Unknown Prior Information Research Articles

Robust adaptive non‐linear alignment algorithm for SINS/DVL integrated navigation system based on variational Bayesian

AbstractThe problem of dynamic attitude alignment for strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation has become a research hotspot. Underwater complex environment makes DVL output vulnerable to non‐Gaussian noise pollution, which makes it difficult to converge the SINS misalignment angle to within 1° based on analytical coarse alignment. This is to say, the performance of SINS fine alignment via Kalman filter will be degraded because the model of initial alignment exhibits non‐linearity. The inaccurate and/or unknown prior information of measurement noise statistical characteristics will also degrade the filtering performance. To ensure the SINS/DVL alignment accuracy under non‐linear, non‐Gaussian and uncertain conditions, this paper proposed a robust adaptive unscented Kalman filter (UKF) based on Variational Bayesian (VB) method (VBRAUKF). The proposed VBRAUKF improves the adaptability and robustness of UKF from the following two main aspects. Firstly, an adaptive estimation strategy for the measurement noise covariance is designed based on the VB algorithm, which can restrain the effect of inaccurate observation model and improve the adaptive ability of the filtering method. Secondly, an expansion factor is designed based on Mahalanobis distance algorithm, which can restrain the effect of non‐Gaussian noise and improve the robustness of the filtering method. The experimental results for the problem of the SINS/DVL dynamic alignment under mixed Gaussian noise and/or outlier conditions demonstrate the superiority of the proposed VBRAUKF over the traditional ones.

Joint Direction of Arrival-Polarization Parameter Tracking Algorithm Based on Multi-Target Multi-Bernoulli Filter

This paper presents a tracking algorithm for joint estimation of direction of arrival (DOA) and polarization parameters, which exhibit dynamic behavior due to the movement of signal source carriers. The proposed algorithm addresses the challenge of real-time estimation in multi-target scenarios with an unknown number. This algorithm is built upon the Multi-target Multi-Bernoulli (MeMBer) filter algorithm, which makes use of a sensor array called Circular Orthogonal Double-Dipole (CODD). The algorithm begins by constructing a Minimum Description Length (MDL) principle, taking advantage of the characteristics of the polarization-sensitive array. This allows for adaptive estimation of the number of signal sources and facilitates the separation of the noise subspace. Subsequently, the joint parameter Multiple Signal Classification (MUSIC) spatial spectrum function is employed as the pseudo-likelihood function, overcoming the limitations imposed by unknown prior information constraints. To approximate the posterior distribution of MeMBer filters, Sequential Monte Carlo (SMC) method is utilized. The simulation results demonstrate that the proposed algorithm achieves excellent tracking accuracy in joint DOA-polarization parameter estimation, whether in scenarios with known or unknown numbers of signal sources. Moreover, the algorithm demonstrates robust tracking convergence even under low Signal-to-Noise Ratio (SNR) conditions.

Overlapping group prior for image deconvolution using patch-wise gradient statistics

In image deconvolution problems, global gradient statistics serve as powerful image priors that separate high-quality images from degraded observations. However, it is unclear whether the important detailed information is sufficiently regularized in these approaches. Owing to the limitations, the restored images exhibit sharply reconstructed edges around structures, but commonly suffer from the loss of significant textures and details. In this study, we introduced patch-wise gradient statistics as an image prior strategy to restore disregarded regions in consideration of regional characteristics. The analytic relation between the prior and the restored local statistics was investigated through the Bayesian interpretation of overlapping group sparsity. The regularization function that incorporates the contextual information was derived in the maximum a posteriori framework. Subsequently, the unknown prior information and latent image were alternately estimated from the observations based on the patch-wise statistical analysis of the non-blind deconvolution process. In the experimental results, the superiority of the proposed algorithm was verified by more original textures in the restored images compared to the conventional approaches.

Partial least squares regression with conditional orthogonal projection for variable selection

Partial least squares regression (PLS) is a popular multivariate statistical analysis method. It not only can deal with high-dimensional variables but also can effectively select variables. However, the traditional PLS variable selection approaches cannot deal with some prior important variables. In this article, we propose two filter PLS variable selection algorithms (CPLS-BETA, CPLS-VIP) that combine with the prior variables by the conditional orthogonal projection. The performance of variable selection can be improved by projecting the other variables and response orthogonally on some prior active variables. Moreover, we introduce a kind of data-driven conditional method named forward projection PLS (FPPLS), which is suitable for the situation of unknown prior information. Finally, the validity of our method is verified by simulated datasets and real datasets.

EM-EKF fast time-varying channel estimation based on superimposed pilot for high mobility OFDM systems

The scarcity of wireless spectrum resources and high mobility have brought serious challenges to orthogonal frequency division multiplexing (OFDM) communication systems. To improve spectral efficiency and combat fast time-varying channel fading, this paper studies the OFDM system with high mobility based on superimposed pilots, and adopts a basis expansion model (BEM) to describe fast time-varying channels. An iterative channel estimation algorithm combining expectation–maximization (EM) and extended Kalman filtering (EKF) is proposed, which is called EM-EKF in this paper. It uses EM to calculate the parameter matrix, measured values and the covariance of the process noise of the unknown first-order time-varying autoregressive (TVAR) model to obtain the optimal estimation. To further enhance the system’s robustness for fast time-varying channel fading, EKF and smoothing algorithms are jointly used for time-varying channel tracking. The results show that compared with the existing representative channel estimation algorithms, the iterative EM-EKF algorithm proposed in this paper not only solves the problem of unknown prior information, but also significantly outperforms other non-iterative algorithms, and the performance of the proposed algorithm is similar to that of the iterative EKF algorithm, which is more suitable for the actual OFDM communication systems with high mobility.

Adaptive Total-Variation Regularized Low-Rank Representation for Analyzing Single-Cell RNA-seq Data.

High-throughput sequencing of single-cell gene expression reveals a complex mechanism of individual cell's heterogeneity in a population. An important purpose for analyzing single-cell RNA sequencing (scRNA-seq) data is to identify cell subtypes and functions by cell clustering. To deal with high levels of noise and cellular heterogeneity, we introduced a new single cell data analysis model called Adaptive Total-Variation Regularized Low-Rank Representation (ATV-LRR). In scRNA-seq data, ATV-LRR can reconstruct the low-rank subspace structure to learn the similarity of cells. The low-rank representation can not only segment multiple linear subspaces, but also extract important information. Moreover, adaptive total variation also can remove cell noise and preserve cell feature details by learning the gradient information of the data. At the same time, to analyze scRNA-seq data with unknown prior information, we introduced the maximum eigenvalue method into the ATV-LRR model to automatically identify cell populations. The final clustering results show that the ATV-LRR model can detect cell types more effectively and stably.

Multidimensional Information Fusion in Active Sonar via the Generalized Labeled Multi-Bernoulli Filter

Under the conditions of low detection probabilities, high clutter rates, low data-sampling rates, large measurement errors, and unknown prior information of the target position, multi-object tracking is difficult. This paper proposes a multidimensional information fusion method in active sonar via the generalized labeled multi-Bernoulli (GLMB) filter. After modeling the position measurement, radial velocity measurement and amplitude measurement of the target and clutter, new target births are adaptively generated by the measurement-driven model, predictions are made by the target motion model and updates are performed via multidimensional measurements with the generalized likelihood in the GLMB filter, which enables the measurement information of different dimensions to be elegantly applied to information fusion and significantly improves the filter performance. The contribution of specific dimension measurement to fusion can be evaluated by the Kullback-Leibler (KL) divergence. In the efficient implementation, we propose flat Gibbs sampling to realize multiple hypothesis optimization. Moreover, the filtering recursion is derived from Gaussian mixtures. Simulations are presented to verify the proposed method.

Adaptive Digital Channelization of Sparse Wideband Analog Signals at Sub-Nyquist Rate

Considering that the conventional digital channelized receiver requires a high sampling rate for a wideband signal and produces a cross-channel problem due to unknown prior information of the received signals, a method of adaptive digital channelization based on compress sensing (CS) theory is proposed in this paper. The method consists of three parts, i.e., modulated wideband converter (MWC) sampling system, sparse recovery algorithm, synthesis filter banks. MWC sampling system is applied to receive sparse wideband analog signals at sub-Nyquist sampling rate. Sparse recovery algorithm such as sparsity adaptive matching pursuit (SAMP) is chosen to recover the channelized signals. Based on the adjacent channels containing signals, the complete wideband signal can be obtained by the synthesis filter banks adaptively, thus solving the cross-channel problem. Finally, the numerical experiments demonstrate that the proposed method can implement adaptive channelization of sparse wideband signal at sub-Nyquist sampling rate.

Fractional central difference Kalman filter with unknown prior information

Abstract In this paper, a generalized fractional central difference Kalman filter for nonlinear discrete fractional dynamic systems is proposed. Based on the Stirling interpolation formula, the presented algorithm can be implemented as no derivatives are needed. Besides, in order to estimate the state with unknown prior information, a maximum a posteriori principle based adaptive fractional central difference Kalman filter is derived. The adaptive algorithm can estimate the noise statistics and system state simultaneously. The unbiasedness of the proposed algorithm is analyzed. Several numerical examples demonstrate the accuracy and effectiveness of the two Kalman filters.

Novel underdetermined blind source separation algorithm based on compressed sensing and K‐SVD

AbstractIn this paper, a novel two‐stage underdetermined blind source separation (UBSS) algorithm based on eigenvalue decomposition and compressed sensing (CS) is proposed. UBSS techniques can separate the complex mixed signals into multiple single signal components, which are widely applied in the field of electronic surveillance, spectrum management, radar applications, etc. Our major contributions are summarized as follows: (1) deriving a single source points detection algorithm based on eigenvalue decomposition; (2) proposing a novel hierarchical coupling dictionary training method based on K‐means singular value decomposition; and (3) using the two‐stage method to realize the separation of radar signals under the condition of unknown prior information. In the first stage, we present a single source point detection method involving eigenvalue decomposition to estimate the unknown mixing matrix. In this method, the eigenvector corresponding to the maximum eigenvalue is estimated and clustered as a vector of mixing matrix. The second stage is the recovery of sources from the observed signals utilizing the mixing matrix estimated by the first step. In the second stage, we first build the unified model of UBSS and CS, thus the reconstruction algorithm in the CS field can be applied to UBSS. Then, a hierarchical coupling dictionary training method based on K‐means singular value decomposition is proposed. The dictionary training method obtains the prior training signals by pre‐separation and employs the hierarchical coupling idea to train the dictionary efficiently. Simulations illustrate the validity of the method and show that the proposed method outperforms the traditional methods in source separation, especially in real‐world noncooperative cases without prior knowledge.

Tracklet-Global Track Fusion Using Support Degree Function in Sensor Networks

For the situation with unknown qualities of local tracks in sensor networks, a new tracklet-global track fusion method using the support degree function (SDF-T2GTF) is proposed. According to the characteristic of actual transmission modes, two local estimates of a moving target in adjacent interval transmitted by the same local node are defined as a tracklet, and subsequently tracklet-global track (T2GT) fusion can replace the traditional track fusion in the global node, namely local track-global track (LT2GT) fusion. Considered the advantage of the fuzzy track association (TA) method for unknown prior information of local tracks, it is used in T2GT association. Then all correlated tracklets in the same interval can be mapped into a set of points in parameter space by the Hough transform (HT) algorithm. The support degree function of these points is utilized to dynamically estimate the qualities of tracklets and reasonably allocates the weights of local estimates in fusion results. Hence, the proposed method can realize T2GT fusion without the prior information of local tracks. The experimental result illustrates that the proposed method can satisfy the requirement of data transmission in real systems, and can realize T2GT fusion; on the other, it can improve the performance of track fusion in accuracy compared with the traditional methods.

From Adaptive Reasoning to Cognitive Factory: Bringing Cognitive Intelligence to Manufacturing Technology

There are two important aspects that will play important roles in future manufacturing systems: changeability and human-machine collaboration. The first aspect, changeability, concerns with the ability of production tools to reconfigure themselves to the new manufacturing settings, possibly with unknown prior information, while maintaining their reliability at lowest cost. The second aspect, human-machine collaboration, emphasizes the ability of production tools to put themselves on the position as humans’ co-workers. The interplay between these two aspects will not only determine the economical accomplishment of a manufacturing process, but it will also shape the future of the technology itself. To address this future challenge of manufacturing systems, the concept of Cognitive Factory was proposed. Along this line, machines and processes are equipped with cognitive capabilities in order to allow them to assess and increase their scope of operation autonomously. However, the technical implementation of such a concept is still widely open for research, since there are several stumbling blocks that limit practicality of the proposed methods. In this paper, we introduce our method to achieve the goal of the Cognitive Factory. Our method is inspired by the working mechanisms of a human’s brain; it works by harnessing the reasoning capabilities of cognitive architecture. By utilizing such an adaptive reasoning mechanism, we envision the future manufacturing systems with cognitive intelligence. We provide illustrative examples from our current research work to demonstrate that our proposed method is notable to address the primary issues of the Cognitive Factory: changeability and human-machine collaboration.

Equivalence of finite memory filters

The well-known Jazwinski's limited memory filter, Schweppe's finite memory filter, and Kwon's optimal finite impulse response (FIR) filter are compared in the filter structures, system models, and optimality criterions, and are shown to be equivalent on condition that they are applied to the discrete system with no process noise and unknown prior information of the system state. The different properties such as stability and computation burden are briefly discussed. Kwon's optimal FIR filter is shown to have some advantages in terms of stability and modeling constraints.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>