adaptive interference cancellation

This study addresses the challenge of contamination in stereo audio recordings caused by real-world train noise and environmental acoustic interference. To this end, it proposes a multi-reference recursive least squares (RLS) adaptive noise cancellation method that operates without requiring a clean reference signal. The approach leverages stereo reference signals originating from the same noise source, employing per-channel 30th-order adaptive filters, a 15th-order anti-causal structure, a forgetting factor of 0.999, and an FIR low-pass post-filter to effectively model and suppress interference components under complex propagation conditions. Experimental results demonstrate that the method reduces the correlation between residual output and reference signals to 0.011–0.016, achieving a correlation ratio attenuation of 30.6–34.1 dB and an RMS reduction of 1.8–4.8 dB. This work significantly enhances audio quality in the absence of ground-truth clean data and represents the first successful application of efficient multichannel noise cancellation to real train noise.

Adaptive filters lack a unified theoretical foundation. Method: This paper proposes a general Bayesian recursive inference framework, modeling observation noise as Gaussian or Laplacian to systematically derive classical algorithms—including LMS, NLMS, and Kalman filtering—as well as a novel family of robust filters. Contribution/Results: It establishes, for the first time, a unifying Bayesian interpretation encompassing both conventional and robust adaptive filters. Compared to conventional sign-error methods, the proposed algorithms exhibit superior robustness and convergence under Laplacian noise. The framework integrates state-space modeling, probabilistic noise characterization, and simplified structural analysis, ensuring both interpretability and extensibility. Numerical experiments demonstrate the algorithms’ enhanced performance in non-Gaussian noise environments. Overall, this work provides a rigorous, unified Bayesian theoretical basis for the design and analysis of adaptive filters.

To address the performance degradation of conventional recursive least squares (RLS) algorithms under strong harmonic interference in power grid event estimation, this paper proposes a novel RLS algorithm featuring a second-order update mechanism. The method integrates exponential and instantaneous forgetting strategies, reconstructs the parameter update formulation using second-order gradient information, and establishes new theoretical properties regarding the convergence of both the inverse information matrix and the parameter vector—enabling superior adaptive forgetting design. Compared with classical first-order RLS, the proposed algorithm achieves significantly improved tracking accuracy and faster convergence in dynamic harmonic environments. Its effectiveness and robustness are validated across multiple typical grid events, including voltage sags and resonance transients. The approach provides a new paradigm for real-time state estimation in high-interference scenarios.

This work proposes a novel subband adaptive filtering framework based on the recent Kronecker product decomposition to address the slow convergence under highly correlated inputs, high computational complexity, and poor robustness against impulsive noise exhibited by the conventional NKP-NLMS algorithm. By integrating Type-I and an improved Type-II structure, the proposed approach significantly enhances convergence speed while reducing computational cost. Furthermore, robustness and nonlinear modeling capability are strengthened through the incorporation of the maximum correntropy criterion, logarithmic cost functions, and nonlinear extensions such as trigonometric link networks and Volterra series, with the framework extended to active noise control scenarios. Experimental results demonstrate that the proposed NSAF-NKP-II and its robust variants consistently outperform state-of-the-art methods in tasks including echo cancellation, sparse system identification, and nonlinear signal processing, offering superior efficiency, robustness, and practical applicability.

The adaptive radar signal processing (RASP) community lacks large-scale, high-fidelity, geographically diverse real-world clutter benchmark datasets, hindering the development of data-driven models and standardized algorithm evaluation. Method: We introduce RASPNet—an open-source, >16 TB benchmark dataset—comprising 10,000 complex-valued airborne radar clutter snapshots per scene across 100 geographically distinct real-world locations in the contiguous United States. It integrates GIS-driven scene modeling, complex-valued signal acquisition, and structured metadata annotation. Contribution/Results: RASPNet is the first publicly available RASP benchmark enabling rigorous cross-scene generalization and transfer learning validation. Experiments demonstrate that transfer models trained on RASPNet achieve a 3.2 dB improvement in signal-to-interference-plus-noise ratio (SINR) for clutter suppression in unseen regions, significantly advancing the practical deployment of complex-domain deep learning in operational radar systems.

为解决长滤波器回声消除中RLS算法计算复杂度高的问题，提出了一种正则化块对角RLS算法，通过简化更新过程和并行计算降低复杂度。

This work addresses the limitations of existing nonlinear adaptive filtering algorithms, which often neglect input noise and exhibit insufficient robustness under non-Gaussian output noise. To overcome these issues, the paper proposes the RFFBCGA algorithm, which synergistically integrates random Fourier features, a bias-compensation mechanism, and a generalized adaptive function within a fixed network architecture. This integration effectively mitigates the adverse effects of input noise while enhancing signal representation capability. By transcending the constraints of conventional fixed dictionaries, the method significantly improves robustness against both input perturbations and non-Gaussian output noise. Experimental results demonstrate that RFFBCGA consistently achieves superior performance, stability, and adaptability across a range of simulated and real-world time series prediction tasks.

This work addresses the performance limitations of traditional adaptive room equalization methods, such as the filtered-x least mean squares (Fx-LMS) algorithm, which suffer from structural rigidity in time-varying acoustic environments and under complex excitations like music. The authors propose a modular, differentiable digital signal processing (DDSP) framework that, for the first time, integrates classical adaptive filtering with DDSP. By leveraging automatic differentiation, the Fx-LMS algorithm is embedded within a unified formulation, enabling flexible substitution of equalization structures, room response estimators, loss functions, and optimizers. The framework reveals that frequency-domain objectives yield superior adaptation stability in time-varying scenarios. Experimental results demonstrate up to a 70% reduction in system distance and a 13% decrease in Mel-spectral distance, while elucidating the trade-off between room response estimation accuracy, frame length, convergence stability, and adaptation speed.

This study addresses the challenge of detecting subspace signals embedded in non-zero-mean clutter by developing adaptive detectors based on the generalized likelihood ratio test (GLRT), Rao, Wald, gradient, and Durbin criteria. Closed-form expressions for the probabilities of false alarm and detection are derived, revealing two key performance degradations compared to the zero-mean case: a reduction of one effective degree of freedom and a signal-to-clutter ratio loss. Theoretical analysis demonstrates that, while the detector structures remain identical to those in zero-mean scenarios, their performance is significantly influenced by the clutter mean. The effectiveness of the proposed approach and its potential for practical radar applications are validated through both simulated and real-world data.

This work addresses the longstanding trade-off between performance and latency in large finite impulse response (FIR) filters commonly used in image, video, and audio processing. The authors propose a unified design language that abstracts multirate filtering, recursive filtering, and filter decomposition into composable primitives. By combining program-space search with gradient-based optimization of continuous parameters, the framework automatically synthesizes Pareto-optimal approximate filtering algorithms. This approach enables, for the first time, the systematic integration of diverse fast filtering techniques and fully automated code generation, producing vectorized and parallelized C++ implementations. Evaluated across multiple mainstream image and audio tasks, the generated filters consistently outperform existing methods in both speed and accuracy.