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Radioengineering

Radioeng

Proceedings of Czech and Slovak Technical Universities

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April 2026, Volume 35, Number 1 [DOI: 10.13164/re.2026-1]

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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