Radar Model Research Articles

Frequency Diverse Array Radar: New Results and Discrete Fourier Transform Based Beampattern

In the phased-array radar (PAR) signals from each antenna are transmitted at the same carrier frequency, which yields narrowly focused only angle dependent beampattern. In contrast, in the frequency-diverse-array (FDA) radar signals from antenna array are generally transmitted at linearly increasing frequencies that yields range, time, and angle dependent beampattern. Reported literature on FDA radar missed the contribution of path-differences in the signal model due to the antenna array elements, which may lead to misleading results. In this work, incorporating missed path-differences, the signal model of FDA radar is corrected. Using the corrected signal model, it is shown that the instantaneous beampattern depends on the number of transmit antenna and average beampattern depends on the product of frequency-offset and pulse-duration. Moreover, to illuminate the desired region-of-interest for longer dwell time, discrete-Fourier-transform based low-complexity algorithm is proposed. In contrast to the conventional FDA radar's 'S' shaped beampattern, the beampattern of the proposed algorithm changes linearly with range. Simulation results compare the performance of our proposed algorithm with the existing ones and show the superiority of our proposed algorithm.

A Drone Fleet-Borne SAR Model and Three-Dimensional Imaging Algorithm

Due to the advantages of low cost and flexible flight, small drones are showing tremendous application prospect in plenty of fields. Aiming at public area monitoring, a drone fleet-borne synthetic aperture radar (SAR) model is built in this paper. In this model, each radar is mounted on a small drone while illuminating the area of interest and the drones fly forward straightly in an array form. For practical feasibility, it is assumed that each radar works independently and simultaneously. To avoid interference, frequency-divided (FD) signals are adopted. Besides, bandwidth synthesizing is performed to improve the range resolution. Furthermore, 3D imaging is realized via combining the traditional 2D SAR imaging technique and the sensor array signal processing method. In specification, beamforming (BF) is adopted for the array direction compression. With flexible data acquisition model and fast imaging algorithm, 3D imaging using drone fleet borne SAR has great potential in real-time monitoring. The simulation experiments of point targets are performed and the imaging results validate the effectiveness of the proposed imaging model and 3D image formation algorithm.

Compressive sensing‐based super‐resolution DOA estimation for mechanical scanning radar

This study presents a novel super-resolution directions of arrival (DOA) estimation method for mechanical scanning radar by using the advanced compressive sensing algorithm. This method is implemented by constructing a signal model of the mechanical scanning radar through imitating the array radar signal model. Also then it established the compression relationship between the transmitted and received echo signals of the reader by sparsification technique of sending signals. Finally, the DOA estimation can be solved by employing advanced compressive sensing algorithm including basic pursuit (BP), orthogonal matching pursuit (OMP), regularised orthogonal matching pursuit algorithms and so on. Compared with the conventional super-resolution DOA estimation method such as multiple signal classification, the proposed method can distinguish two or more targets within one beamwidth more precisely. Compared with the earlier super-resolution DOA estimation method such as maximum likelihood, the proposed method can obtain more accurate and robust angle difference estimation and do not need to previously know the source number. Both simulated and experimental results show that the overall performance of the proposed DOA estimation is better than that of other methods.

Tracking Multiple Vehicles Using a Variational Radar Model

High-resolution radar sensors are able to resolve multiple detections per object and, therefore, provide valuable information for vehicle environment perception. For instance, multiple detections allow us to infer the size of an object or to measure the object’s motion more precisely. Yet, the increased amount of data raises the demands on tracking modules; measurement models that are able to process multiple detections for an object are necessary and measurement-to-object associations become more complex. This paper presents a new variational radar model for tracking vehicles using radar detections and demonstrates how this model can be incorporated into a random-finite-set-based multi-object filter. The measurement model is learned from actual data using variational Gaussian mixtures and avoids excessive manual engineering. In combination with the multi-object tracker, the entire process chain from raw measurements to the resulting tracks is formulated probabilistically. The presented approach is evaluated on experimental data, and it is demonstrated that the data-driven measurement model outperforms a manually designed model.

Modeling the Statistics of Microwave Radar Sea Clutter

One of the key requirements of a radar system is to detect targets against the background interference. This can be a challenging problem in the maritime domain when the sea moves in a complex manner and the characteristics are time-varying or nonstationary. Understanding the characteristics of sea clutter is therefore essential in developing effective and robust detection schemes. The first part of this tutorial provides details of the relevant statistical models from the literature which are typically used to characterize the sea clutter. Using these models enables us to represent clutter in different environments that may be difficult to observe in trials and to predict the performance of radars over a range of different conditions with more confidence. Future radar systems may well comprise transmitters and receivers which are separated by some distance. Analysis of bistatic sea clutter is therefore covered in the second part of this paper with a focus on how the statistics vary with different bistatic angles. The final part then looks at the application of radar models for clutter simulation and performance prediction for both coherent and non-coherent detection schemes.

Space‐time adaptive detection for bistatic MIMO system under covariance matrix persymmetry and symmetric spectrum

Space-time adaptive detection (STAD) is an important research aspect of the bistatic multiple-input multiple-output (MIMO) radar system. The signal and clutter space-time models of the bistatic MIMO radar are described in this study. The knowledge-aided (KA) transform, which utilises the structural properties of the target-free data and the sample covariance matrix, is applied to increase the independent secondary data fourfold in the STAD frame. The performance assessment of the KA–STAD detectors with generalised likelihood ratio test (GLRT), adaptive matched filter (AMF), and Rao rules is presented. The simulation experiments show that the detection and constant false alarm rate performances of the presented KA–GLRT, KA–AMF, and KA–Rao detectors outperform the GLRT, AMF, and Rao detectors for the bistatic MIMO radar system STAD frame in clutter background with limited secondary data.

Prediction of radar sea clutter based on LSTM

Radar sea clutter is the backscattering radar echo of rough sea surface, the research of radar sea clutter is of great significance to national defense construction and national economic development. Sea clutter prediction is also an important point in radar signal processing field. The traditional sea clutter prediction method has lower precision when predicting long-distance sea clutter data, and when the amount of data is large, the time is also lengthened, thereby reducing the efficiency of prediction. In this paper, a new method based on long short-term memory (LSTM) for predicting sea clutter at longer distances in the atmospheric duct environment using near-distance observations is proposed, the principle of LSTM network is introduced, and the factors affecting prediction accuracy are analyzed. The high precision prediction of radar sea clutter by LSTM network is realized. It provide the basis for further work on inversion problem of atmospheric ducts. It also has very important application value for studying the clutter suppression of the radar model and improving the target detection performance of the radar.

LFM-PC Radar Evaluation in Presence of Jamming using SystemVue

Pulse Compression (PC) radars become; now a day; are the most commonly used in a lot of military applications. This is due to its capability to resolve the problem of designing long range radar with excellent range resolution. Also, it provides additional processing gain that enhances radar detection capability. Moreover, Pulse compression is considered as an electronic counter countermeasures (ECCM) capability. It provides the radar with a good immunity against different jamming techniques. There are a lot of types of PC waveforms. Linear Frequency Modulated (LFM) signals are the most tolerant to high Doppler shifts. This is why LFM-PC radars are widely utilized with high speed targets. However, the literature lacks a simulation model to evaluate the detection performance of LFM-PC radar in presence of different noise jamming techniques. This paper introduces a typical simulation model for LFM-PC search radar using SystemVue. The simulated model allows the studying of the PC radar performance with different jamming techniques. Also, the optimum jamming technique; from electronic warfare point of view; is explored.

Forward Modeling of GPR Data by Unstaggered Finite Difference Frequency Domain (FDFD) Method: An Approach towards an Appropriate Numerical Scheme

Forward modeling of ground penetrating radar (GPR) is an important part to the inversion/modeling of the observed data. The aim of this study is to establish specific numerical schemes for forward modeling of GPR data by finite difference frequency domain (FDFD) method which were originally developed for seismic or finite difference time domain (FDTD) method. A total number of six modified and improved FDFD techniques have been used to discretize the two-dimensional (2D) transverse electric (TE)-mode scalar wave equation in order to find the suitable method for this. These techniques include five-point classical to nine-point mixed unstaggered-grid configurations. The numerical schemes for three unsplit perfectly matched layer (PML) for nine-point mixed unstaggered-grid configurations are also presented. The applicability of these techniques is tested by using the underground models of relative permittivity and conductivity for the two cases of homogeneous and 2-cross models. GPR shot gather data for these two models are also produced for this study. The relative reflection errors of the numerical schemes are also estimated for the homogeneous model to comprehend the appropriate method for the modeling. The algorithm for complex-frequency shifted PML (CFSPML) gives the least error in case of the forward modeling of the GPR data.

Масштабное физическое гидроакустическое моделирование РЛС с синтезированной апертурой малой дальности для БПЛА.

Масштабное физическое гидроакустическое моделирование РЛС с синтезированной апертурой малой дальности для БПЛА.

A road map to the detection performance of LFM-PC search radars with CFAR detectors

Pulse Compression (PC) radars are used to resolve the problem of designing long range radar systems with excellent range resolution. PC provides radar system with additional processing gain that enhances its detection capability. Among the different types of pulse compression waveforms, Linear Frequency Modulated (LFM) signals are the most tolerant to high Doppler shifts. This is why LFM-PC radars are widely utilized with high speed targets. However, the literature lacks a complete mathematical or simulation model to evaluate the its detection performance. This paper introduces a typical simulation model for search radar that is verified mathematically. The proposed model includes matched filter (MF), moving target detector (MTD), two different constant false alarm rate (CFAR) algorithms and LFM signal sidelobes on radar detection are to be considered. Two simulation models are introduced. Firstly, the Matlab simulation is introduced to verify the mathematical model and the theory. Secondly, the SystemVue simulation is applied to be used in the future work with the hardware in the loop facility.

Uncertainty Analysis in Dispersive and Lossy Media for Ground-Penetrating Radar Modeling

The uncertainty analysis (UA) in simulation results is essential when the acceptability of the results is considered. This letter introduces an improved intrusive generalized polynomial chaos expansion (gPCE) method for UA in ground-penetrating radar modeling. The uncertainty in simulation results induced by the uncertain parameters of two-dimensional dispersive and lossy media is quantified with the auxiliary differential equation finite-difference time-domain method combined with gPCE. The results from the new method are compared against the Monte Carlo method. The new method shows its considerable advantage in the computational expense and speed.

A Closed-Form Model for Long- and Short-Range Forward Scatter Radar Signals From Rectangular Conductive Targets

A closed-form model for the forward scatter radar (FSR) signal is presented with the goal to overcome the far-field (FF) limitation of the commonly used models. The proposed Fresnel closed-form (FCF) model, based on an approximation of the Helmholtz–Kirchhoff electromagnetic theory, correctly represents the received field of rectangular metallic targets crossing the baseline either in the short range or long range of the FSR transmitter and receiver. The FCF model outputs are validated by comparison with numerical results of a full-wave simulation tool in terms of received field amplitude and phase. The FCF model is exploited to assess the FSR detection performance loss, caused by the use of the FF models for some case studies of practical interest. Based on the proposed FCF model, we provide an analysis of the FSR ideal optimum performance achievable in these scenarios.

The innovation challenge in micro and small enterprises (MSE)

Purpose This study aims to measure the degree of innovation of micro and small industrial companies in the West and Southwest metropolitan regions of the city of São Paulo, through a survey with 203 firms in the metallurgy sector. Design/methodology/approach The research had a quantitative and descriptive focus and used as methodology the validated and international approach known as Innovation Radar. Findings The degree of innovation in these micro and small companies is low; thus, the authors could not characterize them as systemic innovators. Most of them are little innovative, although some were classified as occasional innovators. The dimensions organization, processes, presence, supply chain and added value were the least developed. Research limitations/implications To carry out similar studies in other Brazilian regions, to compare results and draw new conclusions, or even check if the degree of innovation present in micro-firms of these regions would not be even lower; to monitor the evolution of companies through a longitudinal study, to detect improvements in the degree of innovation; and to conduct a qualitative research that can deepen questions on the results of our study, such as the reasons why this type of company does not adopt innovative practices, or even the real suitability of the Innovation Radar model for micro and small enterprises (MSEs). We observed that some dimensions proved to be too sophisticated for these companies, such as R&D investments and the adoption of technological advances. Practical implications The study shows that the degree of innovation measured by the Innovation Radar is a useful and initial measure to check an innovative attitude in micro and small companies. It can also drive the actions that should be prioritized to stimulate the culture of innovation in SME. However, it does not allow to answer why this type of organization does not adopt innovative practices as a management attitude. Regarding its contribution, the authors expect that the paper may bring an awareness of managers and owners of micro and small companies for the need to foster innovative practices that can help increase the competitiveness and survival of this type of organization. Social implications In Brazil, despite the fact that MSEs represent 98 per cent of the existing companies, and are mainly responsible for job creation, their leaders have a low concern for innovative practices. Originality/value The study contributes to identify the degree of innovation of these firms, which comprise a representative and strategic segment of the city’s economy, by checking to what extent an innovative attitude is effectively present in this sector. The theoretical contribution of this study regards the appropriateness of mechanisms or methodologies created to measure the degree of innovation in large organizations. Dimensions such as technological platform, brand, innovative ambience, degree of organization or systematization of processes, which are frequently considered for companies in general, and especially for large ones, are not sufficient or, instead, too sophisticated to allow an effective measurement of the degree of innovation in MSE. Thus, this study provides information for designing more effective ways to evaluate the degree of innovation that take into account MSE’s specificities, which can be considered innovation efforts, such as simple process improvements, professional development of teams, and actions to seize ideas and opportunities, among others.

Relationship between Rainfall Variability and the Predictability of Radar Rainfall Nowcasting Models

Radar rainfall nowcasts are subject to many sources of uncertainty and these uncertainties change with the characteristics of a storm. The predictive skill of a radar rainfall nowcasting model can be difficult to understand as sometimes it appears to be perfect but at other times it is highly inaccurate. This hinders the decision making required for the early warning of natural hazards caused by rainfall. In this study we define radar spatial and temporal rainfall variability and relate them to the predictive skill of a nowcasting model. The short-term ensemble prediction system model is configured to predict 731 events with lead times of one, two, and three hours. The nowcasting skill is expressed in terms of six well-known indicators. The results show that the quality of radar rainfall nowcasts increases with the rainfall autocorrelation and decreases with the rainfall variability coefficient. The uncertainty of radar rainfall nowcasts also shows a positive connection with rainfall variability. In addition, the spatial variability is more important than the temporal variability. Based on these results, we recommend that the lead time for radar rainfall nowcasting models should change depending on the storm and that it should be determined according to the rainfall variability. Such measures could improve trust in the rainfall nowcast products that are used for hydrological and meteorological applications.

Sensor scheduling for electronic support to intercept beam‐agile radar

Modern electronically scanned-array radars can switch between beams in a matter of microseconds. This allows great latitude in constructing the beam schedule. From the point of view of an electronic support (ES) receiver, what are the implications? Repeated search tends to make the radar's beam schedule more regular and predictable. Can this be exploited by the receiver to intercept the radar more quickly? What can be done about strongly aperiodic beam schedules? In this study, the author proposes two opposing models for a beam-agile radar. One is a periodic model, accounting for radars with a predictable, repetitive search operation. The other is a stochastic model, in which the beam schedule is modelled by a continuous-time Markov chain, accounting for strongly aperiodic radars. Taking the published beam schedule of a hypothetical, agile radar, numerical experiments are conducted under two different assumptions about the ordering of beam dwells in each radar scan. Under the assumption that the dwells are sequentially ordered, it is found that the beam schedule appears periodic from the receiver's point of view, to a good approximation. On the other hand, when the radar dwells are randomly shuffled on each scan, the Markov-chain model proves useful in guiding receiver settings.

Modelling ground‐penetrating radar wave propagation using graphics processor unit parallel implementation of the symplectic Euler method

ABSTRACTInversion of ground‐penetrating radar signals requires accurate and efficient forward modelling. The symplectic Euler method promises good results when simulating ground‐penetrating radar wave propagation in substructures, but its computational efficiency is limited by the same Courant–Friedrichs–Lewy stability condition as the finite‐difference time‐domain method. A two‐dimensional graphics processor unit–accelerated parallel symplectic Euler algorithm is used to simulate ground‐penetrating radar wave propagation. We compared the reflection waveforms as well as the simulation time of the complex underground structure models simulated by the parallel symplectic Euler method with traditional finite‐difference time‐domain method. Results show that the parallel symplectic Euler algorithm achieves the same level of accuracy as the standard finite‐difference time‐domain method. Moreover, it significantly improves the computational efficiency, as the calculation speed is improved by more than 21 times. We verify the performance of the proposed algorithm through a map of the single‐track radar data for a three‐layered pavement model and a simulation wiggle map for a structural damage pavement model. This provides a theoretical basis for accurately interpreting ground‐penetrating radar detection data and efficient forward modelling for the next step of inversion imaging.

Polarisation‐space‐time adaptive processing for heterogeneous clutter suppression of airborne‐phased array radar

Conventional space-time adaptive processing (STAP) achieves clutter suppression by combining spatial signals and the temporal pulses. However, extreme slow-moving target cannot be effectively detected by space-time joint filtering in ambiguous and non-broadside clutter environment for airborne-phased array radar. By increasing the polarisation information in clutter suppression, improved moving target detection performance can be obtained by the polarisation-space-time adaptive processing (P-STAP). In this study, the performance of P-STAP for airborne-phased array radar is analysed. The polarised signal model of airborne-phased array radar is firstly established and then clutter suppression principle of P-STAP is derived in detailed. Afterwards, the performance of P-STAP is investigated and compared with conventional STAP in ambiguous clutter environment. It is verified that the P-STAP can outperform the conventional STAP for slow moving target detection by three-domain joint processing, especially for ambiguous and non-broadside clutter environment for airborne-phased array radar.

Moving target detection in clutter background with FDA‐MIMO radar via three‐dimensional focus processing

Low observable moving target-detection technology under clutter background is the key factor affecting the radar performance. Here, frequency diverse array (FDA) MIMO radar is investigated for low-observable moving target detection. FDA-MIMO radar not only has all the advantages of phased array and MIMO radar, but also can make a two-dimensional joint estimation of the target distance and azimuth. In order to achieve coherent integration for Doppler processing, a novel three-dimensional processing method is proposed, i.e. space-range-Doppler focus (SRDF) processing. It utilises the property of FDA and high-resolution Doppler processing of MIMO. After discussing signal model of FDA-MIMO radar, the flowchart of SRDF-based low-observable moving target detection and estimation is provided. Finally, simulation of moving target detection in clutter background verifies that proposed method has better ability for joint angle-range-Doppler processing with higher resolutions.

A Unified Formulation of Polarimetric Weather Radar With Application to IQ Data Simulation

The meteorological parameters derived from polarimetric weather radar measurements are of interest for nowcasting and to feed numerical models for forecasting. The accuracy of the derived meteorological parameters is affected by the polarimetric measurement scheme considered as well as by the system hardware (e.g., the antenna radiation patterns or receive channels imbalances). However, determining the accuracy of the estimated meteorological parameters is difficult since no ground truth is available. This fact has motivated the development of weather radar data simulators that allow comparison of the assumed weather parameters used to feed the system and the finally estimated weather parameters. In this paper, the weather radar model is reviewed. A unified formulation applicable to all polarimetric measurement systems is developed. This formulation shows that antenna effects can be decoupled from scattering effects. Based on this formulation, a weather radar simulator is proposed. This radar simulator does not require the fine sampling in the elevation and azimuth directions required by previous simulators. Therefore, the computational load and execution times are significantly reduced. Finally, it is important to point out that the radar simulator proposed provides as much fidelity to the actual radar system as previous simulators.