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In stochastic estimation problems, we aim to estimate an unknown state of interest given a set of measurements received from noisy sensory devices, such as radar, light detection and ranging, etc. A common model of the measurements’ random error is white Gaussian noise. This noise model is usually used to derive an estimator of the unknown state; accordingly, the state is described as a random variable with a Gaussian density. In some applications, the unknown state is known to take only positive values, e.g., estimating size or dimension. In such a case, the classical approaches based on Gaussian densities might fail, i.e., produce a negative value. In this article, a recursive Bayesian filter is proposed based on modeling the unknown state as Wishart distributed random matrix. This model ensures that the probability densities of the random variables are restricted to positive real values, even though the measurement’s noise is still modeled as white Gaussian noise. The feasibility of the proposed Bayesian–Wishart filter is demonstrated within the framework of extended target tracking. A target contour measurement model of a symmetric star-convex shape is presented and integrated in the proposed filter to track the target’s extent. [ABSTRACT FROM AUTHOR]

This article presents an image-based radar detector, named neighborhood difference order statistics (NDOS) detector. Different from the classic likelihood ratio detector, the proposed detector treats the echo spectrum as an image and determines the existence of a target by comparing the difference between the test cell and its adjacent cells with a threshold. The closed-form expressions of probabilities of detection and false alarm are derived under Gaussian noise background and Swerling I target model. It is proved that the detection performance of the proposed detector approaches the optimal likelihood ratio detector when the homogenous noise power is known. When the noise power is unknown, we modify the detector into cell-averaging (CA) NDOS detector by estimating the noise power. Analytical derivations show that the CA-NDOS detector holds the constant false alarm rate (CFAR) property. Moreover, CA-NDOS detector possesses a better detection performance compared with two typical CFAR algorithms under the condition of typical reference window size. [ABSTRACT FROM AUTHOR]

This article proposes two algorithms for the localization or motion analysis of a moving target, by using a number of stationed sensors observing the frequency of the emitted target signal. This is a challenging problem due to the complicated non-linear relation between the target location parameters and the observations. The first algorithm is closed-form through algebraic evaluation that is computationally efficient. The second is derived from semidefinite programming by relaxationthat is robust against noise. The situations of having target frequency known but erroneous and completely unknown are investigated, as well as the presence of sensor position errors. Sequential algorithms for motion analysis are proposed in addition to the two for reducing latency and complexity. The algorithms are analyzed and their performance is able to achieve the CRLB accuracy under Gaussian noise. [ABSTRACT FROM AUTHOR]

Many high-resolution maritime radars for sea-surface small target detection transmit pulses with large time-bandwidth-products to increase transmitter power and to use the pulse compression technique for high range resolution. On the sea surface, besides small targets of interest, there exist many uninteresting large objects such as big ships, reefs, and offshore platforms for oil production. Large objects have too strong radar returns and their range sidelobes often mask wide range intervals. In these range intervals, masked small targets are difficult to be detected directly. In this article, a complete scheme is proposed to find small targets masked by range sidelobes of large objects in high-resolution maritime radars. In this scheme, high-precision reconstruction of radar returns of large objects is crucial because tiny reconstruction errors are enough to mask weak returns of small targets of interest. An algorithm using the linear programming and oversampled high-resolution range profile model of a large object is given to obtain high-precision reconstruction of the strong radar returns of a large object. From residual radar returns removed the reconstructed strong returns, a near-optimal coherent detector under sea clutter modeled by the spherically invariant random vector with inverse Gamma texture plus colored Gaussian noise is constructed to find masked small targets. Simulated experiments and real measured radar data are used to verify the effectiveness of the proposed scheme and method and reveal their limitations. The results show that the proposed scheme and method are all effective unless small targets fall into the range mainlobe regions of large objects. [ABSTRACT FROM AUTHOR]

This article studies the problem of radar detection of correlated gamma-fluctuating targets in the presence of clutter described by compound models with correlated speckle. It models signals from a pulsed radar with pulse-to-pulse correlation of components of the range-gated returns from a target with added thermal noise and compound Gaussian clutter. If the correlation is not accounted for in a radar model, the required signal-to-interference ratio as predicted by the model for a given probability of detection will be incorrect, resulting in overestimated performance. Although more generally applicable, the focus of this article is on airborne maritime radar systems. Hence, $K$ -distributed sea clutter is used as the main example. Detection via square-law noncoherent pulse integration is formulated in a way that accommodates arbitrary partial correlation for both target radar cross section (RCS) and clutter speckle. The obstacle to including this degree of generality in previous work was the fact that Swerling’s original characterization of the standard RCS fluctuation classes based on specifying distributions for the power is not sufficient to accommodate inclusion of both sources of correlated returns (i.e., target and clutter speckle) in the case of gamma-fluctuating targets. An extension of the model is required at the quadrature component (i.e., voltage) level, as phase relationships can no longer be neglected. This is addressed in the present article, which not only postulates an extended model, but also demonstrates how to efficiently compute it, with and without a number of simplifying approximation schemes within the framework of the saddle-point technique. [ABSTRACT FROM AUTHOR]

Crowd management is an essential task to ensure the safety and smoothness of any event. Using novel technologies, including surveillance cameras, drones, and the communication techniques between security agents, the control of the crowd has become easier. Yet, the usage of such techniques is still not effective. This article presents an approach for crowd counting from drones' data. The proposed method exploits the dilated and scaled neural networks for feature extraction and density crowd estimation. A new dataset named ViseDrone2020 is used for training and testing of the proposed method. In order to compare the proposed method, we implemented 10 state-of-the-art methods and trained it on this dataset. The experiments show that the proposed model is more efficient for crowd counting compared with the implemented methods. Also, some of these methods give relatively accurate results in terms of estimated crowd numbers as well as the quality of estimated density maps. The proposed model was further evaluated on nondrone datasets, namely, UCF_QNRF, UCF_CC_50, and shanghaiTech_(A, B), which produced satisfying results for all the datasets. In addition, the proposed method was tested on noisy images where Gaussian noise and salt-and-pepper noise was applied to all the images of the dataset with a noise density of 0.02. The analysis showed that the quality of the density map as well as the quantity of the crowd count estimation is comparatively better than other existing methods without the presence of noise. After the acceptance, the code of the proposed method as well as 10 implemented methods will be available at: [Online]. Available: https://github.com/elharroussomar/Crowd-Conting-on-Drone-Data. [ABSTRACT FROM AUTHOR]

A new approach to exploit nonlinear reradiation of electronic circuits under test (ECUT) for classification of electronic devices using harmonic radar is proposed in this article. Unlike prior approaches, we develop a linear model to relate the measurements to the unknown parameters representing the nonlinear characteristics of the ECUT. Each nonlinear circuit under test has a distinguishable response to a power-varying single-tone or two-tone incident wave. As a result, each ECUT possesses a unique unknown deterministic vector of parameters derived from the proposed linear model. We use maximum likelihood estimator in the presence of complex white Gaussian noise based on the newly constructed linear model. The statistical features of the normalized estimated vectors of parameters are employed as distinguishing factors in classification of different nonlinear electronic devices using K-nearest neighbors classification method. Simulation results show that the proposed method of linear model with power-swept signals and estimated vectors of parameters successfully classifies nonlinear devices. [ABSTRACT FROM AUTHOR]

In this article, an algebraic solution for localizing a moving target using a nonstationary multistatic radar system is proposed. By utilizing the quadruple hybrid measurement set, which consists of time delay, Doppler shift, angle of arrival, and angle rate measurements, the proposed method can estimate the target position and velocity vectors via a simple one-stage weighted least squares estimator without the need for introduction of the nuisance parameters, which enables us to locate the target with the minimum number of antennas. The proposed estimator takes the uncertainty concerned with the position and velocity of the antennas in its design. The proposed estimator is shown to be approximately unbiased and efficient under small Gaussian noise level assumption, that is, the estimator achieves the smallest possible variance (the Cramér–Rao lower bound) among all other unbiased estimators. Numerical simulations demonstrate substantial performance improvement of the proposed estimator over the state-of-the-art methods presented for moving target localization. [ABSTRACT FROM AUTHOR]

This article addresses the multistatic localization of an object by exploiting the direct-blast and object-reflected signals, where the transmitter location is not known and only the sensor positions are available. We study the cases that either the object or the transmitter is moving, in a noncooperative environment at which unknown offsets are present in the time delay and Doppler shift measurements. We analyze thoroughly by the Cramer–Rao lower bound (CRLB) if the direct-blast signals are useful in increasing the object localization performance, albeit doing so introduces extra parameters of the transmitter for estimation. Performance comparison with the fully dynamic moving object and moving transmitter modeling is established. Algebraic solutions are derived for the localization problem and analysis is made to validate that the proposed estimators are able to reach the CRLB performance under Gaussian noise over the small error region. [ABSTRACT FROM AUTHOR]

It is always an important problem to recover sparse signals from observations corrupted by Gaussian noise and has been extensively investigated. In high-resolution maritime surveillance radars working at scan mode, ship classification, and recognition need to recover high-resolution range profiles (HRRPs) of ships from radar returns of several pulses with severe range sidelobe effect. Multipulse synthetic data by the aid of ship Doppler information are complex sparse signals corrupted by non-Gaussian correlated interference. In this article, a sparse recovery via iterative minimization (SRIM) method is proposed to estimate complex HRRPs of ships from multipulse synthetic data. The SRIM method adapts the non-Gaussianity nature of the interference in the multipulse synthetic data and ship complex HRRPs are modeled by the random sequences of the biparametric generalized Gaussian distributions (GGDs) (0 < p ≤ 1). In the SRIM method, the parameters of the GGD model are iteratively searched by the minimal criterion of the Kolmogorov–Smirnov distance of the residue and the interference model. The SRIM method is compared with the recent linear-programming-based method and the classic sparse learning via iterative minimization (SLIM) method by using simulated and measured radar data and the results show that the SRIM method obtains the better performance. [ABSTRACT FROM AUTHOR]