73 research outputs found
State Estimation of Wireless Sensor Networks in the Presence of Data Packet Drops and Non-Gaussian Noise
Distributed Kalman filter approaches based on the maximum correntropy
criterion have recently demonstrated superior state estimation performance to
that of conventional distributed Kalman filters for wireless sensor networks in
the presence of non-Gaussian impulsive noise. However, these algorithms
currently fail to take account of data packet drops. The present work addresses
this issue by proposing a distributed maximum correntropy Kalman filter that
accounts for data packet drops (i.e., the DMCKF-DPD algorithm). The
effectiveness and feasibility of the algorithm are verified by simulations
conducted in a wireless sensor network with intermittent observations due to
data packet drops under a non-Gaussian noise environment. Moreover, the
computational complexity of the DMCKF-DPD algorithm is demonstrated to be
moderate compared with that of a conventional distributed Kalman filter, and we
provide a sufficient condition to ensure the convergence of the proposed
algorithm
Proportionate Recursive Maximum Correntropy Criterion Adaptive Filtering Algorithms and their Performance Analysis
The maximum correntropy criterion (MCC) has been employed to design
outlier-robust adaptive filtering algorithms, among which the recursive MCC
(RMCC) algorithm is a typical one. Motivated by the success of our recently
proposed proportionate recursive least squares (PRLS) algorithm for sparse
system identification, we propose to introduce the proportionate updating (PU)
mechanism into the RMCC, leading to two sparsity-aware RMCC algorithms: the
proportionate recursive MCC (PRMCC) algorithm and the combinational PRMCC
(CPRMCC) algorithm. The CPRMCC is implemented as an adaptive convex combination
of two PRMCC filters. For PRMCC, its stability condition and mean-square
performance were analyzed. Based on the analysis, optimal parameter selection
in nonstationary environments was obtained. Performance study of CPRMCC was
also provided and showed that the CPRMCC performs at least as well as the
better component PRMCC filter in steady state. Numerical simulations of sparse
system identification corroborate the advantage of proposed algorithms as well
as the validity of theoretical analysis
Robust Sensor Fusion for Indoor Wireless Localization
Location knowledge in indoor environment using Indoor Positioning Systems
(IPS) has become very useful and popular in recent years. Indoor wireless
localization suffers from severe multi-path fading and non-line-of-sight
conditions. This paper presents a novel indoor localization framework based on
sensor fusion of Zigbee Wireless Sensor Networks (WSN) using Received Signal
Strength (RSS). The unknown position is equipped with two or more mobile nodes.
The range between two mobile nodes is fixed as priori. The attitude (roll,
pitch, and yaw) of the mobile node are measured by inertial sensors (ISs). Then
the angle and the range between any two nodes can be obtained, and thus the
path between the two nodes can be modeled as a curve. Through an efficient
cooperation between two or more mobile nodes, this framework effectively
exploits the RSS techniques. This constraint help improve the positioning
accuracy. Theoretical analysis on localization distortion and Monte Carlo
simulations shows that the proposed cooperative strategy of multiple nodes with
extended Kalman filter (EKF) achieves significantly higher positioning accuracy
than the existing systems, especially in heavily obstructed scenarios
Distributed fusion filter over lossy wireless sensor networks with the presence of non-Gaussian noise
The information transmission between nodes in a wireless sensor networks
(WSNs) often causes packet loss due to denial-of-service (DoS) attack, energy
limitations, and environmental factors, and the information that is
successfully transmitted can also be contaminated by non-Gaussian noise. The
presence of these two factors poses a challenge for distributed state
estimation (DSE) over WSNs. In this paper, a generalized packet drop model is
proposed to describe the packet loss phenomenon caused by DoS attacks and other
factors. Moreover, a modified maximum correntropy Kalman filter is given, and
it is extended to distributed form (DM-MCKF). In addition, a distributed
modified maximum correntropy Kalman filter incorporating the generalized data
packet drop (DM-MCKF-DPD) algorithm is provided to implement DSE with the
presence of both non-Gaussian noise pollution and packet drop. A sufficient
condition to ensure the convergence of the fixed-point iterative process of the
DM-MCKF-DPD algorithm is presented and the computational complexity of the
DM-MCKF-DPD algorithm is analyzed. Finally, the effectiveness and feasibility
of the proposed algorithms are verified by simulations
Sparse feature learning for image analysis in segmentation, classification, and disease diagnosis.
The success of machine learning algorithms generally depends on intermediate data representation, called features that disentangle the hidden factors of variation in data. Moreover, machine learning models are required to be generalized, in order to reduce the specificity or bias toward the training dataset. Unsupervised feature learning is useful in taking advantage of large amount of unlabeled data, which is available to capture these variations. However, learned features are required to capture variational patterns in data space. In this dissertation, unsupervised feature learning with sparsity is investigated for sparse and local feature extraction with application to lung segmentation, interpretable deep models, and Alzheimer\u27s disease classification. Nonnegative Matrix Factorization, Autoencoder and 3D Convolutional Autoencoder are used as architectures or models for unsupervised feature learning. They are investigated along with nonnegativity, sparsity and part-based representation constraints for generalized and transferable feature extraction
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