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Radioengineering

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Proceedings of Czech and Slovak Technical Universities

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June 2026, Volume 35, Number 2 [DOI: 10.13164/re.2026-2]

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R. Zhou, P. Lai, L. Chen, Z. Hu, L. Qian [references] [full-text] [DOI: 10.13164/re.2026.0195] [Download Citations]
Mid-Air Hand Rehabilitation Evaluation and Virtual Training System

This paper presents the design of a mid-air hand rehabilitation evaluation and virtual training system based on Leap Motion and gesture recognition algorithms. The system aims to provide a scientifically-grounded and accessible home-based rehabilitation solution for patients with hand injuries. It employs a spatio-temporal attention-enhanced multi-scale residual graph convolutional network algorithm for gesture recognition. Following this, specific joint angles are calculated and 14 representative gesture scores are derived. Weights are assigned to each scoring item via ridge regression to achieve a quantitative assessment. The rehabilitation training module comprises two modes: interactive turn-based games and music rhythm games, designed to train hand movement and dexterity. The system was subsequently tested to evaluate the performance of both the assessment function and the two training games. Results show an average gesture recognition accuracy of 94.86%. Furthermore, the reliability scores for both training games exceeded 90%. These findings demonstrate that the system achieves good accuracy in gesture recognition and effective assessment of hand rehabilitation progress.

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Keywords: Gesture recognition, hand rehabilitation, multi-scale residual graph convolution, attention mechanism, human-computer interaction

Y. D. Wang, X. Fan, M. M. Lu [references] [full-text] [DOI: 10.13164/re.2026.0208] [Download Citations]
Study on the Influence of Channel’s Non-Ideal Characteristic on Pseudo-Code Measurement Bias for GNSS Receiver

GNSS has been widely used in civilian and military areas due to its high precision. However, the channel’s non-ideal characteristic may introduce pseudo-code measurement bias to degrade positioning precision. There are few theoretical analyses for interpreting why the channel’s non-ideal characteristic may introduce pseudo-code measurement bias and what kind of channels will not introduce pseudo-code measurement bias. We have conducted a systematical study on the above issues. Firstly, we established an analysis model for interpreting the channel’ non-ideal characteristic’s influence on pseudo-code measurement bias. Based on the analysis model, we proposed four sufficient conditions for unbiased pseudo-code measurement. Secondly, we applied the analysis model to typical channels, i.e. sine type group delay channel, frequency-domain anti-jamming channel and space-domain anti-jamming channel, to evaluate the channel’s influence on pseudo-code measurement bias. Finally, we conducted simulations to evaluate the theoretical analysis’ correctness. Results show that these typical channels do not introduce pseudo-code measurement bias, which are consistent with theoretical analysis. As a result, the correctness of the proposed four sufficient conditions is verified so that the proposed four sufficient conditions can be used to guide the design of unbiased pseudo-code measurement channel.

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Keywords: Channel characteristic, pseudo-code measurement bias, GNSS receiver, non-ideal

L. Kirasamuthranon, J. Koseeyaporn, P. Wardkein [references] [full-text] [DOI: 10.13164/re.2026.0218] [Download Citations]
DSSZ-SM: A Simplified Chirp Coding Scheme with Inherent Clock Synchronization

This study proposes a novel double-slope chirp symbol, termed double–slope start zero stop minimum (DSSZ–SM), for efficient data communication. Unlike conventional chirp coding, which often involves complex generation and synchronization, the DSSZ–SM provides a simpler structure with inherent clock synchronization using a PWM-based generator. System performance is evaluated through analysis and simulations over additive white Gaussian noise (AWGN) and Rayleigh fading channels. Two asynchronous decoding methods, with and without an integrator, are compared. Results show that the non-integrator approach achieves lower error rates under both channel conditions. The proposed DSSZ–SM offers a simplified and robust alternative for efficient data communication.

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Keywords: Double-slope chirp, chirp encoding, asynchronous decoding, data communication

X. Yao Z. Xu, G. Liu [references] [full-text] [DOI: 10.13164/re.2026.0232] [Download Citations]
UWB Indoor Localization Based on XGBoost NLOS Identification and DS-TWR Ranging

Indoor environments present significant challenges for ultra-wideband (UWB) localization due to ranging errors and non-line-of-sight (NLOS) propagation. This paper proposes a robust UWB indoor localization framework that integrates double-sided two-way ranging (DS-TWR), XGBoost-based NLOS identification, residual-weighted localization, and Kalman filter (KF). The main contribution of this work is the unified use of NLOS identification in both ranging correction and localization fusion, significantly improving localization accuracy in complex environments. Experimental results demonstrate improvements in ranging accuracy of up to 53.7% and 47.22% under human-body and wooden-board occlusions. In dynamic experiments, the proposed method outperforms conventional UWB localization, KF, and weighted least squares methods with positioning accuracy improvements of 38.64%, 28.95%, and 12.9%, respectively. These results confirm the framework’s effectiveness in mitigating NLOS impact and enhancing UWB localization robustness.

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Keywords: Ultra-Wide Band (UWB), indoor localization, Non-Line-of-Sight (NLOS) identification, Double-Sided Two-Way Ranging (DS-TWR), XGBoost algorithm

W. Yu, W. Su, H. Gu [references] [full-text] [DOI: 10.13164/re.2026.0247] [Download Citations]
Non-searching High Speed Target Detection Method for Carrier Frequency and Pulse Repetition Frequency Agile Radar

Range migration has become the major problem faced by radar high speed target detection. The existing methods primarily focus on range migration elimination and coherent integration in the traditional radar system with fixed carrier frequency (CF) and pulse repetition frequency (PRF), whereas they are not applicable to the radar system with joint agility of CF and PRF, which possesses robust low-probability-of-intercept and anti-jamming performance under complex electromagnetic circumstances. In this paper, the high speed target detection problem for the CF-PRF agile radar system is considered and a non-searching coherent integration algorithm is proposed, where range frequency reversal and correlation transform (RFRCT), azimuth nonuniform fast Fourier transform (NUFFT) and range inverse fast Fourier transform (IFFT) are combined to eliminate the effect of range migration and radar parameter agility upon coherent integration. Experimental results have been provided to validate the effectiveness of the proposed method.

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Keywords: High speed target, Carrier Frequency (CF) and Pulse Repetition Frequency (PRF) agile radar, range migration, coherent integration, Target detection

J. Dong, Y. Liang, J. Wang, Z. Ma [references] [full-text] [DOI: 10.13164/re.2026.0256] [Download Citations]
Mixed Signal Recognition Network Based on FD-MCNN and BiLSTM

Mixed-signal recognition in realistic wireless environments is challenging because weak signal components are often masked by stronger ones. To address this issue, this paper proposes a hierarchical recognition framework that combines multi-domain feature disentanglement with temporal dependency modeling. Specifically, the proposed network extracts complementary features from the time, frequency, modulation, and energy domains, enabling more robust representation of mixed signals under complex interference conditions. Based on these features, the framework first identifies the dominant signal component, then enhances the weak component to reduce the masking effect, and finally employs a bidirectional long short-term memory network (BiLSTM) network for temporal modeling and classification. Experiments with signal-to-noise ratio (SNR) ranging from 0 to 30 dB show that the proposed method can effectively recognize both strong and weak signal components while improving overall robustness. These results demonstrate the effectiveness of the proposed framework for mixed-signal recognition in interference-rich wireless environments.

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Keywords: Mixed signal recognition, hierarchical recognition framework, deep learning, feature disentanglement

H. Song, Y. He, L. He, X. Cao, Y. Liu, J. Chen [references] [full-text] [DOI: 10.13164/re.2026.0272] [Download Citations]
A Spectrum Prediction Framework Based on Graph Convolutional Recurrent Neural Network Integrated with Variational Mode Decomposition

Wireless spectrum prediction is crucial for dynamic spectrum management and congestion mitigation. However, existing methods often emphasize spatio-temporal feature modeling while neglecting the frequency-domain structure of the spectrum. Moreover, spectrum data inherently exhibits non-stationarity, multi-scale characteristics, and strong spatio-temporal coupling, making it difficult for traditional prediction models to fully capture its complex dynamics, thereby limiting prediction accuracy. To address these issues, this paper proposes a spectrum prediction model that integrates Variational Mode Decomposition (VMD) with a graph convolutional recurrent neural network. First, VMD is employed to adaptively decompose the raw spectrum into multi-scale intrinsic modes, achieving frequency-domain decoupling and noise suppression, thus enhancing the model's sensitivity to spectral details. Second, the Spearman correlation coefficient is utilized to construct a graph topology among frequency bands, and a Graph Convolutional Network (GCN) is applied to extract spatial dependency features, combined with Gated Recurrent Units (GRU) to model temporal evolution patterns. Furthermore, an attention mechanism is introduced to dynamically weight the hidden states, focusing on critical information and improving training efficiency. Experiments on real-world spectrum datasets demonstrate the superior performance of the proposed model, achieving an accuracy of 96.79%, MAE of 0.3342, RMSE of 0.4632, and R² of 0.9969.

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Keywords: Spectrum prediction, Variational Mode Decomposition (VMD), Graph Convolutional Network (GCN), Gated Recurrent Unit (GRU), attention mechanism

S. Kumar, N. Mehra, Y. K. Jain [references] [full-text] [DOI: 10.13164/re.2026.0287] [Download Citations]
DEAR-SFCM: Dynamic Energy Aware Radius Adaptive Subtractive Fuzzy C-Means Clustering Framework for Efficient and Sustainable Wireless Sensor Networks

Managing energy efficiently in Wireless Sensor Networks (WSNs) is challenging because uneven energy consumption and early node failures degrade network performance. To address this issue, the dynamic energy-aware and radius-adaptive subtractive fuzzy C-means clustering method (DEAR-SFCM) is proposed to create more balanced clusters and improve both network stability and lifetime. The proposed method combines adaptive subtractive clustering, an energy-distance weighted measure, and entropy-regularized fuzzy membership updates to generate clusters that better reflect both the energy levels and spatial locations of sensor nodes. It incorporates an adaptive radius control mechanism that adjusts the clustering radius according to node density, thereby preventing overcrowding in dense regions and excessive cluster formation in sparse areas. In addition, optimal nodes are selected as cluster heads (CHs) using a multi-criteria CH selection strategy. Compared with existing state-of-the-art methods, DEAR-SFCM demonstrates superior performance in terms of energy balancing, network lifetime, alive-node ratio, and packet delivery rate. The results show that DEAR-SFCM reduces hotspot formation, distributes communication tasks more evenly, and extends both the stable operation period and the overall network lifetime. These improvements make DEAR-SFCM a robust and adaptable solution for energy-constrained WSNs and future large-scale Internet of Things (IoT) monitoring applications.

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Keywords: Cluster head selection, energy efficiency, fuzzy C-means, network lifetime, wireless sensor networks

R. Cheng, J. Su, M. Huang [references] [full-text] [DOI: 10.13164/re.2026.0304] [Download Citations]
TriFusion-Lite: A Temporal-Frequency-Phase Fusion Lightweight Network for Modulation Recognition

Automatic Modulation Recognition (AMR) is an essential technology for modern wireless communication systems. However, existing deep learning models are often computationally complex and frequently overlook the phase relationships within in-phase/quadrature (I/Q) signals, thereby hindering their deployment on resource-constrained devices. This paper introduces TriFusion-Lite, a lightweight multi-stream deep learning architecture designed to optimize both efficiency and accuracy in AMR. The proposed framework begins with a tailored preprocessing pipeline that compresses the input signal while enriching its representation with robust statistical features. A novel four-stream parallel network then processes the enhanced signal: a complex-valued convolutional stream to preserve phase integrity, two parallel 1D convolutional streams for independent I/Q channel analysis, and a Short-Time Fourier Transform (STFT) stream to capture spectral characteristics. A hierarchical fusion mechanism progressively integrates these multi-domain features for final classification. Comprehensive evaluations on benchmark datasets demonstrate the effectiveness and competitive performance of the proposed approach. The experiments confirm the effectiveness of the compression stage and analyze its performance across various compression levels. Furthermore, the proposed method achieves competitive results compared with state-of-the-art approaches while maintaining a favorable balance between classification performance and computational efficiency, making it a promising solution for AMR applications on edge devices. The source code of the proposed framework is publicly available at https://github.com/sansi34jun/TriFusion-Lite

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Keywords: Modulation recognition, I/Q signals, lightweight neural network, multi-stream fusion

J. Xue, B. Su, Y. Liu, L. Zhou, J. Meng [references] [full-text] [DOI: 10.13164/re.2026.0315] [Download Citations]
An Ultra-Wideband High-Isolation Single-Antenna Full-Duplexer Based on Dual-Phase Cancellation

Wideband transmit–receive shared-antenna full-duplexers are a key technology for radar, communication, and electronic countermeasure systems. In special scenarios such as retrodirective cross-eye jamming, RF systems must adopt a TR shared-antenna architecture with a frequency coverage of 6–18 GHz. However, high-power signals leaking from the transmitter in this architecture interfere with the reception of useful signals at the receiver. To address this issue, this paper proposes an ultra-wideband high-isolation anti-symmetric duplexer. This duplexer is configured with one 180° hybrid coupler, one power divider, and two circulators. Based on a dual-phase cancellation mechanism, it theoretically enables complete suppression of self-interference signals induced by circulator leakage and antenna standing waves. Experimental results demonstrate that the proposed duplexer achieves Tx–Rx isolation exceeding 33 dB over the 6–18 GHz band. Compared with traditional duplex systems based on a single circulator and an orthogonal balanced network, the proposed design improves isolation by 13–22 dB. This work represents the first passive full-duplexer architecture to simultaneously cancel both circulator leakage and antenna reflections over the full 6–18 GHz band. This scheme can be extended to applications in retrodirective cross-eye jammers as well as other radio-frequency systems that face wideband isolation problems.

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Keywords: Transmit-receive isolation, full-duplexer, ultra-wideband, retrodirective cross-eye jamming

L. Qiao, M. W. Shen, R. Y. Chen, Y. S. Zhang, D. Wu, D. Y. Zhu [references] [full-text] [DOI: 10.13164/re.2026.0323] [Download Citations]
Anti-Jamming Multi-Target Parameter Estimation Using Space-Time Cascaded Adaptive Monopulse

The multi-target space-time cascaded monopulse (M-STCMP) algorithm is an efficient method for parameter estimation in radar systems. However, under jamming conditions, the signal-to-jamming-plus-noise ratio (SJNR) deteriorates significantly, causing the sum and difference beam weights computed by the M-STCMP algorithm to become unreliable for target parameter estimation. To address this limitation, this paper proposes an anti-jamming multi-target space-time cascaded monopulse (AM-STCMP) algorithm as a robust framework for multi-target parameter estimation. The proposed AM-STCMP algorithm improves the conventional M-STCMP framework by integrating spatial adaptive monopulse processing. Unlike conventional derivative-based methods, this approach adaptively optimizes the sum and difference beam weights through maximum likelihood estimation, thereby effectively suppressing strong jamming while maintaining estimation accuracy. In addition, iterative optimization of the angle discrimination curve enhances the SJNR and improves parameter estimation accuracy. In the subsequent processing stage, the algorithm employs space-time cascaded monopulse processing for efficient range-velocity estimation and uses the RELAX algorithm for high-precision angle-velocity-range estimation, thereby maintaining accuracy while reducing computational complexity. Theoretical analysis and Monte Carlo simulations validate the AM-STCMP algorithm and demonstrate its improved robustness under strong jamming conditions.

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Keywords: Spatial adaptive monopulse, space-time cascaded monopulse, jamming suppression, three-dimensional parameter estimation