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Due to continuous and unplanned urbanization, biases and probability of occurrences of non-line-of-sight (NLOS) errors can be drastically enlarged in macro-cellular smart urban environments. This paper presents a new robust estimation approach to mobile tracking improvements based on adequately tackling NLOS errors. To cover the dynamics of a mobile station, we cast a wireless localization problem...
The robustness of adaptive beamforming is relate to the input signal-to-noise ratio (SNR). In order to further improve the performance of input SNR estimation, a modified method of input SNR estimation for robust adaptive beamforming is proposed. Comparatively accurate value of the input SNR can be obtained by the proposed method, especially when the input SNR exceeds 0 dB. When the proposed method...
Non-linear methods are usually used to analyze and process random sequences with outliers or in presence of impulsive noise. One of these methods is based on order statistics, which includes rank information by increasing the problem size. In this article we use a rank one quadratic measurement model based on sketches and apply it to order statistics. We introduce a method to estimate the correlation...
Both state propagation and sensor measurements are often corrupted by unmodeled non-Gaussian or heavy-tailed noise. Without dealing with such outliers, the accuracy of a estimator significantly degrades, and control systems that rely on high-quality estimation lose stability. To estimate the states of dynamic systems in which both types of outliers occur, we propose a novel approach that combines...
This paper proposes a new filter design optimized for GNSS attitude determination applications using low cost devices. The filter contains all relevant constraints and observations, including double differenced carrier phase and code observations along with MEMS-IMU observations. Benefited from the unified treatment, the priori information is well used during the resolution of double differenced GNSS...
Person re-identification (ReID) stands for the task of determining the co-occurrence of individuals across a network of cameras with disjoint viewfields. The relevant literature documents a plausible number of contributions so far. KISS metric learning is an effective ReID method. However, as reported in the existing works, KISS metric learning is sensitive to the feature dimensionality and can not...
This paper adresses the problem of simultaneously estimating the state and the fault of nonlinear discrete-time stochastic systems in light of the unknown input filtering framework. The fault and unknown disturbances which may cause great estimate errors and even divergence of conventional filters, affect both the system state and the measurements. Inspired by the robust two stage Kalman filter for...
An information-theoretic approach is described to estimate the determinant of the covariance matrix of a random vector sequence (a common task in a wide range of estimation and detection problems in signal processing for communications). The method is based on a prior entropy-based processing of the data using kernels and offers robustness against small-entropy contamination. The trade-off between...
This paper firstly analyzes the shortcoming of a self-organizing incremental neural network (SOINN), then proposes a novel online similarity metric and online adaptive kernel density estimator to handle 2 basic problems of unsupervised learning: clustering and density estimation. Our approach is an extension of the standard Gaussian process, online density estimator and SOINN; not only does it fully...
In the last decade, monocular simultaneous localization and mapping (mono-SLAM) has appeared as another alternative for pose estimation, but this last gives a localization up to scale, and suffers from scale drift due to the difficulty of depth evaluation; however, several approaches had been tackled to recover the scale and take off the ambiguity. Both methods were designed to get the accurate scale...
In this work, well known Sigma Point Kalman Filters (SPKFs); namely Unscented Kalman filter (UKF), the Cubature Kalman filter (CKF), and the Central Differences Kalman filter (CDKF) will be combined to the Smooth Variable Structure Filter (SVSF), in order to create stable and robust algorithms that can be applied to highly non-linear systems. The proposed algorithms will be applied into 4-DOF robotic...
The purpose of this paper is to derive new asymptotic properties of the robust adaptive normalized matched filter (ANMF). More precisely, the ANMF built with Tyler estimator (TyE-ANMF) is analyzed under the framework of complex elliptically symmetric (CES) distributions. We show that the distribution of TyE-ANMF can be accurately approximated by the well-known distribution of the ANMF built with the...
Regularized Tyler Estimator's (RTE) have raised attention over the past years due to their attractive performance over a wide range of noise distributions and their natural robustness to outliers. Developing adaptive methods for the selection of the regularisation parameter α is currently an active topic of research. Indeed, the bias-performance compromise of RTEs highly depends on the considered...
In this paper, we consider robust direction-of-arrival (DOA) estimation for an array that contains mis-calibrated sensors with unknown gain and phase uncertainties. We develop two robust DOA estimation algorithms based on the maximum correntropy criterion (MCC). In the first algorithm, adaptively optimized weighting factors are obtained and applied to each sensor to effectively mitigate the effect...
An effective method is proposed to estimate the desired-signal (S) subspace by the intersection between the signal-plus-interference (SI) subspace and a reference space covering the angular region where the desired signal is located. The estimated S subspace is robust to steering vector mismatch and overestimation of the SI subspace, capable of detecting the relative strength of the desired signal...
In this paper, we consider the problem of robustly estimating a structured covariance matrix (CM). Specifically, we focus on CM structures that involve Kronecker products of low rank matrices, which often arise in the context of array processing (e.g. in MIMO-Radar, COLD array, and STAP). To tackle this problem, we derive a new Constrained Tyler's Estimators (CTE), which is defined as the minimizer...
Every spectrum sensing technique aim at improving network throughput of Cognitive Radio Networks (CRNs) at reduced computational complexity. Recent wideband spectrum sensing schemes in literature suggest performing spectrum sensing over narrow subbands sequentially using Energy Detection (ED) or other narrowband techniques. Eigen Value Decomposition (EVD) of covariance matrix is generally used to...
The minimum variance distortionless response (MVDR) beamformer is known to be sensitive to steering vector error, especially when the desired signal is present in the training snapshots. The robust capon beamformer (RCB) can deal with this problem, but is computational expensive. A modification of RCB is presented utilizing the low-rank property of the steering matrix which consists of the steering...
A new robust adaptive beamforming based on phase extraction is proposed in this paper. The steering vector (SV) is firstly estimated by iterative procedure, and then the angle sector of the desired signal can be determined by using the phase information of the estimated SV. By using the estimated angle sector, the interference-plus-noise covariance matrix can be calculated by Capon spectral integration...
Although the minimum variance (MV) beamformer can provide enhancement in both resolution and contrast of ultrasound images when compared with conventional delay-and-sum (DAS) beamforming, its clinical application is limited by its sensitivity to phase aberrations. Several robust MV beamformers have been proposed, but present limitations when faced with second order phase aberration. Additionally,...
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