Signal Processing最新文献

Aiming at the problem of accurate tracking of group objects, where multiple closely spaced objects within a group pose a coordinated motion, this paper develops a new type of labeled random finite set (LRFS), i.e., augmented LRFS, which inherently integrates group information such as the group geometry center and the group index into the definition of LRFS. Specifically, for each element in an augmented LRFS, the kinetic states, the track labels, and the corresponding group information of its represented object are incorporated. Then, by means of the proposed augmented LRFS-based filter, i.e., the labeled multi-Bernoulli filter with the proposed augmented LRFS, the group structure is iteratively propagated and updated during the tracking process, which achieves the holistic estimation of the kinetic states, track labels, and the corresponding group information of multiple group objects, and further improves the tracking performance. Finally, simulation experiments are conducted to verify the effectiveness of the proposed augmented LRFS-based filter.

Recently, a modified adapt-then-combine diffusion (mATC) strategy has been developed to handle distributed estimation problem with missing regressions (inputs). However, the mATC algorithm only considers the white input scenario and suffers from high complexity for long model filter lengths. To overcome these shortcomings, this paper proposes novel regularization-based frequency-domain diffusion algorithms for networks with missing input data. First, bias-eliminating cost function based on regularization is established by using the frequency-domain diagonal approximation. Then, with stochastic gradient descent, periodic update, and power normalization schemes, we design the regularization-based frequency-domain least mean square (R-FDLMS) algorithm as well as its normalized variant (R-FDNLMS). The latter converges faster than the former under colored inputs. The stability and steady-state behavior of the R-FDNLMS algorithm are also analyzed. Moreover, two effective power estimation methods are presented for both situations without and with the power ratio between the input signal and perturbation noise, along with a reset mechanism in the first case to enhance tracking performance. Finally, simulations are conducted to illustrate the superiority of the proposed algorithms and the validity of theoretical findings.

This paper focuses on the issue of event-triggered nonlinear state estimation for multi-sensor networks. An event-triggered mechanism reduces data transmission, balancing communication rate and estimation performance through triggered thresholds. After that, a novel event-triggered robust filter is proposed. The non-triggered case is a non-Gaussian process. The fading matrix adaptively adjusts the noise variance and the gain matrix is designed by the maximum correntropy criterion, avoiding the conservatism and randomness brought by the upper bound. Subsequently, an event-triggered distributed diffusion robust cubature Kalman filter is presented relying on the cubature criterion, covariance intersection technique and diffusion fusion strategy. Compared with average consensus fusion, the error covariance is utilized to compute the weights in real time and does not involve complicated iterative processes. Moreover, the consistency, convergence and stability are proven under certain conditions. Finally, the simulation results verify the effectiveness and accuracies of the proposed algorithm.

In the study of vacating-room-after-encryption reversible data hiding in encrypted images (VRAE RDHEI), pixel prediction is an important mechanism to achieve reversibility, which has a crucial impact on the capacity and fidelity. In this paper, we propose a novel pixel-level masked autoencoders (PLMAE) as a high-performance pixel predictor for RDHEI. Unlike the original masked autoencoders (MAE), PLMAE focuses on pixel-level reconstruction rather than semantic patch-level reconstruction. The purpose of PLMAE is to spare more carrier pixels while maintaining relatively high prediction accuracy, thereby improving the RDHEI capacity. Based on PLMAE, a novel RDHEI method is proposed. In the proposed method, the data hider encodes the secret data using a polar code and then embeds the encoded data. After the image is decrypted, the receiver considers the carrier pixels as masked pixels, predicts the original states of the carrier pixels using PLMAE to extract the secret data, and then decodes the secret data and recovers the image based on the decoding results. The experimental results demonstrate that the proposed method in this paper can achieve better performance than the existing methods.

Inferring graph topologies from data is crucial in many graph-related applications. Existing works typically assume that signals are observed at all nodes, which may not hold due to application-specific constraints. The problem becomes more challenging when data are sequentially available and no delay is tolerated. To address these issues, we propose an approach for learning graphs from incomplete data. First, the problem of learning graphs with missing data is formulated as maximizing the posterior distribution with hidden variables from a Bayesian perspective. Then, we propose an expectation maximization (EM) algorithm to solve the induced problem, in which graph learning and graph signal recovery are jointly performed. Furthermore, we extend the proposed EM algorithm to an online version to accommodate the delay-sensitive situations of sequential data. Theoretically, we analyze the dynamic regret of the proposed online algorithm, illustrating the effectiveness of our algorithm in tracking graphs from partial observations in an online manner. Finally, extensive experiments on synthetic and real data are conducted, and the results corroborate that our approach can learn graphs effectively from incomplete data in both batch and online situations.

Phase Retrieval (PR) is an ill-posed inverse problem which arises in various science and engineering applications. Recently, it has been empirically shown that unrolled iterative methods or model-driven deep learning methods are effective for solving this problem. However, the prior modules in these model-driven networks lack model interpretability, leading to a lack of rigorous analysis about the convergence behaviors of these re-implemented iterations, and thus the significance of such PR methods is a little bit vague. For this issue, this paper proposes an effective and provable Unrolled Phase Retrieval Iterative MEthod (UPrime) for the PR problem. Our theoretical analysis demonstrates that UPrime using an elaborated bounded prior module can generate fixed-point convergent trajectories. Meanwhile, the proposed prior module, a flexible and interpretable module, is beneficial for the convergence analysis of regularized imaging methods in the non-convex scenario. Experiments on coded diffraction imaging applications verify the superiority of UPrime.

The paper aims to bridge a part of the gap between source separation and sensor placement studies by addressing a novel problem: “Predicting optimal sensor placement in noisy environments to improve source separation quality”. The structural information required for optimal sensor placement is modeled as the spatial distribution of source signal gains and the spatial correlation of noise. The sensor positions are predicted by optimizing two criteria as measures of separation quality, and a gradient-based global optimization method is developed to efficiently address this optimization problem. Numerical results exhibit superior performance when compared with classical sensor placement methodologies based on mutual information, underscoring the critical role of sensor placement in source separation with noisy sensor measurements. The proposed method is applied to actual electroencephalography (EEG) data to separate the P300 source components in a brain-computer interface (BCI) application. The results show that when the sensor positions are chosen using the proposed method, to reach a certain level of spelling accuracy, fewer sensors are required compared with standard sensor locations.

This paper develops a statistically efficient gridless two-dimensional (2D) direction-of-arrival (DOA) estimation method for sparse planar arrays under the coarray signal model. Our approach is based on the 2-level Toeplitz structure of the augmented covariance matrix and includes two steps. In the first step, to reconstruct the 2-level Toeplitz augmented covariance matrix, we propose a rank-constrained weighted least squares (WLS) method and then design an alternating direction method of multipliers (ADMM) algorithm to implement it. Compared to the conventional coarray-based scheme, the proposed method considers the distribution of the array output and hence has better estimation accuracy. In addition, our augmented covariance matrix reconstruction method is still valid even if there exist holes in the difference coarray. In the second step, we present a gridless algorithm to recover and automatically pair DOAs from the estimate of the 2-level Toeplitz augmented covariance matrix. We theoretically show that the proposed estimator is consistent and its performance can attain the Cramér–Rao bound (CRB) for a large number of snapshots. Numerical results confirm the statistical efficiency of our approach.

A hypothesis testing based pre-processing procedure is proposed in this paper to reduce the computational complexity of latent group lasso (LGL) problem. The redundant overlapping support groups can be efficiently pruned while the desired groups are kept at a guaranteed rate. Three different schemes of hypothesis testing are theoretically studied and empirically compared in terms of complexity reduction, pruning accuracy, and recovery error. Of possible independent interest, the optimal designs of test statistics are also pursued to make explicit use of different signal structural priors. The theoretical claims are demonstrated via extensive numerical experiments under different settings and the proposed pre-processing procedure exhibits obvious empirical superiority in the concerned aspects.