Radar Signal Simulation Research Articles

Development of a Software Package Architecture for Simulation and Prototyping of Radar Systems and Complexes

Introduction. Computer simulation and prototyping software can simplify the design process of complex information and measurement systems significantly, including radar systems and complexes. At present, a number of software packages are used to solve these problems to varying degrees. However, these software packages are either versatile, thus being incapable of taking the specifics of radar operation into account and requiring hand-made implementation of mathematical models for simulating radar signals, or are aimed at a narrow range of prototyping problems and algorithm development for processing radar information for a strictly defined radar type (or even a specific model). Some software packages, such as MATLAB, offer extension packages that allow radar signal simulation for automotive radars, as well as radar signal processing; however, these packages cannot cover the full range of simulation and prototyping tasks.Aim. Analysis of current software packages for simulation and prototyping of radar systems and complexes, justification of the demand and development of the concept and architecture of a software package for simulation and prototyping of radar systems and complexes.Materials and methods. Systems approach, architectural and conceptual software design, system analysis, criterion analysis. Results. The criteria that software packages for simulating and prototyping of radar systems and complexes must meet were determined. A comparative analysis of the existing approaches and software packages that solve problems arising at various stages of radar development was carried out. A list of requirements for such a software package was compiled, its concept and architecture was developed, and some features of its implementation were determined.Conclusion. The developed architecture allows creation of a versatile software package which could provide solutions to the problems of simulation and prototyping of radar systems and complexes using a single software package. The applied principles of modularity and decomposition ensure versatility and a high potential for adapting software modules, including for creating software for controlling radar prototypes and visualizing radar data in real time.

MIDW‐Net: A multi‐tasking network architecture for radar intra‐pulse parameter description

AbstractThe automatic modulation recognition (AMR) of radar signals has become a popular research topic in recent years. However, most algorithms focus on the type of signal modulation and lack further understanding of the signal. To address this gap, a network architecture for multi‐tasking intra‐pulse description words (MIDW‐Net) is proposed herein. In this framework, the denoising algorithm employs a convolutional denoising autoencoder, which is an effective method for suppressing noise interference and preserving signal information. The multiscale feature‐extraction capability of a feature pyramid network (FPN) is utilized to expand the perceptual domain without losing the high‐frequency features of the image. Finally, AMR and modulation parameter estimation are accomplished via multitask learning. Experiments performed on simulated radar signals using four intra‐pulse descriptors verified the effectiveness of the proposed algorithm.

Convolutional neural network for fast adaptive beamforming of phased array weather radar

PurposeThe purpose of this paper is to present a deep-learning-based beamforming method for phased array weather radars, especially whose antenna arrays are equipped with large number of elements, for fast and accurate detection of weather observations.Design/methodology/approachThe beamforming weights are computed by a convolutional neural network (CNN), which is trained with input–output pairs obtained from the Wiener solution.FindingsTo validate the robustness of the CNN-based beamformer, it is compared with the traditional beamforming methods, namely, Fourier (FR) beamforming and Capon beamforming. Moreover, the CNN is compared with a radial basis function neural network (RBFNN) which is a shallow type of neural network. It is shown that the CNN method has an excellent performance in radar signal simulations compared to the other methods. In addition to simulations, the robustness of the CNN beamformer is further validated by using real weather data collected by the phased array radar at Osaka University (PAR@OU) and compared to, besides the FR and RBFNN methods, the minimum mean square error beamforming method. It is shown that the CNN has the ability to rapidly and accurately detect the reflectivity of the PAR@OU with even less clutter level in comparison to the other methods.Originality/valueMotivated by the inherit advantages of the CNN, this paper proposes the development of a CNN-based approach to the beamforming of PAR using both simulated and real data. In this paper, the CNN is trained on the optimum weights of Wiener solution. In simulations, it is applied on a large 32 × 32 planar phased array antenna. Moreover, it is operated on real data collected by the PAR@OU.

General Electromagnetic Simulation of Radar Signals Backscattered from Metallic Wind Turbines

General Electromagnetic Simulation of Radar Signals Backscattered from Metallic Wind Turbines

Developing a Radar Signal Simulator for the Community Radiative Transfer Model

Developing a Radar Signal Simulator for the Community Radiative Transfer Model

Lidar and Radar Signal Simulation: Stability Assessment of the Aerosol–Cloud Interaction Index

Aerosol–cloud interactions (ACI) are in the spotlight of atmospheric science since the limited knowledge about these processes produces large uncertainties in climate predictions. These interactions can be quantified by the aerosol–cloud interaction index (ACI index), which establishes a relationship between aerosol and cloud microphysics. The experimental determination of the ACI index through a synergistic combination of lidar and cloud radar is still quite challenging due to the difficulties in disentangling the aerosol influence on cloud formation from other processes and in retrieving aerosol-particle and cloud microphysics from remote sensing measurements. For a better understanding of the ACI and to evaluate the optimal experimental conditions for the measurement of these processes, a Lidar and Radar Signal Simulator (LARSS) is presented. LARSS simulate vertically-resolved lidar and cloud-radar signals during the formation process of a convective cloud, from the aerosol hygroscopic enhancement to the condensation droplet growth. Through LARSS simulations, it is observed a dependence of the ACI index with height, associated with the increase in number (ACINd) and effective radius (ACIreff) of the droplets with altitude. Furthermore, ACINd and ACIreff for several aerosol types (such as ammonium sulfate, biomass burning, and dust) are estimated using LARSS, presenting different values as a function of the aerosol model. Minimum ACINd values are obtained when the activation of new droplets stops, while ACIreff reaches its maximum values several meters above. These simulations are carried out considering standard atmospheric conditions, with a relative humidity of 30% at the surface, reaching the supersaturation of the air mass at 3500 m. To assess the stability of the ACI index, a sensitivity study using LARSS is performed. It is obtained that the dry modal aerosol radius presents a strong influence on the ACI index fluctuations of 18% cause an ACI variability of 30% while the updraft velocity within the cloud and the wet modal aerosol radius have a weaker impact. LARSS ACI index uncertainty is obtained through the Monte Carlo technique, obtaining ACIreff uncertainty below 16% for the uncertainty of all LARSS input parameters of 10%. Finally, a new ACI index is introduced in this study, called the remote-sensing ACI index (ACIRs), to simplify the quantification of the ACI processes with remote sensors. This new index presents a linear relationship with the ACIreff, which depends on the Angstrom exponent. The use of ACIRs to derive ACIreff presents the advantage that it is possible to quantify the aerosol–cloud interaction without the need to perform microphysical inversion retrievals, thus reducing the uncertainty sources.

Signal Simulation of Dual-Polarization Weather Radar and Its Application in Range Ambiguity Mitigation

In this paper, a dual-polarization weather radar echo signal simulation method is proposed for the evaluation of the performance enhancement of dual-polarization weather radar systems, the optimization of signal processing algorithms and the improvement of scanning strategies. The actual weather radar base data are used in the simulation as the reference weather scene, which avoids using a complex algorithm for weather modeling. Moreover, based on radar weather equations, the radar system parameters are added into the radar echo signal modeling to establish the relationship between the simulated echo signal and radar system. As a result, the final simulated echo signal not only shows both the time and frequency domain characteristics of the weather target, but also includes the effects of the important performance of the dual-polarization weather radar system. In addition, to evaluate the performance of range ambiguity mitigation using phase coding and batch working modes, two different simulation methods for the radar signal are established on the method above; one is for batch working mode with long-PRT (pulse repetition time) and short-PRT transmission and receiving, and the other is for phase-coded mode with phase-coded transmission and phase-uncoded receiving. Under the same weather scene, the observation of these two different methods of range ambiguity mitigation are simulated and compared. Results show that the performance of the phase coding mode for mitigating range ambiguity is better than that of the batch mode. Obviously, the simulation method can be used to directly show the observation of different algorithms for mitigation range ambiguity under the same weather process, and quickly compare and evaluate the algorithm’s performance, which is not possible for real radars.

Convolutional Neural Networks for Classification of Drones Using Radars

The ability to classify drones using radar signals is a problem of great interest. In this paper, we apply convolutional neural networks (CNNs) to the Short-Time Fourier Transform (STFT) spectrograms of the simulated radar signals reflected from the drones. The drones vary in many ways that impact the STFT spectrograms, including blade length and blade rotation rates. Some of these physical parameters are captured in the Martin and Mulgrew model which was used to produce the datasets. We examine the data under X-band and W-band radar simulation scenarios and show that a CNN approach leads to an F1 score of 0.816±0.011 when trained on data with a signal-to-noise ratio (SNR) of 10 dB. The neural network which was trained on data from an X-band radar with 2 kHz pulse repetition frequency was shown to perform better than the CNN trained on the aforementioned W-band radar. It remained robust to the drone blade pitch and its performance varied directly in a linear fashion with the SNR.

Simulation of Moving Target Indication Radar System Based on VisSim/Comm Application

Moving target indication (MTI) is mainly designed to detect moving targets and while unmoving targets are filtered out. It focuses on the technique of the modern stationary target indication (STI), by using directly the signal details to determine the reflecting object’s mechanical properties, after that it becomes easier to find moving or non-moving targets. This paper presents the simulation design of the MTI radar system. The main purpose of this design is to help students in understanding the radar system subject and help teachers to explain this subject in a simpler approach. Both students and teachers need to know how the signals inside the MTI radar processor are working and how they are generated and related to each other. This paper introduces a method of simulation of MTI radar signals including, A-scope radar display, transmitted and returned radar pulses with constant and multiple PRF, n-delay line cancellers.

Simulation and Synchronous Generation of Radar Signals at Geographically Distributed Sensors for Testing Emitter Location Systems

Location Fixing (LF) of Radar Emitters is carried out in an Electronic Warfare scenario by Sensors with Direction-of-Arrival and signal parameter measurement capabilities, geographically distributed over few tens of kilometres. The present paper brings out the Scenario Simulation and Synchronous generation of Radar Signal at Geographically distributed locations for comprehensive testing of the Emitter Location Systems (ELS). This test facility permits exhaustive testing of the ELS against various parameters since the performance of the ELS depends on factors such as Sensors’ geometry with respect to the location of the Emitter of interest, signal parameter measurement accuracies, LF algorithms used, time synchronization accuracy achievable among the Sensors, etc. The Radar signals are synthesized synchronously, locally, at each of the distributed Sensors by replicating the Emitter Signal characteristics as received at the Sensors. The Simulator System uses a High Accuracy GPS Receiver as the Global Clock against which the signals are timed and a Field Programmable Gate Array for generating controls triggered on the absolute time of the Global Clock.

Microphysical and Polarimetric Radar Modeling of Hydrometeor Refreezing

AbstractA unique polarimetric radar signature indicative of hydrometeor refreezing during ice pellet events has been documented in several recent studies, yet the underlying microphysical causes remain unknown. The signature is characterized by enhancements in differential reflectivity (ZDR), specific differential phase (KDP), and linear depolarization ratio (LDR), and a reduction in copolar correlation coefficient (ρhv) within a layer of decreasing radar reflectivity factor at horizontal polarization (ZH). In previous studies, the leading hypothesis for the observed radar signature is the preferential refreezing of small drops. Here, a simplified, one-dimensional, explicit bin microphysics model is developed to simulate the refreezing of fully melted hydrometeors, and coupled with a polarimetric radar forward operator to quantify the impact of preferential refreezing on simulated radar signatures. The modeling results demonstrate that preferential refreezing is insufficient by itself to produce the observed signatures. In contrast, simulations considering an ice shell growing asymmetrically around a freezing particle (i.e., emulating a thicker ice shell on the bottom of a falling particle) produce realistic ZDR enhancements, and also closely replicate observed features in ZH, KDP, LDR, and ρhv. Simulations that assume no increase in particle wobbling with freezing produce an even greater ZDR enhancement, but this comes at the expense of reducing the LDR enhancement. It is suggested that the polarimetric refreezing signature is instead strongly related to both the distribution of the unfrozen liquid portion within a freezing particle and the orientation of this liquid with respect to the horizontal.

Separating Cloud and Drizzle Signals in Radar Doppler Spectra Using a Parametric Time Domain Method

AbstractThe ability to separate cloud and drizzle returns in active remote sensing observations is important for understanding the microphysics of clouds and precipitation. Yet, robust separations remain challenging in radar remote sensing. Prior methods for cloud and drizzle separation for radar observations use the properties of the Doppler spectra such as skewness. However, these methods have challenges when the drizzle becomes dominant in the observation volume. This paper presents a parametric time domain method (PTDM) that separates cloud and drizzle using the Doppler spectra measurements without assuming any prior properties of cloud and drizzle. The advantage of PTDM is that it can estimate the signal properties in the time domain and can obtain the cloud and drizzle estimates simultaneously. Based on our radar signal simulations, the uncertainty in estimated power and velocity from PTDM are within 2 dB and 0.02 m s−1, respectively. We have also evaluated the PTDM algorithm using observations from the Atmospheric Radiation Measurement (ARM) Program W-band cloud radar in the Clouds, Aerosols, and Precipitation in the Marine Boundary Layer (CAP-MBL) campaign at the Azores in 2009–10. Two cases corresponding to light and moderate drizzling conditions are considered for the study. The statistics of the estimates obtained show that the PTDM method performs well in separating the cloud and drizzle returns. Finally, the estimated cloud and drizzle reflectivity from PTDM were used to retrieve their corresponding microphysical properties, showing that the retrieved liquid water path agrees to 25 g m−2 with the benchmark microwave method.

Graphic User Interface Development of a Digital Phased Array Radar System

A graphical user interface (GUI) of a digital phased array radar system is developed to detect and reveal the supposed multi-targets. As the core of the digital radar system, the radar signal generation environment aims at producing a radar echo signal, which is achieved by superposing the simulated radar transmission signals, clutter, jamming signals and radar internal noise linearly; And the radar signal processing environment focuses on processing the radar echo signal to measure multi-target range and velocity information, by employing pulse compression (PC), moving target indication (MTI), moving target detection (MTD) and constant false alarm rate (CFAR) technologies. Every module of the core can be exhibited in the form of MATLAB GUI. In addition, an integrated GUI of the digital phased array radar system is constructed to offer all the multi-target detection and display functions.

Radar signal processing algorithm and simulation of detection system

With its unique array arrangement, the detection system radar has both space diversity gain and waveform diversity gain, and is currently recognized as a stealth target buster. The detection system radar is applied to a high-speed moving platform. Using distributed cooperative detection technology, non-coherent fusion detection based on signals can further improve the detection of stealthy targets. Aiming at the high-speed motion radar signal processing algorithm, this paper mainly studies the following three aspects: the first content is the analysis of the waveform characteristics: the basic principles and characteristics of the radar are explained; then the three orthogonal waveforms commonly used in the radar are introduced, including Stepwise frequency division chirp signal, quadrature phase coded signal and mixed-signal; the second content detects radar targets and analyzes the correlation between the scattering coefficients of different radar channels; for scenarios where the scattering coefficients between the channels are non-coherent Introduced two kinds of non-coherent fusion detectors based on generalized likelihood ratio algorithm: centralized detector and double threshold detector; the third content radar multi-target pairing is aimed at the problem of radar multi-target pairing with large inertial navigation error. A multi-target pairing algorithm that uses target delay information and combines the radar’s multi-channel information redundancy characteristics is presented. An expression for judging the correctness of target pairing is derived, and the target pairing steps are given. The relationship between the amount of algorithm operation and the number of radar stations and the number of targets is analyzed in conclusion.

Parametric Scattering Center Model for Canonical and Composite Dielectric Objects

The scattering center (SC) model provides a concise, explicit description of the object electromagnetic characteristics, significantly improves the efficiency of radar signal simulations, and plays an important role in model-based automatic target recognition. The existing models are mainly developed for conducting objects, and few are developed for dielectric ones. The SC models of dielectric objects are essentially different from those of their conducting counterparts due to the different scattering mechanisms. In this article, the backscattering mechanisms of canonical dielectric structures are studied, and their corresponding SC models are proposed. More importantly, the parametric SC models of the composite objects of canonical structures are first presented. These SC models are validated by comparing the radar cross section (RCS), the time–frequency representation (TFR), and the high-resolution range profile (HRRP) from the SC model with the results of the full-wave numerical method.

Microphysics of Snowfall Over Coastal East Antarctica Simulated by Polar WRF and Observed by Radar

AbstractThe current assessment of the Antarctic surface mass balance mostly relies on reanalysis products or climate model simulations. However, little is known about the ability of models to reliably represent the microphysical processes governing the precipitation. This study makes use of recent ground‐based precipitation measurements at Dumont d'Urville station in Adélie Land to evaluate the representation of the precipitation microphysics in the Polar version of the Weather Research Forecast (Polar WRF) atmospheric model. During two summertime snowfall events, high‐resolution simulations are compared to measurements from an X‐band polarimetric radar and from a Multi‐Angle Snowflake Camera (MASC). A radar simulator and a “MASC” simulator in Polar WRF make it possible to compare similar observed and simulated variables. Radiosoundings and surface‐meteorological observations were used to assess the representation of the regional dynamics in the model. Five different microphysical parameterizations are tested. The simulated temperature, wind, and humidity fields are in good agreement with the observations. However, the amount of simulated surface precipitation shows large discrepancies with respect to observations, and it strongly differs between the simulations themselves, evidencing the critical role of the microphysics. The inspection of vertical profiles of reflectivity and mixing ratios revealed that the representation of the sublimation process by the low‐level dry katabatic winds strongly influences the actual amount of precipitation at the ground surface. By comparing the simulated radar signal as well as MASC and model particle size distributions, it is also possible to identify the microphysical processes involved and to pinpoint shortcomings within the tested parameterizations.

A Preliminary Application of SystemVue on Weather Radar Simulation

This paper first briefly introduces the simulation environment of SystemVue, and introduces the creation process of weather radar component using the PPP algorithm in weather radar signal processing as an example. Next, a test platform for PPP component was built on SystemVue, and the performance of the component was tested using simulated radar echo signals. The results show that the SystemVue platform can help to verify the weather radar signal processing algorithm, and that the software radar can be built through SystemVue to implement the simulation and new algorithm verification of different weather radar systems.

Design and implementation of multi‐channel moving target radar signal simulator

The development of radar signal simulator in specific environment can effectively accelerate the debugging process of radar system and reduce the consumption of human, material, and financial resources. This study analyses the mathematical model of the IF radar signal in aerial distributed semi-active radar system and deduces the parameters of radar signals based on direct digital synthesiser (DDS) technology. After solving the problem of multi-channel synchronisation, delay precision control, and envelope delay compensation, an IF radar signal simulator system was designed and implemented, which can generate three direct-path-wave signals and three echo signals.

Time-Frequency Represetation of Radar Signals Using Doppler-Lag Block Searching Wigner-Ville Distribution

Radar signals are time-varying signals where the signal parameters change over time. For these signals, Quadratic Time-Frequency Distribution (QTFD) offers advantages over classical spectrum estimation in terms of frequency and time resolution but it suffers heavily from cross-terms. In generating accurate Time-Frequency Representation (TFR), a kernel function must be able to suppress cross-terms while maintaining auto-terms energy especially in a non-cooperative environment where the parameters of the actual signal are unknown. Thus, a new signal-dependent QTFD is proposed that adaptively estimates the kernel parameters for a wide class of radar signals. The adaptive procedure, Doppler-Lag Block Searching (DLBS) kernel estimation was developed to serve this purpose. Accurate TFRs produced for all simulated radar signals with Instantaneous Frequency (IF) estimation performance are verified using Monte Carlo simulation meeting the requirements of the Cramer-Rao Lower Bound (CRLB) at SNR > 6 dB.

Detection of Frozen Soil Using Sentinel-1 SAR Data

The objective of this paper is to evaluate the potential of Sentinel-1 Synthetic Aperture Radar “SAR” data (C-band) for monitoring agricultural frozen soils. First, investigations were conducted from simulated radar signal data using a SAR backscattering model combined with a dielectric mixing model. Then, Sentinel-1 images acquired at a study site near Paris, France were analyzed using temperature data to investigate the potential of the new Sentinel-1 SAR sensor for frozen soil mapping. The results show that the SAR backscattering coefficient decreases when the soil temperature drops below 0 °C. This decrease in signal is the most important for temperatures that ranges between 0 and −5 °C. A difference of at least 2 dB is observed between unfrozen soils and frozen soils. This difference increases under freezing condition when the temperature at the image acquisition date decreases. In addition, results show that the potential of the C-band radar signal for the discrimination of frozen soils slightly decreases when the soil moisture decreases (simulated data were used with soil moisture contents of 20 and 30 vol%). The difference between the backscattering coefficient of unfrozen soil and the backscattering coefficient of frozen soil decreases by approximately 1 dB when the soil moisture decreases from 30 to 20 vol%). Finally, the results show that both VV and VH allow a good detection of frozen soils but the sensitivity of VH is higher by approximately 1.5 dB. In conclusion, this study shows that the difference between a reference image acquired without freezing and an image acquired under freezing conditions is a good tool for detecting frozen soils.