman is achieved. The actual simulation results show that the relative position RMSE of the host, Wing Plane 1, and Wing Plane 2 in the X, Y, and Z dimensions are 1.345 m, 0.7388 m, 1.645 m, 2.167 m, 1.201 m, and 2.069 m, respectively. It is apparent that this scheme improves the relative navigation performance and robustness of the UAV group.

Evaluation methods of BDS-3 ground-segment defensive capability oriented to satellite navigation countermeasures

Owing to intentional interference and destructions during satellite navigation countermeasures, the security application of global navigation satellite systems (GNSS) faces immense challenges. To ensure the safe application of the GNSS in complex electromagnetic environment, the system-level defensive capability need be assessed, which can make for maintaining GNSS normal service during countering and further realizing the optimal decision. Therefore, this study used the ground control segment in the GNSS as the evaluation object, the bottom indices related to the criterion layers including service performance and station function as the key evaluation indices, and the technical requirement of each index under a secure situation as the standard, to propose the evaluation system including index system and evaluation methods for the ground-segment defensive capability. As BeiDou navigation satellite system III (BDS-3) is a global navigation satellite system independently built and operated by China, it was taken as a case for method verification. Then, BDS-3 two-way observation data between the satellite and the ground and between stations, which was collected from Beijing flight Control Center, from March 5 to 12 (2022) were selected during tests, and the results were compared with the existing standards contained in BDS open service performance standard to evaluate the system-level defensive capability. Evaluation Results of positioning accuracy and error correction ability for the BDS-3's ground stations determined through seven damage experiments with different interference strength show that: Corresponding horizontal and vertical positioning accuracy of stations under test did not exceed 1 m in the statistical period and their convergence time did not exceed the limit (30 min); thus, the ground segment of the BDS-3 has a good defensive capability. Compared to comprehensive evaluation results of the BDS-3, based on the theoretical threshold (the ground-segment threshold ≤48.40 % and the space-segment threshold ≤51.60 %), we know that its ground-segment defense (14.06 %) was worse than GPS ground-segment defense (14.43 %), whereas the ground-segment defense of the BDS/GPS integrated system (15.29 %) was best and its capability evaluation grade was strong. They indicate system's future improvements should focus more on enhancing the development scale and service efficiency of the BDS-3's ground stations in the Western Hemisphere, and reasonably promote system compatibility and interoperability while considering facility cost and operating efficiency.

Simulation study of electromagnetic protection of ignition chip

The purpose of this paper is to study the adaptability and enhancement methods of ignition chips in complex electromagnetic environments. In this paper, combined with simulation and calculation technology, we first establish the electromagnetic–electrical–thermal coupling simulation model of ignition chips and analyze the influence of two electromagnetic environments, continuous electromagnetic wave and nuclear electromagnetic pulse, on the electrical–thermal coupling in the bridge area of the ignition chip. The results show that a continuous electromagnetic wave field strength of 1200 V/m, irradiated for 5 s, a nuclear electromagnetic pulse with a rise time of 5 ns, a half-height width of 24 ns, and a leg wire length of 350 mm will cause the ignition chip to fire accidently. In order to suppress its accidental firing, the electromagnetic protection is enhanced by using parallel Transient Voltage Suppressor (TVS) diodes. It has been shown that parallel TVS diodes provide good protection against both continuous electromagnetic waves and nuclear electromagnetic pulses. In particular, the peak induced current decreases by more than 64.7% during the action of continuous electromagnetic waves, and the maximum temperature in the bridge area decreases by 54.34% during the action of nuclear electromagnetic pulses.

Demand and key technology for a LEO constellation as augmentation of satellite navigation systems

A Low Earth Orbit (LEO) constellation augmenting satellite navigation is important in the future development of Global Navigation Satellite System (GNSS). GNSS augmented by LEO constellations can improve not only the accuracy of Positioning, Navigation, and Timing (PNT), but also the consistency and reliability of secure PNT system. This paper mainly analyzes the diverse demands of different PNT users for LEO augmented GNSS, including the precision demand in real-time, the availability demand in special areas, the navigation signal enhancement demand in complex electromagnetic environments, and the integrity demand with high security. Correspondingly, the possible contributions of LEO constellations to PNT performance are analyzed from multiple aspects. A particular attention is paid to the special PNT user requirements that cannot be fulfilled with existing GNSS, such as the PNT service demand in the polar regions and the onboard GNSS orbit determination demand of some LEO satellites. The key technologies to be considered in the constellation design, function realization, and payload development of the LEO-augmented navigation system are summarized.

Antenna modeling based on meta-heuristic intelligent algorithms and neural networks

As wireless communication technology continues to advance, the antenna, as an essential front-end device in radio communication system, is surrounded by more and more complex electromagnetic wave environments with increasing variety, resulting in greater demand for antennas and higher design requirements. While the traditional antenna design methods suffered from the disadvantage of low design efficiency, a powerful tool for accelerating antenna design is the modelling of antennas with neural networks. Aiming to enhance the modeling accuracy of neural network, multiple novel meta-heuristic swarm intelligent algorithms are introduced and part of them are modified for the purpose of applying to optimizing network’s weights and biases so as to raise the antenna model`s prediction precision on the basis of neural network. Specifically, the intelligent algorithms and their improvement directions include the strategy of optimizing weights and biases for neural networks with seagull optimization algorithm, optimizing the weights and biases of neural network with the improved butterfly algorithm fused with reverse learning, and the artificial rabbit algorithm optimizing the neural network weights and biases. In addition, two intelligent optimization algorithms that are already more mature: the particle swarm algorithm and the genetic algorithm are added to compare with the above three algorithms. The accuracy of neural network prediction before and after the optimisation of neural network by seagull algorithm, the butterfly algorithm incorporating reverse learning, the artificial rabbit algorithm, the particle swarm algorithm, and the genetic algorithm are got through the results respectively. The results of the experiments displayed that the neural network optimized of the improved butterfly algorithm incorporating reverse learning has a prediction accuracy of 99.69 % with stable results, the optimised neural network prediction accuracy of the seagull algorithm reaches 99.51%, and the optimised neural network prediction accuracy of the artificial rabbit algorithm is 99.49 %. The remaining two traditional algorithms optimized neural network accuracy is 83.1 % and 99.43 % respectively. Therefore, the improved butterfly algorithm incorporating reverse learning is the most effective among these three new algorithms applied to the field of antenna prediction. Moreover, the running time of the network optimized by different algorithms is quite distinct, among which the neural network optimized by the improved butterfly algorithm incorporating reverse learning takes the shortest time, which increases the prediction efficiency of the network by more than 70%. In summary, the application of the fused reverse learning improved butterfly algorithm in optimizing neural network predictions yields the shortest processing time and highest accuracy. This not only enables faster and more precise antenna design but also holds greater significance for the field of antenna design and analysis.

A Study on Anti-Jamming Algorithms in Low-Earth-Orbit Satellite Signal-of-Opportunity Positioning Systems for Unmanned Aerial Vehicles

Low-Earth-Orbit (LEO) satellite Signal-of-Opportunity (SOP) positioning technology has gradually matured to meet the accuracy requirements for unmanned aerial vehicle (UAV) positioning in daily scenarios. Advancements in miniaturization technology for positioning terminals have also made this technology’s application to UAV positioning crucial for UAV development. However, in the increasingly complex electromagnetic environment, there remains a significant risk of degradation in positioning performance for UAVs in LEO satellite SOP positioning due to unintentional or malicious jamming. Furthermore, there is a lack of in-depth research from scholars both domestically and internationally on the anti-jamming capabilities of LEO satellite SOP positioning technology. Due to significant differences in the downlink signal characteristics between LEO satellites and Global Navigation Satellite System (GNSS) signals based on Medium Earth Orbit (MEO) or Geostationary Earth Orbit (GEO) satellites, the anti-jamming research results of traditional satellite navigation systems cannot be directly applied. This study addresses the narrow bandwidth and high signal-to-noise ratio (SNR) characteristics of signals from LEO satellite constellations. We propose a Consecutive Iteration based on Signal Cancellation (SCCI) algorithm, which significantly reduces errors during the model fitting process. Additionally, an adaptive variable convergence factor was designed to simultaneously balance convergence speed and steady-state error during the iteration process. Compared to traditional algorithms, simulation and experimental results demonstrated that the proposed algorithm enhances the effectiveness of jamming threshold settings under narrow bandwidth and high-power conditions. In the context of LEO satellite jamming scenarios, it improves the frequency-domain anti-jamming performance significantly and holds high application value for drone positioning.

Analysis of Coverage and Capacity for UAV-Aided Networks with Directional mmWave Communications

Millimeter wave (mmWave) unmanned aerial vehicle (UAV)-aided networks have enormous application potential due to their large bandwidth and ultra-high speed, being regarded as an effective technology for improving the reliability of military and civilian fields. However, due to their complex electromagnetic spectrum environment and the sensitivity of mmWaves to blocking effects, its performance analysis faces certain difficulties. This article investigates the coverage and network capacity of mmWave UAV-aided networks under significant blocking effects and complex electromagnetic environments; for this purpose, we equipped each UAV with mmWave antennas featuring adjustable beamwidth and direction. A Matérn hard-core point process (MHCPP) with repulsion constraints was also employed to reflect the minimum distance constraints to isolate the mutual interference between UAVs. Then, using a stochastic geometric analysis, we derived the coverage and capacity characteristics and further obtained a closed-form expression for the network coverage probability. Finally, the simulation results showed that the network throughput could reach 86% when the density of UAVs was half of that of ground base stations (GBSs) in the city center, validating the efficiency and accuracy of our theoretical derivations.

Instrumentation for Sub-Ampere Lightning Current Measurement on a Tall Meteorological Tower in Complex Electromagnetic Environment

Measurement of lightning current plays a critical role in the field of atmospheric electricity. Traditionally, Rogowski coils or low-resistance shunts were employed for measuring lightning currents in the range from several amperes up to several hundreds of kilo-amperes, and high-value resistors were utilized for measuring corona discharge currents at sub-ampere levels. However, these approaches were not suitable for continuously recording the vast range of lightning currents. For this sake, we have developed a lightning current measurement system equipped with a shock-tolerant low-noise amplifier module. With the system installed on a tall tower, sub-ampere level currents just before the lightning initiation were observed for the first time. To confirm the authenticity of the recorded currents, the background noise of the measurement system and surrounding environment were identified, and a digital multi-frequency notch filter was proposed for de-noising. Results show that the system can achieve a current identification level of 50 mA even in complex electromagnetic environments, while having a measurement capability of 220 kA.

Enhanced Clustering MAC Protocol Based on Learning Automata for UV Networks

Ultraviolet (UV) networks are widely applied in complex electromagnetic environments. Designing an efficient multi-node medium access control (MAC) protocol for these networks is important. In this study, we proposed an enhanced clustering time division multiple access (TDMA) MAC protocol based on clustering and learning automata (LA). Subsequently, the effects of the network topology, class of service, and number of cluster nodes on the network performance under the proposed protocol were analyzed. Then, the protocol was compared with the TDMA protocol and clustering system. Results revealed that it obtained a better network performance, proving its suitability for multi-node UV networking.

Electromagnetic deformation of a high-precision radio navigation field consumers of the basins of the inland waterways of Russia

The basins of the inland waterways of the Russian Federation are characterized by a variety of specific navigation features, the destructive effects of which significantly affect the parameters of the purpose of radio navigation systems. Therefore, the task arises to develop a model and estimate the electromagnetic deformation of the high-precision radio navigation field in the basins of the inland waterways of Russia caused by their navigational features. The study and identification of the general navigational features of the basins of the inland waterways of Russia and the assessment of the degree of influence of their destabilizing factors on the quality indicators of the high-precision radio navigation field. New navigation features have been identified in the basins of the inland waterways of Russia, the destructive factors of which deform the high-precision radio navigation field and reduce the quality of transmission of differential corrections. The results obtained confirm a decrease in the accuracy of positioning unmanned vessels in sections of the waterway with a complex terrain topology and electromagnetic environment. At the same time, the positioning accuracy in river basins may deteriorate in local areas from 1.6 times (from 6.8 m to 28.7 m) to 4.3 times (from 27.3 m to 117 m), depending on the electro-magnetic situation and the value of the geometry coefficient. A decrease in the quality of differential correction transmission will be manifested at the boundaries of service areas and range from 1 dB in sparsely populated areas and up to 30 dB in industrial zones. These factors will reduce the safety of navigation, so they must be taken into account when operating ships and designing functional additions to the global nav. The results obtained in the work can be used in the formation and optimization of the topology of control and correction stations of differential additions to the global navigation satellite system and to meet the requirements for the accuracy of positioning of mobile objects in difficult physical and geographical conditions of the terrain and electromagnetic environment.

Recognition of pulse-in-pulse modulation type of radar signal based on feature extraction

In order to solve the problem of identifying radar signal modulation types in a complex electromagnetic environment, this paper analyzes and evaluates some of the existing identification methods, such as CW, LFM, NLFM, BPSK, QPSK, FSK, FSK + PSK, and LFM + BPSK modulation types, and proposes a hierarchical decision recognition algorithm based on feature extraction. First, the signal is preprocessed by FFT transformation and STFT transformation. Second, a new set of radar signal recognition characteristic parameters is proposed to identify and classify different signals. Then, by analyzing the characteristics of different signals, the signals are identified and classified one by one. Finally, the usability of the proposed algorithm is obtained by comparing several commonly used recognition algorithms. The simulation results show that the recognition success rate of this algorithm can reach 95% when the SNR is better than −7 dB, which has certain industrial application value.

A broadband multi-frequency microwave combined biological exposure setup.

With the rapid popularization of wireless electronic devices, there has been an increasing concern about the impacts of the electromagnetic environment on health. However, most research reports on the biological effects of microwaves have focused on a single frequency point. In reality, people are exposed to complex electromagnetic environments that consist of multiple frequency microwave signals in their daily lives. It is important to investigate whether multi-frequency combined microwave energies have different biological effects compared with single frequency microwave energy. Unfortunately, there are limited reports on this topic due to the lack of suitable platforms for research on multi-frequency microwave energy combined with biological exposure. To address this issue, this study presents a setup that has a very wide working frequency bandwidth and can be compatible with single frequency and multi-frequency microwave combined exposure. Moreover, it can achieve relatively equal exposure to multiple biological samples at any frequency point in the working frequency range, which is crucial for electromagnetic biology research. The experimental results are in good agreement with the simulation results, confirming its capability to facilitate the study of complex electromagnetic environment effects on organisms.

Dual‐labelled multi‐Bernoulli filter based on specific emitter identification

AbstractIn complex electromagnetic environments, airborne passive bistatic radar encounters the challenge of associating emitters with measurements for multi‐target tracking. The authors propose a solution based on specific emitter identification technology. Firstly, generative adversarial networks (GANs) are utilised to extract and classify emitter signals using radio frequency fingerprint (RFF) features. The classification results are then used to construct a set of emitter labels for pre‐processing the measurement data. Subsequently, the pre‐processed measurement data set is input into the labelled multi‐Bernoulli filter framework, which is extended to a dual‐labelled (target label and emitter label) multi‐Bernoulli filter. This filter jointly predicts and updates the multi‐target posterior density, enabling the estimation of multi‐target trajectories. The effectiveness of the proposed algorithm is validated using two experiments. The results demonstrate that the GAN based on RFF features effectively identifies emitter signals. Moreover, the dual‐labelled multi‐Bernoulli filter, based on specific emitter identification, accurately estimates multi‐target trajectories using measurement data from an airborne passive radar of the multi‐transmit single‐receive type. This approach provides a novel and effective solution to the multi‐target tracking problem in complex electromagnetic environments.