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In robotics, non-linear least squares estimation is a common technique for simultaneous localization and mapping. One of the remaining challenges are measurement outliers leading to inconsistency or even divergence within the optimization process. Recently, several approaches for robust state estimation dealing with outliers inside the optimization back-end were presented, but all of them include...
In this paper, we propose an approach to distributed localization and motion control of unicycle mobile agents for the target circumnavigation problem. The bearing angle measurement-based localization performs when not all the agents get access to the target, but its performance is decided by the motion behaviors, and vice versa. Therefore we propose a coupled framework where we estimate the relative...
One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly estimate the blur kernel and the latent image. While several successful MAP based methods have been proposed, there has been much controversy and confusion about their convergence, because sparsity priors have been shown...
In this paper, the problem of echo cancellation in long acoustic impulse responses (AIRs) is highlighted. Three of the mostly-used recent NLMS-based sparse adaptive filtering algorithms are presented; and their performances in the context of acoustic echo cancellation (AEC) are studied and compared. The algorithms of interest include the improved proportionate normalized least mean square (IPNLMS),...
This paper presents an Unscented Kalman filter (UKF) based algorithm for estimating available bandwidth in a space Disruption-Tolerant Network (DTN) link. In the proposed algorithm, an UKF model is proposed for accurately tracking available bandwidth of bundle delivery over a space link. In particular, the proposed UKF will iteratively filter a serial of noised measurements on successive time intervals,...
In this work a system for the estimation of the forces (both longitudinal and lateral) exerted between the tires and the road is presented. Starting from two of the most commonly used descriptions of the vehicle dynamics, the single-corner and the single-track models, a system composed of Sub-Optimal Second Order Sliding Mode observers in a cascade structure plus an adaptive element is developed and...
In this paper, we present a novel image reconstruction algorithm for positron emission tomography(PET). Almost all of existing reconstruction approaches assume that the measurement model for PET is linear equation with Gaussian white noise or energy-bounded noise, which only approximates the emission and detection of PET very roughly. In fact, the real situation is much more complicated than the one...
This paper considers the problem of decentralized, cooperative, and dynamic self-localization in wireless sensor networks. In particular, we are interested in a restrictive but very realistic scenario where few anchors are deployed and each anchor whose location is priori known may only communicate with very few agents (e.g. just one agent) whose location is unknown and to-be-estimated. The lack of...
The paper proposes iterative data-driven generalized minimum variance (GMV) regulatory control via L2 — regularization. The proposed approach reformulates the derivation of the GMV regulatory control as a L2-regularized optimization problem, and employs an iterative design approach that repeats the same routine as the one-shot GMV regulatory control via L2-regularization. The L2-regularization assures...
Like other divergences, Jeffrey's divergence (JD) is used for change detection, for model comparison, etc. Recently, a great deal of interest has been paid to this symmetric version of the Kullback-Leibler (KL) divergence. This led to analytical expressions of the JD between autoregressive (AR) processes, moving-average (MA) processes, either noise-free or disturbed by additive white noises, as well...
This paper presents a new model order selection technique for signal processing applications related to source localization or subspace orthogonal projection techniques in large dimensional regime (Random Matrix Theory) when the noise environment is Complex Elliptically Symmetric (CES) distributed, with unknown scatter matrix. The proposed method consists first in estimating the Toeplitz structure...
Noise pollution has a large negative influence on the health of humans, especially in case of long-term exposure. Various passive hearing protection approaches are available. However, they often lack good protection against low frequency noise. For these applications, the principle of Active Noise Cancellation (ANC) offers a promising supplement. It relies on anti-phase compensation of the noise signal...
Using atomic force microscopy (AFM) for studying soft, biological material has become increasingly popular in recent years. New approaches allow the use of recursive least squares estimation to identify the viscoelastic properties of a sample in AFM. As long as the regressor vector is persistently exciting (PE), exponential convergence of the parameters to be identified can be guaranteed. However,...
This paper presents novel constrained consensus least mean square (cLMS) algorithms with adjustable constraints that can improve the learning performance of distributed estimation problems in sensor networks by exploiting the spatial diversity of the estimates. For the first algorithm, the constraint vectors are adjusted by combining the components of the estimate orthogonal to its neighbor estimates...
Quaternion-valued adaptive filters based on the mean square error (MSE) criterion have been extensively studied in recent years. However, the MSE cost function has only one degree of freedom, and to circumvent this problem, we propose another criterion which enables separate control of the magnitude and phase. Next, a quaternion least mean magnitude phase (QLMMP) filtering algorithm is introduced...
Digital predistortion (DPD) is an effective power amplifier (PA) linearization technique improving the system energy efficiency. At this point, real-time DPD adaptation is still an open issue due to the high computational complexity during the coefficients estimation procedure. Online censoring approach, which is effective in reducing the redundant data samples, can be applied in the DPD coefficients...
Most of the times Wireless Sensor Networks (WSNs) operate in hostile and unattended network environments, which makes the nodes vulnerable to node misbehaviour attacks. The applicability of conventional security methods, such as authentication, encryption and cryptography, to counter node misbehaviour attacks is debatable due to their incapability and involved cost. Trust aware secure mechanisms provide...
In this paper, a least squares based on two-step update identification algorithm is established for the Wiener system by introducing a relaxation factor, which controls the relative importance of the two estimation parts. In addition, the convergence performance of the proposed LS-TSU algorithm is then analyzed. It is shown by a numerical example that if the weighting factor is appropriately chosen,...
This paper introduces alternating direction method of multipliers (ADMM) to decrease the computational workload of DOA estimation which uses the framework of compressive sensing, i.e., Basis Pursuit De-Noising (BPDN). BPDN transforms the DOA estimation problem to an optimization problem. And interior-point method (IPM) is traditionally used to solve this optimization problem. Though IPM can obtain...
This paper proposes a Newton extremum seeking algorithm based accurate global task coordinate frame (Newton-AGTCF) for precision contouring motion control of biaxial systems under complicated free-form contouring tasks. Specifically, a cost function is defined based on the reference contour and the current position. The point on the reference contour, where the minimal value of the cost function can...
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