Y. Cheng, J. Liu, J. Su [references] [full-text] [DOI: 10.13164/re.2026.0001] [Download Citations]
Research on Clutter Suppression Based on Complex-Valued Residual Network and Dynamic Reward Mechanism

As deep reinforcement learning becomes increasingly applied to clutter suppression, existing methods have shown a certain level of adaptability. However, their capabilities in feature representation and generalization remain limited. To address the shortcomings associated with the static reward mechanism—namely, its limited adaptability and slow learning speed—a Complex-Valued Residual Deep Q-Network based on a Dynamic Reward Function (CV-ResDQN-DRF) is proposed in this study. In this method, complex-valued residual units are introduced into the complex-valued neural network framework. Through these units, a complex-valued residual network is constructed to enhance the representational capacity of both amplitude and phase features of signals. Simultaneously, a dynamic reward mechanism is designed, wherein the feedback is adaptively adjusted in real time according to the environmental states and the agent’s behavior, thereby accelerating the learning process. Experimental results show that the proposed CV-ResDQN-DRF model achieves an average signal-to-clutter-plus-noise ratio (SCNR) improvement of approximately 2.3 dB on simulated data and 1.8 dB on real measured data, and exhibits a significantly faster convergence speed. These results demonstrate a significant enhancement in clutter suppression performance under complex and non-stationary environments.

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Keywords: Clutter suppression, complex-valued residual network, dynamic reward function, deep reinforcement learning

W. Xu, J. Zhang, Z. Su [references] [full-text] [DOI: 10.13164/re.2026.0015] [Download Citations]
Explainable Spectrum Prediction Based on VMD-LSTM

To improve the accuracy and interpretability of neural network enabled spectrum prediction, an explainable spectrum prediction framework based on Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) networks, integrated with the Shapley Additive Explanations (SHAP) method (VMD-LSTM), is proposed in this work. Firstly, the raw spectrum data is decomposed into multiple Intrinsic Mode Functions (IMFs) via VMD to reduce sequence complexity. These IMFs are then fed into the LSTM network in parallel to improve prediction accuracy. Secondly, the SHAP method is incorporated to evaluate the impact weights of individual IMF components on the prediction outcomes, revealing the model's decision-making logic. Finally, we weight the input data by multiplying each IMF by its SHAP value to optimize prediction performance. Simulation results based on real spectrum data demonstrate that the proposed VMD-LSTM significantly outperforms baseline models on the metrics of Weighted Quality Evaluation Index (WQE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and mean absolute error (MAE). By incorporating SHAP weights to refine the model input features, the framework not only provides transparent explanations for the black-box model but also reduces the average WQE, RMSE, and MAPE by 3.99%, 3.23%, and 3.67%, respectively.

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Keywords: Spectrum prediction, Shapley additive explanations, variational mode decomposition, explainable artificial intelligence

Y. Cheng, X. Zhang, Y. Yan [references] [full-text] [DOI: 10.13164/re.2026.0026] [Download Citations]
Adaptive IMM-Based Smoothing Probabilistic Data Association for Maneuvering Target Tracking in Cluttered Environments

Modern radar systems face many challenges, including complex nonlinear motion modelling, real-time changes of target motion model and difficult target trajectory estimation under low signal noise ratio when tracking high maneuvering targets in a cluttered environment. Therefore, an improved probabilistic data association tracking algorithm, termed adaptive transition probability matrix and improved smoothing integrated probabilistic data association (ATPM-ISIPDA) by embedding adaptive interactive multiple models (IMM) and parallel cubature information filter (PCIF) is proposed. Based on the fixed-lag smoothing integrated probabilistic data association (FLSIPDA) and IMM framework, the proposed algorithm uses the model posterior information to adaptively adjust the model transition probability, thereby enhancing model matching accuracy. Additionally, the parallel cubature information filter (PCIF) is utilized in each IMM sub-filter to suppress the state estimation error of nonlinear systems. In the fusion stage, the multi-branch cubature Kalman filter (CKF) prediction results are fused by the weighted accumulation method of the information matrix and vector, and the optimized smoothed state predictions and covariance matrices are generated. Then, the smoothed component data association probability is calculated to obtain the final state estimate, enhancing the fusion and smoothing performance of forward and backward tracks. The simulation results show that compared to the traditional IMM-IPDA algorithm, the average position RMSE is reduced by 31.1%, the FTD accuracy is improved by 15%-20%, and it still maintains a good tracking confirmation rate in cluttered environments.

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Keywords: ATPM, data association, smoothing, false-track discrimination, parallel cubature information filter

Z. R. Hong, Q. F. Lu, G. Q. Bao [references] [full-text] [DOI: 10.13164/re.2026.0041] [Download Citations]
Enhanced Recognition of Naval Ship HRRP Targets Using Improved Adaptive Threshold Wavelet Denoising

To address the challenges of noise interference and low signal-to-noise ratio (SNR) in measured one-dimensional ship range profile data, which significantly affect target recognition, a new method is proposed. An improved adaptive threshold wavelet denoising (IATWD) method is introduced. Initially, the two critical parameters of wavelet denoising (WD)—namely, the threshold and threshold functions (TFs)—are optimized. For threshold optimization, a formula related to the number of decomposition levels, the noise standard deviations per level, and the signal length is developed. As decomposition levels change, an optimal threshold can be adaptively determined for each level. Regarding threshold function (TF) improvement, an enhanced TF is designed that flexibly adjusts based on the benefits of both soft and hard TFs. Subsequently, by analyzing the interactions between the variable factors, wavelet base functions, and decomposition levels, optimal parameters for this denoising method are selected. Finally, the efficacy of the denoising and its impact on recognition were validated using denoising evaluation metrics and a Support Vector Machine (SVM) for both simulated and empirical data. Experimental results with both data types demonstrate that the IATWD method significantly outperforms both traditional WD and comparative improved methods in terms of denoising effectiveness and recognition rates.

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Keywords: High resolution range profiles, classification recognition, adaptive threshold, improved threshold functions, wavelet denoising

B. N. Tran-Thi, T. H. Le [references] [full-text] [DOI: 10.13164/re.2026.0056] [Download Citations]
Reduced Check Node Storage for Hardware-Efficient LDPC Decoder

This paper proposes a hardware- and memory-efficient architecture for Low-Density Parity-Check (LDPC) decoding, targeting enhanced-performance applications with constrained resources. The design integrates two novel techniques: (i) the Variable Single minimum Min-Sum (VSMS) algorithm, which reduces hardware complexity by identifying the first minimum value and its position during check node processing, while improving error correction through a correction factor applied in variable node updates; and (ii) a memory splitting strategy that exploits the structural properties of LDPC codes to optimize memory usage. Implementation on a Xilinx Kintex UltraScale+ (xcku5p) FPGA demonstrates a reduction in storage requirements by over 46.2% compared to conventional decoders. Furthermore, the proposed decoder achieves a performance gain of up to 0.38 dB at a Bit Error Rate (BER) of 10⁻⁸, outperforming traditional Min-Sum-based approaches.

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Keywords: 5G NR, error correction, FPGA, LDPC decoder, VSMS algorithm

N. M. Hung, H. M. Thuan, N. M. Giang [references] [full-text] [DOI: 10.13164/re.2026.0067] [Download Citations]
Planar Dual-Band Baluns with Large Frequency Ratios and Improved Performance

In this paper, a novel design method for dual-band baluns operating with a high frequency ratio between the two bands is presented. The circuit is designed based on a dual-band cross-shaped impedance transformation circuit combined with a dual-band Pi-shaped phase shifter. The investigation results show that the proposed balun structure achieves a frequency ratio f2/f1 of up to 4.9, which exceeds the typical limits reported for most previously published dual-band balun circuits. The enhanced frequency ratio between the two bands enables the circuit to be applicable in wideband applications. To evaluate the effectiveness of the proposed design method, a prototype of dual-band balun operating at two bands f1=0.7 GHz and f2=2.3 GHz was designed, fabricated and measured. The measured results demonstrate that the proposed dual-band balun not only achieves a significantly large frequency ratio between the two bands but also ensures good performance in key parameters, particularly exhibiting a substantial improvement in amplitude imbalances. At the operating frequencies, the balun exhibits an insertion loss of less than 0.9 dB, isolation better than 23.4 dB, amplitude imbalance less than 0.19 dB, and phase difference of 180 ± 4.4 degrees

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Keywords: Balun, dual-band, Pi-shaped, cross-shaped, large frequency ratio

N. Pritha, S. Maheswari [references] [full-text] [DOI: 10.13164/re.2026.0075] [Download Citations]
Asymmetric Coupled-Resonator Bandpass Filter for Ultra-Wide Stopband

The research presents a novel combination of an asymmetric coupling with a stepped impedance resonator (ACSIR) to realize high performance, especially an ultra-wide stopband rejection. A basic second-order bandpass filter was developed and subsequently extended to a fourth-order configuration to improve key performance metrics, including wide stopband characteristics and compact size, without compromising insertion loss. The use of asymmetric coupling enables optimization of the filter response while preserving a simple configuration. Furthermore, the resonance conditions associated with the asymmetric coupling were examined through mathematical analysis. The proposed ACSIR filter achieved an enhanced fractional bandwidth (FBW) of 15.4%, a low insertion loss of 0.64 dB, and wide stopband rejection extending up to 7.25 f₀ tailored for WLAN applications operating at 2.45 GHz.

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Keywords: Stepped impedance resonator, asymmetric coupled-resonator, bandpass filter, ultra-wide stopband rejection

J. Pan, H. Wang, J. Xu, H. Xu [references] [full-text] [DOI: 10.13164/re.2026.0084] [Download Citations]
A Pre-impact Fall Algorithm Based on a Lightweight Re-Parameters-Parallel Convolutional-TCN

With the aging society intensifying, the problem of elderly falls has become a key issue of social concern. Research on fall prediction based on Internet of Things (IoT) technology has received widespread attention. To effectively predict fall events, a lightweight IoT-based fall prediction model called lwRPPC-TCN (lightweight Re-Parameters-Parallel-Convolutional Temporal Convolutional Network) is proposed. The model utilizes the temporal data collected by IoT sensors in the input stage and achieves efficient decoupled extraction of temporal and spatial features through lwRPPC blocks. The subsequent Temporal Convolutional Networks (TCNs) further strengthens the ability of modeling the global temporal dependency, thus optimizing the processing capability of sensor time-series data. To validate the generalization ability of the model and mitigate fall data scarcity, two public datasets, SisFall and KFall, are fused, and the performance of the model is evaluated by five-fold cross-validation. In addition, a homogeneous (models belong to the same model family) knowledge distillation technique is introduced to improve the performance of the model. Experimental results demonstrate that the proposed lwRPPC-TCN achieves an accuracy of 98.88% on the fused dataset, outperforming existing fall prediction models, with a fall prediction lead time (interval between the fall prediction time and the collision time) of 250ms, and a compact model size of 60 KB, which makes it suitable and possible to deploy in a resource-constrained wearable device.

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Keywords: Deep learning, fall prediction, knowledge distillation, Re-Parameters-Parallel Convolutional-Temporal Convolutional Network, Inertial sensor, spatio-temporal feature decoupling

F. Z. Hamrioui, R. Touhami, M. Al Sabbagh, M. C. E. Yagoub [references] [full-text] [DOI: 10.13164/re.2026.0095] [Download Citations]
Compact Wideband 5G SIW Bandpass Filter with Enhanced Selectivity Using Mixed-Coupling Butterfly CSRRs

In this work, a novel integrated waveguide bandpass filter for 5G applications is presented. The proposed compact filter is loaded by mixed coupling butterfly shaped complementary split ring resonators (B-CSRRs). Initially, the design exhibited only one transmission zero at higher stop-band. Thus, an asymmetric etched slot has been inserted between the split of each face-to-face B-CSRRs to produce an induced mixed coupling, thereby allowing the emergence of an additional transmission zero at lower stop-band. To further improve the selectivity factor, a second-order SIW filter using two-mixed coupling face-to-face B-CSRRs, separated by a Plus shaped Ring Slot Resonator was designed. The measured results show good performance, including low insertion loss of 1.46 dB, high selectivity factor of 55.75%, compact size of 0.107 〖λ_g〗^2 (with g the guided wave at the center frequency fc = 4.60 GHz), and high fractional bandwidth of 13.70% (i.e., 4.28-4.91 GHz). By covering the N79 5G band with a minimum attenuation of 40 dB from dc to 3.74 GHz and a minimum attenuation of 20 dB from 5.15 to 9.43 GHz, the proposed filter can be used for sub-6GHz 5G applications, as it prevents the interference between the N79 band and WiFi 5 GHz.

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Keywords: 5G, complementary split ring resonators (CSRR), filter, selectivity factor, substrate integrated waveguide (SIW)

M. I. Al-Rayif, E. E. Eldukhri [references] [full-text] [DOI: 10.13164/re.2026.0105] [Download Citations]
A Neural Network-Enabled OTFS-PAPR Reduction with Low Computational Complexity

This study proposes a new solution to overcome the high peak-to-average power ratio (PAPR) in Orthogonal Time Frequency Space (OTFS) by using an Artificial Neural Network (ANN) algorithm. The algorithm checks the magnitude (power) of each element in the matrix of the first stage of the inverse symplectic finite Fourier transform (ISFFT) process against a pre-specified threshold and, consequently adjusts the elements whose magnitudes exceed the threshold. This is achieved by using the ANN algorithm to apply fractional shifts to the elements of the original delay-Doppler (DD) data matrix without changing their orientation. The simulation results demonstrated a significant PAPR reduction while maintaining the system performance in terms of the Bit Error Rate (BER), with almost the same computational complexity of the conventional OTFS system.

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Keywords: OTFS, PAPR, ISFFT, artificial neural networks