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This paper proposes using the sparse-recovery (SR) based 2-D multiple-signal classification (MUSIC) to enhance the multi-target detection capability of high-frequency surface wave radars (HFSWRs). Usually, for wide-beam HFSWRs, target detection is first conducted in the range-Doppler spectrum; bearings are then estimated by super-resolution methods, such as MUSIC. Unfortunately, this approach can...
The MUSIC(MUltiple SIgnal Classification) method with high resolution is typically used to estimate far-field source bearing. When the source is located in the near-field, it can be applied by compensating the time-delay difference and the magnitude attenuation using spherical wave rules. However, it has a problem with complex calculations, such as eigen-decomposition of a matrix. In this paper, we...
A technique is presented for estimating the covariance matrix due to underwater ambient noise by enforcing frequency- and angle-smoothness on the power-angle spectrum underlying an observed sample covariance matrix. This smoothing is achieved through a combination of least-squares fitting procedures and explicit suppression of contributions from discrete broadband emitters. The process preserves contributions...
Methods based on order statistics are often used in finance, quality control, data and signal processing, especially when signals of interest are immersed in impulsive noise. These allow to include rank information by increasing the dimension of the problem. In large dimension problems, we are usually required to know only the second order statistics. In this article we use a rank-one quadratic measurement...
Direction of arrival (DOA) estimation of wideband signals is considered of great importance in array signal processing. In order to estimate both azimuth and elevation angles with good performance, a spherical array is used to capture the space signal information. Considering the property of spherical harmonic function, we propose a unitary transformation and two-step smoothing to turn the complex-valued...
Kalman Filters are utilised for filtering and estimation in a large set of application. Here, this methodology is utilised for trajectory estimation of an underwater robot. In this work, three Kalman Filter methods are proposed for trajectory estimation. There are: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Central Difference Kalman Filter (CDKF). Simulation results are presented...
Linear Discriminant Analysis (LDA) is widely-used for supervised dimension reduction and linear classification. Classical LDA, however, suffers from the ill-posed estimation problem on data with high dimension and low sample size (HDLSS). To cope with this problem, in this paper, we propose an Adaptive Wishart Discriminant Analysis (AWDA) for classification, that makes predictions in an ensemble way...
Sparse Discriminant Analysis (SDA) has been widely used to improve the performance of classical Fisher's Linear Discriminant Analysis in supervised metric learning, feature selection and classification. With the increasing needs of distributed data collection, storage and processing, enabling the Sparse Discriminant Learning to embrace the Multi-Party distributed computing environments becomes an...
We consider tracking of a target with elliptical nonlinear constraints on its motion dynamics. The state estimates are generated by sensors and sent over long-haul links to a remote fusion center for fusion. We show that the constraints can be projected onto the known ellipse and hence incorporated into the estimation and fusion process. In particular, two methods based on (i) direct connection to...
The DOA estimation problem for wideband signals has attracted much attention in the past years, and how to utilize and derive the common DOA information among frequency bins is the essential question. We address the wideband DOA estimation problem in this paper, and to solve this problem we propose a joint sparse Bayesian learning algorithm based on the sparse signal representation (SSR) of the covariance...
The under-determined direction of arrival (DOA) estimation problem for a mixture of circular and non-circular signals is studied in the context of sparse arrays and a novel compressive sensing based DOA estimation algorithm is proposed. Compared to a direct application of existing compressive sensing based DOA estimation algorithm, the new one can make a more effective use of the degree of freedoms...
In this paper, we present an extended Tobit Kalman filter that deals with fault detection problem in nonlinear systems with missing measurements and censored data. The missing measurements randomly occurring are regulated by individual random variables whose probability distributions are on the interval [0,1]. The censored data are characterized by the Tobit measurement model. The Tobit Kalman filter...
Lithium Iron Phosphate (LiFePO4) batteries have obtained extensive interests for the high energy density, little contamination, and ready availability. To enhance the compatibility of the batteries in electrical systems, the accurate estimation of the state of charge (SOC) is remarkably significant. Conventionally, Kalman filter algorithm and its derivations can be utilized for SOC estimation. To...
The autonomous navigation of two satellites based on relative position measurements is studied in this paper. The state dependent Riccati equation filtering (SDREF) is used for orbit determination. The difference between SDREF and EKF in computation and model approximation is compared and the influence of eccentricity to the accuracy of navigation with SDREF is studied. The relationship between eccentricity...
The paper utilizes a novel battery model based on the electrical features of LiFePO4 battery, because Kalman filter algorithm(KF) is largely dependent on system model. Measurements of battery state are easily disturbed by colored noise which is high relevance in working condition, and the paper studies that the system noise satisfy one-order AR model. The paper proposes an adaptive extended Kalman...
In this paper, a matrix pencil characteristic equation-based source number estimator is proposed. An enhanced matrix is defined through partition-and-stacking process by the original data on the uniform linear array, and its covariance matrix is computed, which is proved to be a toeplitz conjugate symmetry matrix. The covariance matrix elements are aligned to from a vector. A measurement matrix is...
This paper presents a new two-step method to estimate the ratios of the moment of inertia, the direction of the angular momentum in inertial frame, the attitude and angular velocity of the space noncooperative tumbling target, based on the measured relative attitude from the servicing spacecraft. At the beginning, the target's body frame should be determined based on its geometrical shapes. In the...
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...
In this paper, we consider the direction-of-arrivals (DOAs) estimation of coherent narrowband signals impinging on an arbitrary linear array in a computational efficient way. A new interpolation transform based modified Capon beam-forming method is proposed without eigendecomposition, where the arbitrary linear array is transformed to a virtual uniform linear array (ULA) by utilizing the interpolation...
We propose a likelihood test for a covariance estimated from sample data which is used to determine the number of narrow band source signals. This Minimum Description Length (MDL) estimator is shown to be robust against deviations from the assumption of equal noise level across the array. A number of source Direction-Of-Arrivals (DOA) greater than the number of physical array elements is of interest...
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