Frequency Of Arrival Research Articles

Headway distribution for manually operated tollbooths in India in mixed traffic conditions

This paper presents a vehicle-type-specific headway distribution analysis at toll plazas under mixed traffic conditions. The aim of the paper is to provide headway distribution models that may be used for traffic flow analysis and traffic management at tollbooths. Seven classes of vehicle and a total of 49 combinations of leader–follower vehicles were considered, out of which 11 combinations were considered for studying the headway distribution model based on the frequency of arrival of such vehicle combinations at tollbooths. The results showed that vehicle classes requiring a headway of 5–25 s followed a log-normal distribution whereas, for higher headway ranges of 15–40 s, the generalised extreme value showed the best fit. For the small car category, the Wakeby distribution showed a better result than conventional distributions. The findings of this study may be useful for developing toll plaza microscopic simulation models, which could be used for assessing and improving the performance and efficiency of tollbooths.

A Closed-Form Location Algorithm Without Auxiliary Variables for Moving Target in Noncoherent Multiple-Input and Multiple-Output Radar System

This paper focuses on the moving target localization and velocity measurement in incoherent centralized multiple-input and multiple-output (MIMO) radar systems with widely separated antennas in 3D space. In this paper, we assume that parameters such as time-of-arrival (TOA), frequency-of-arrival (FOA), azimuth angles and elevation angles have already been measured. With these measurements, a final closed-form solution of the target position and velocity can be obtained via the weighted least squares (WLS) method. When the target is located in the far field, due to poor system observability, a first-order Taylor expansion based on the WLS solution is necessary to obtain a more accurate and unbiased solution. Unlike the preceding papers which were based on the two-stage weighted least squares (2SWLS) method [1]-[3], in this paper, the angle information is introduced into the time delay equations and the Doppler frequency equations, so that the intermediate variables in the estimator can be eliminated [4], [5]. Meanwhile, the time delay equations and the Doppler equations are transformed into linear equations only related to the position and the speed of the target. This method, unlike 2SWLS-based methods [1]-[3], does not introduce auxiliary variables, so it does not require the decorrelation procedure. Simulation results show that the root mean-square error (RMSE) of position and velocity can reach Cramer-Rao lower bound (CRLB) when the noise is at a moderate level before the thresholding effect occurs.

A bias‐reduced solution for target localization with illuminator of opportunity passive radar

SummaryAn improved solution for target localization on the basis of the realistic distance‐dependent noises in illuminator of opportunity passive radar and an algebraic reduction method of the bias, which exists in the two‐stage weighted least squares (TSWLS) method is proposed. The classic TSWLS and its improved solutions have great localization performances just on the basis of two approximations; that is, setting the noise to a constant and ignoring the high‐order terms of measurement noises. It is these two approximations that lead to a suboptimal solution with bias. The bias of TSWLS has a significant influence on the target localization in illuminator of opportunity passive radar that has lower measurement accuracy and higher noises than common radars. Therefore, this paper starts by taking into consideration the realistic distance‐dependent characteristics of time‐of‐arrival (TOA) and frequency‐of‐arrival (FOA) noises and improving TSWLS method. Then, the bias of the improved TSWLS method is analyzed and an algebraic bias‐reduced solution on the basis of weighted least squares (WLS) is developed. Numerical simulations demonstrate that, compared with the existing methods, the proposed bias‐reduced solution attains the Cramer‐Rao lower bound (CRLB) better and reduces the bias effectively.

Position and Velocity Estimation Using TOA and FOA Based on Lagrange Programming Neural Network

This paper addresses the problem of estimating the position and velocity of a moving source utilizing the time-of-arrival (TOA) and frequency-of-arrival (FOA) measurements. Since the concerned estimation problem is highly non-linear and non-convex, we propose to utilize a novel neural circuit, namely the Lagrange programming neural network (LPNN) framework, to solve this problem. LPNN equips the abilities of fast convergence and the robustness of resisting high noise level, and thus these two advantages have drawn much attention for it recently. Since LPNN is able to solve the optimization problem with constraint, we first reformulate the original non-linear and non-convex maximum likelihood (ML) problem by introducing additional variables and constraints, and thus a neural network is built up based on the LPNN framework. Subsequently, the convergence and stability of the proposed neural network is mathematically proved and then verified by the results of numerical experiments. Different from the conventional numerical algorithms, the analog neural network can be utilized to fulfil the task of real-time calculation, especially when there are limited computation resources in some applications. The simulation results demonstrate that the proposed LPNN model equips the basic properties of convergence and stability, and also show the superior localization accuracy of the proposed method than other numerical algorithms.

A Closed-Form Solution for Moving Target Localization With Sphere Constraint

For moving target localization with sphere constraint, the existing methods are essentially iterative and extremely inefficient. Accordingly, based on the well-known two-stage weighted least squares (2SWLS) method, we propose a closed-form solution for moving target localization with sphere constraint. For its explicit expressions, this solution can easily and efficiently estimate the target’s position and velocity from the time-of-arrival (TOA) and frequency-of-arrival (FOA) measurements. Furthermore, the numerical simulations demonstrate that the proposed method is able to attain the constrained Cramer-Rao lower bound (CRLB) and superior to the existing methods.

Credit Scoring with Ego-Network Data

This article investigates a stochastic filtering problem whereby the bor-rower’s hidden credit quality is estimated using ego-network signals. The hidden credit quality process is modeled as a mean reverting Ornstein-Ulehnbeck process. The lender observes the borrower’s behavior modeled as a continuous time diffusion process. The drift of the diffusion process is driven by the hidden credit quality. At discrete fixed times, the lender gets ego-network signals from the borrower and the borrower’s direct friends. The observation filtration thus contains continuous time borrower data augmented with discrete time ego-network signals. Combining the continuous time observation data and ego-network information, we derive filter equations for the hidden process and the properties of the conditional variance. Further, we study the asymptotic properties of the conditional variance when the frequency of arrival of ego-network signals is increased.

Integración del internet de las cosas en los procesos logísticos de máquinas dispensadoras

Este artículo tiene como principal objetivo definir las condiciones en las que se desarrollan algunos procesos logísticos de las organizaciones en la integración de dispositivos en objetos cotidianos conectados a internet y que, por este medio, entregan información generadora de dinámicas diferentes a las tradicionales en cada una de las actividades que conforman la labor de abastecimiento, para garantizar la disponibilidad del producto en las condiciones que el cliente lo desea y, en este caso, en las actividades enmarcadas en el denominado vending: comercialización de productos como snacks, bebidas, artículos de aseo, medicamentos básicos, entre otros, a través de dispensadores electrónicos. La metodología usada está basada en el análisis de la bibliografía especializada en el tema del vending, el Internet de las Cosas y de las “cosas” que demandan constante nivel de abastecimiento de productos dadas las características de suministro permanente a los clientes por parte de estas, como los dispensadores de snacks, refrescos y de productos perecederos tales como frutas, productos frescos y productos lácteos, así como la consulta en fuentes primarias entre las que se encuentran mediciones sobre comportamiento de agotados y frecuencia de llegada de clientes en tres máquinas ubicadas en una entidad educativa. Igualmente, se evidencia la importancia de utilizar la tecnología para la recolección de la información pertinente, ya sea con la interacción de tecnologías blandas (software) y tecnologías duras o la utilización exclusiva de software, decisión en la que juega un papel preponderante el costo del producto.

Compressive <italic>k</italic>-Space Tomography

The coherent optical processing technique, referred to as k -space tomography, enables simultaneous detection of radio waves’ frequency and angle of arrival. As with the majority of signals, the spatial and spectral distribution of RF signals in the electromagnetic environment is sparse in nature. This inherent characteristic provides an ideal opportunity to merge this imaging modality with compressive sensing techniques. Compressive sensing has set a foundation for data acquisition of sparse signals from a small amount of data. By adapting specific aspects from this methodology, and applying them to k -space tomography, compressive k -space tomography is developed. Fundamental performance limits of this compressive sensing application are defined through numerical simulations, and these responses are subsequently utilized in experimental spatial-spectrum utilization mapping for impending 5G bands between 28 and 40 GHz. As a result, reconstruction data size and processing time are reduced consistently by a factor of over 100, without loss of information, for sufficiently sparse RF scenes.

Scalable antinoise-robustness estimation of frequency and direction of arrival based on the relaxed sparse array

In order to realize low-cost frequency and direction of arrival (DOA) estimation in the harsh temporal-spatial undersampling condition, this paper proposed a joint estimator with scalable antinoise robustness. Essentially, owing to that two basic problems are well solved, the proposed estimator concurrently achieves high array sparsity, low hardware complexity and scalable antinoise robustness. On one hand, in the sparse sensor arrangement, a relaxed coprime array was proposed, which is distinguished with low hardware cost (only $$L=3$$ sensors in low sampling rates are employed) and high array sparsity (the inter-element spacings are much greater than the Nyquist spacings). On the other hand, in the design of the frequency and DOA reconstruction algorithm, a series of techniques (including the reconstruction towards robustness in residue number system, spectrum correction and phase difference adjustment) are organically integrated, which endows the proposed estimator with scalable antinoise robustness. Numerical results confirmed the above advantages, which present the proposed joint estimator vast potentials in the radar, remote sensing and other passive sensing related applied fields.

Structural total least squares algorithm for locating multiple disjoint sources based on AOA/TOA/FOA in the presence of system error

Single-station passive localization technology avoids the complex time synchronization and information exchange between multiple observatories, and is increasingly important in electronic warfare. Based on a single moving station localization system, a new method with high localization precision and numerical stability is proposed when the measurements from multiple disjoint sources are subject to the same station position and velocity displacement. According to the available measurements including the angle-of-arrival (AOA), time-of-arrival (TOA), and frequency-of-arrival (FOA), the corresponding pseudo linear equations are deduced. Based on this, a structural total least squares (STLS) optimization model is developed and the inverse iteration algorithm is used to obtain the stationary target location. The localization performance of the STLS localization algorithm is derived, and it is strictly proved that the theoretical performance of the STLS method is consistent with that of the constrained total least squares method under first-order error analysis, both of which can achieve the Cramer-Rao lower bound accuracy. Simulation results show the validity of the theoretical derivation and superiority of the new algorithm.

Passive Three-Dimensional Spatial-Spectral Analysis Based on k-Space Tomography

A 2D distributed-aperture imaging system paired with a newly developed imaging modality, k-space tomography, is presented for detection of radio waves’ frequency and angle of arrival in azimuthal and elevation directions through coherent optical processing. This technique uses fiber-length dispersion in conjunction with a distributed antenna array to provide unique spatio-temporally encoded charge-coupled device (CCD)-captured interferograms, from which a computational tomographic reconstruction of the RF signal environment at newly allocated 5G bands can be obtained.

Photonics-Based Instantaneous Multi-Parameter Measurement of a Linear Frequency Modulation Microwave Signal

A novel approach for simultaneous and instantaneous measurement of the center frequency (CF), bandwidth (BW), pulse width (PW), pulse amplitude (PA), and time of arrival (TOA) of a linear frequency modulation (LFM) pulse signal is proposed and experimentally demonstrated. The approach is based on a linear amplitude comparison function (ACF) obtained by intensity modulation and first-order differentiated phase modulation, which are realized using a polarization modulator, two polarizers, and an optical linear filter. The linearity characteristic of the ACF ensures the ability of the proposed system to measure multiple parameters of an LFM pulse signal. In a proof-of-concept experiment, the measurement errors for CF, BW, PW, PA, and TOA of an LFM pulse are within ±0.3 GHz, ±0.4 GHz, ±120 ps, ±50 mV, and ±15 ps, respectively. Factors affecting the performance of the measurement system, such as the shape, slope, and resolution of the ACF, are discussed.

Characterization of gain and directivity of exponential horn receivers.

It is difficult and expensive to match the sensitivity of the most sensitive vertebrate ears with off-the-shelf microphones due to the self-noise of the sensor. The extremely small apertures of microelectromechanical microphones create options to use horn waveguides to amplify sound prior to transduction without resulting in an unacceptably narrow directivity. Substantial gain can be achieved at wavelengths larger than the horn. An analytical model of an exponential horn embedded in a rigid spherical housing was formulated to describe the gain relative to a free-field receiver as a function of frequency and angle of arrival. For waves incident on-axis, the analytical model provided an accurate estimate of gain at high frequencies as validated by experimental measurement. Numerical models, using the equivalent source method, can account for higher order modes and comprehensively describe the acoustic scattering within and around the horn for waves arriving from any direction. Results show the directivity of horn receivers were adequately described by the analytical model up to a critical wavelength, and the mechanisms of deviation in gain at high frequencies and large angles of arrival were identified.

Location algorithms for moving target in non‐coherent distributed multiple‐input multiple‐output radar systems

In this study, for the problem of estimating the location and speed of a moving target in the non-coherent multiple-input multiple-output (MIMO) radar systems with widely separated antennas, the authors propose two new methods, in which the parameters used are the joint of bearing, elevation, frequency-of-arrival (FOA) and time-of-arrival (TOA). The two proposed methods are based on non-coherent MIMO radar systems, but method 1 centralises all measuring parameters in one linear equation and processes together, while method 2 divides the measurements into several groups according to the different transmitter-receiver pairs. In this study, the authors assume that bearing, elevation, FOA and TOA parameters have already been measured by a preprocessing algorithm. For the both methods, an initial guess is acquired through a best linear unbiased estimator. Then for method 1, a more explicit solution can be acquired by employing a maximum likelihood estimator for decorrelation, while the method 2 applies maximum likelihood estimation of a first-order Taylor expansion for a better solution. The simulations show that these two methods are effective and both of them can attain to Cramer-Rao lower bound at sufficiently moderate noise conditions.

Towards Printable Natural Dielectric Cloaks via Inverse Scattering Techniques

The synthesis of non-magnetic 2D dielectric cloaks as proper solutions of an inverse scattering problem is addressed in this paper. Adopting the relevant integral formulation governing the scattering phenomena, analytic and numerical approaches are exploited to provide new insights on how frequency and direction of arrival of the incoming wave may influence the cloaking mechanism in terms of permittivity distribution within the cover region. In quasi-static (subwavelength) regime a solution is analytically derived in terms of homogeneous artificial dielectric cover with ε < ε0, which is found to be a necessary and sufficient condition for achieving omnidirectional cloaking. On the other hand, beyond quasi-static regime, the cloaking problem is addressed as an optimization task looking for only natural dielectric coatings with ε > ε0 able to hide the object for a given number of directions of the incident field. Simulated results confirm the validity of both analytic and numerical methodologies and allow to estimate effective bandwidths both in terms of frequency range and direction of arrival of the impinging field.

A Parameter Estimation Algorithm for Multiple Frequency-Hopping Signals Based on Sparse Bayesian Method

Parameter estimation and network sorting for noncooperative wideband frequency-hopping (FH) signals have been essential and challenging tasks, especially in the case with little or even no prior information at all. In this paper, we propose a nearly blind estimation approach to estimate signal parameters based on sparse Bayesian reconstruction. Taking the sparsity in the spatial frequency domain of multiple FH signals into account, we propose a sparse Bayesian algorithm to estimate the spatial frequency parameters. As a result, the frequency and direction of arrival (DOA) parameters can be obtained. In order to improve the accuracy of the estimation parameters, we employ morphological filter methods to further clean the data poisoned by the noise. Moreover, our method is applicable to the wideband signal models with little prior information. We also conduct extensive numerical simulations to verify the performance of our method. Notably, the proposed method works well even in low signal-to-noise ratio (SNR) environment.

Gain and directivity of exponential horn receivers

The self-noise of acoustical sensors limits their capacity to monitor extremely quiet environments and measure the subtle, adventitious cues that animals routinely rely upon. Although primarily used in sound production, horns also can amplify sound prior to transduction by a microphone. Given the small size of microelectromechanical microphones, substantial gain can be achieved at wavelengths larger than the horn. An analytical model of an exponential horn embedded in a rigid spherical housing was formulated to describe the gain relative to a free-field receiver as a function of frequency and angle of arrival. Through comparison with experiment and numerical models, the directivity of the horn receiver is shown to be adequately described by the analytical model up to a critical wavelength, beyond which physical assumptions are violated to some degree. Numerical models, based on the equivalent source method, describe the acoustic scattering within and around the horn and provide a means for identifying the...

Joint Frequency and Time Estimation Algorithms

In this paper, we present six subspace decomposition based methods for joint time of arrival (TOA) and frequency of arrival (FOA) estimation of multiple incident sources. These are LU-TLS, QR-TLS, direct TSQR-TLS, direct TSLU-TLS, parallel TSQR-TLS, and parallel TSLU-TLS. The direct and parallel TSQR/TSLU-TLS are recently developed methods in subspace decomposition and are employed in this work for time and frequency estimation. The proposed methods employ a pair of spatially separated sensors to receive multiple incident source signals. A data matrix is constructed in the form of a Hankel matrix from multiple snapshots of the received signal. The information of both TOA and FOA of multiple incident sources is extracted from the data matrix by applying LU/QR techniques (in the first set of the methods) and a tall skinny TSLU/TSQR factorization in the second set. The estimates of the TOA and FOA are obtained from the signal subspace by applying the total least squares (TLS) method. Simulation results are presented to assess the performance of the proposed methods. The effect of parametric variations on the performance has also been analyzed for all the proposed methods. Further, the computational times and complexities of the proposed methods are also computed and compared with each other.

An Improved Algebraic Solution for Moving Target Localization in Noncoherent MIMO Radar Systems

We propose an improved method for moving target localization with a noncoherent multiple-input multiple-output (MIMO) radar system having widely separated antennas. The method is based on the well-known two-stage weighted least squares (2SWLS) method, but in contrast to the recently proposed Group-2SWLS, it requires only one reference transmitter (or receiver) without grouping and combining. This change allows us to obtain a closed-form solution which is less likely to be degraded by bias. Furthermore, the proposed method can easily utilize not only time-of-arrival (TOA) and frequency-of-arrival (FOA) data but also time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) data. We also introduce new auxiliary variables for the purpose of numerical stability; our method using the auxiliary variables is shown to be numerically more stable than the Group-2SWLS, while attaining the Cramer–Rao lower bound (CRLB) at higher noise levels. The adoption of auxiliary variables requires no additional computations in contrast to the adoption of the concept of Turbo-2SWLS for the same purpose.

Адаптивный алгоритм оценки частоты вихреобразования в вихреакустических расходомерах

The article focuses on the development of vortex shedding frequency estimation algorithm. It shows the difficulties of the application of classical algorithms (moving average and median filtering) for whole measurement range of vortex sonic flowmeter. These algorithms are effective at the high flow region, where frequency of arrival of information about vortex frequency is high. However, at the low flow region where aforementioned frequency equals to tenths of hertz these algorithms are ineffective. A new adaptive algorithm is offered which can operate in a whole range of flowmeter measuring. It combines an ability to react immediately if a flow starts changing, and to provide stable measurements if the flow is stable. The simulation of the proposed algorithm is implemented and its advantages over the algorithms used in serial manufacture are shown. It is noted that the developed algorithm does not need in powerful computing facilities.