Interpretation Of Radar Data Research Articles

3D ground-penetrating radar data analysis and interpretation using attributes based on the gradient structure tensor

In near-surface geophysics, ground-penetrating radar (GPR) surveys are routinely used in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. Thanks to recent technical developments in GPR instrumentation and antenna design, 3D surveys comprising several hundred thousand traces can be performed daily. Especially in complex environments such as sedimentary systems, analyzing and interpreting the resulting GPR volumes is a time-consuming and laborious task that is still largely performed manually. In the past few decades, several data attributes have been developed to guide and improve such tasks and assure a higher degree of reproducibility in the resulting interpretations. Many of these attributes have been developed in image processing or computer vision and are routinely used, for example, in reflection seismic data interpretation. Especially in sedimentary systems, variations in the subsurface are accompanied by variations of GPR reflections in terms of the amplitudes, continuity, and geometry in view of the dip angle and direction. A promising tool to analyze such structural features is known as the gradient structure tensor (GST). To date, the application of the GST approach has been limited to a few 2D GPR examples. Thus, we take the basic idea of GST analysis and introduce and evaluate the corresponding attributes to analyze 3D GPR data. We apply our GST approach to one synthetic and two field data sets imaging diverse sedimentary structures. Our results demonstrate that our set of GST-based attributes can be efficiently computed in three dimensions and that these attributes represent versatile measures to address different typical interpretation tasks and, thus, help for an efficient, reproducible, and more objective interpretation of 3D GPR data.

High-precision imaging of small voids in tunnel lining based on reverse time migration

Abstract Diseases such as voids behind the initial support of the tunnel will lead to problems such as lining rupture and concrete damage in the tunnel structure, which seriously affects the driving safety in the tunnel. In tunnel construction, ground penetrating radar is frequently employed as a method for detecting hidden defects. However, when the cavity size is small, there will be a large error when the original radar data is directly interpreted, which cannot meet the needs of practical engineering. To enhance the precision in tunnel detection, the Finite-Difference Time-Domain method is used to simulate various small cavities of different shapes located behind the primary support lining of the tunnel, and the study involves examining the electromagnetic response characteristics of different kinds of holes. The migration techniques of KIRCHHOFF, F-K, and reverse time are employed to reconstruct the hole target. Furthermore, digital morphology is employed to enhance the clarity of the image. Finally, the edge detection technology is used to further extract the hole features. After that, small cavities of different shapes are buried in the established outdoor concrete model box and radar detection is carried out. The detection position of the reconstructed radar image and the actual position of the cavity in the model box are verified. The results show that compared with KIRCHHOFF migration and F-K migration, the image processed by reverse time migration is clearer and more intuitive, and can restore the contour of small holes well. The research findings can serve as a reference for the interpretation of radar data of the cavity behind the initial support of the tunnel.

The ground penetrating radar response recognition of hidden dam defects using multi-output convolutional neural network

The Ground Penetrating Radar (GPR) data interpretation of dam defects detection by manual process is a heavy task. Although the convolutional neural network (CNN) is applicable to detect road defects with the help of other auxiliary instruments and techniques, it is still a challenge for the dam defect detection by deep learning. To overcome this problem, a multi-output CNN model for GPR response recognition of dam defects is proposed. The training dataset and test dataset are produced from a large number of GPR responses with different types of dam defects forward modeling by GPRMax3.0 software. The results show that the highest recognition accuracy of the training dataset and test dataset is about 98% and 97%, respectively. To verify the effectiveness, the new proposed method is used to recognize the hidden GPR responses of dam defects in the real GPR data. The results show that the types and locations of dam defects in the real GPR data can be recognized and classified automatically, efficiently and accurately.

Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review.

Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields have all benefited from recent advancements in mmWave radar sensor technology. A mmWave radar has several advantages that set it apart from other types of sensors. A mmWave radar can operate in bright, dazzling, or no-light conditions. A mmWave radar has better antenna miniaturization than other traditional radars, and it has better range resolution. However, as more data sets have been made available, there has been a significant increase in the potential for incorporating radar data into different machine learning methods for various applications. This review focuses on key performance metrics in mmWave-radar-based sensing, detailed applications, and machine learning techniques used with mmWave radar for a variety of tasks. This article starts out with a discussion of the various working bands of mmWave radars, then moves on to various types of mmWave radars and their key specifications, mmWave radar data interpretation, vast applications in various domains, and, in the end, a discussion of machine learning algorithms applied with radar data for various applications. Our review serves as a practical reference for beginners developing mmWave-radar-based applications by utilizing machine learning techniques.

A Five-Component Decomposition Method with General Rotated Dihedral Scattering Model and Cross-Pol Power Assignment

The model-based polarimetric decomposition is extensively studied due to its simplicity and clear physical interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) data. Though there are many fine basic scattering models and well-designed decomposition methods, the overestimation of volume scattering (OVS) may still occur in highly oriented buildings, resulting in severe scattering mechanism ambiguity. It is well known that not only vegetation areas but also oriented buildings may cause intense cross-pol power. To improve the scattering mechanism ambiguity, an appropriate scattering model for oriented buildings and a feasible strategy to assign the cross-pol power between vegetation and oriented buildings are of equal importance. From this point of view, we propose a five-component decomposition method with a general rotated dihedral scattering model and an assignment strategy of cross-pol power. The general rotated dihedral scattering model is established to characterize the integral and internal cross-pol scattering from oriented buildings, while the assignment of cross-pol power between volume and rotated dihedral scattering is achieved by using an eigenvalue-based descriptor DOOB. In addition, a simple branch condition with explicit physical meaning is proposed for model parameters inversion. Experiments on spaceborne Radarsat−2 C band and airborne UAVSAR L band PolSAR datasets demonstrate the effectiveness and advantages of the proposed method in the quantitative characterization of scattering mechanisms, especially for highly oriented buildings.

Ground penetrating radar investigation and georeferencing without global satellite navigation systems: The case history of the amphitheatre of Lecce, Italy

AbstractIn this work, we present a case history relative to ground penetrating radar measurements performed close to the Roman amphitheatre of Lecce, Italy. We have performed a classical data elaboration with focusing of the data and slicing putting into evidence hidden details of the structure of the monument. It will be shown that the interpretation of the ground penetrating radar data is meaningfully helped by the consultation of ancient documents, which makes the final result multidisciplinary. Finally, we have georeferenced the data matching the shape of the depth slices with the shape of the investigated roads. In fact, we did not have at disposal any differential global satellite navigation systems. Indeed, this can be a method exploitable in cases when satellite data are not available, either because the area is shadowed or because the user does not have a differential global satellite navigation system. The proposed geographical matching is achieved by means of the matching between the shape of the slices and those of the physical obstacles present in the field. Therefore, it is essentially based on a continuous of points, and so it is probably more precise than a method based on the only vertexes. In particular, the proposed procedure does not require any deformation of the shape of the slice.

Research on Underground Pipeline Detection Method Based on Background Matrix Subtraction and Three-Dimensional Velocity Spectrum

Ground-penetrating radar (GPR) can detect urban underground pipelines and image their spatial distribution. However, due to the interference of direct wave and ground reflected wave in radar profile, the detection accuracy of underground pipeline depth is low. In order to improve the reliability and accuracy of the interpretation of ground penetrating radar data, it is necessary to suppress the noise, and then to detect underground pipelines. Firstly, on the basis of data collection, a background matrix subtraction (BMS) method is proposed to suppress noise signals, and the Noise reduction effect is compared and analyzed by two groups of simulation data and two groups of measured data examples with the method of reducing average channel and singular decomposition. Then, a three-dimensional velocity spectrum (3DVS) method is proposed to estimate the buried depth of underground pipeline in radar profile, and the propagation velocity of electromagnetic wave in underground media is calculated by automatically scanning the hyperbolic reflection signal. The estimated velocity is used to carry out back-propagation migration (BPM) processing on the ground penetrating radar profile, and the underground pipeline is accurately detected. Finally, the underground pipeline detection method based on BMS and 3DVS is applied to a residential area in Nanjing, Jiangsu, China. The detection results show that the effect of BMS is obviously better than that of mean-reducing method and singular decomposition method, which can suppress the noise well on the basis of ensuring that the effective signal is not lost. It is helpful to identify the characteristics of target signal in ground penetrating radar profile, and improve the accuracy and reliability of data interpretation. The error between the velocity value estimated by the 3DVS method and the real value is less than 3.8%, and the buried depth error of pipeline target in the obtained data is 1.4%, which indicates that the algorithm has practical application value and can realize the accurate positioning of underground pipelines.

Overview of the dielectric permittivity of lunar surface materials and implications for Chang’E-5 samplemeasurements

<p indent="0mm">The dielectric permittivity of lunar surface materials determines the penetration depth of electromagnetic waves in the lunar subsurface; hence, it is crucial to the design of scientific payloads such as radars, microwave radiometers, and lunar surface electromagnetic experiments. Additionally, dielectric permittivity is a key parameter in deciphering radar and microwave radiometer observations, which is important to obtain quantitative information regarding the subsurface structure and physical properties of the Moon. Our current knowledge of the dielectric permittivity of lunar surface materials depends mainly on the inversion of microwave remote sensing data and laboratory measurements of the Apollo and Luna samples. In December 2020, China’s Chang’E-5 mission successfully collected <sc>1.731 kg</sc> of lunar samples from the northern Oceanus Procellarum, thus providing new samples for studying the dielectric properties of lunar materials. In this paper, I will first introduce the basic knowledge of dielectric permittivity, including the dielectric polarization mechanism, dielectric permittivity mixing model, and measurement methods. Then, I will summarize the history and present status of the research on the dielectric permittivity of lunar materials and demonstrate the dielectric permittivity of the Moon. Finally, I will present the latest research results of the geological background and regolith properties of the Chang’E-5 landing region and provide some suggestions regarding the laboratory measurements and potential scientific outcome of the Chang’E-5 lunar samples. The summarization of our current knowledge of the dielectric permittivity of lunar regolith in this paper can provide theoretical guidance and engineering support for microwave sensor design, radar data interpretation, sampling site selection, and sample analysis for China’s future lunar exploration missions.

Study on the Influencing Factors of the Permittivity Estimation Method Considering Antenna Layout Based on Lunar Penetrating Radar

The permittivity of lunar regolith is crucial for further processing and interpretation of radar data. The conventional hyperbolic fitting method ignores the antenna height and spacing and has a significant error at a shallow depth. For the new method that considers the layout of the antenna, the influencing factors have not been studied. In this paper, we studied the influence of the position of the hyperbola peak and time zero on the new method for permittivity derivation. The simulation results show that when the input errors of time zero, abscissa and ordinate of the hyperbolic peak are ±2 ns, ±0.02 m and ±0.2 ns respectively, the average errors of the calculated results by points within 1 m from the hyperbolic peak are 10.0%, 16.7% and 38.2%, respectively. To improve the accuracy, we used the average results by points that are horizontally more than 1 m away from the hyperbola peak. Hence, we calculated the permittivity of the lunar regolith by the new method based on Lunar Penetrating Radar data. The average permittivity of the lunar regolith is estimated to be 3.3 ± 1.2.

An experimental study of the alteration of basalt on the surface of Venus

Characterizing the surface of Venus has been complicated by its thick atmosphere and caustic surface conditions (~470 °C, 90 bars). Several approaches, including the collection of spectral data, thermodynamic modelling, lander missions, and surface weathering laboratory experiments have progressed our view of what lies at the surface. However, surface-atmosphere interactions remain somewhat unconstrained and interpretations of the spectral data rely on an understanding of the surface-atmosphere alteration. We used a cold-seal pressure vessel apparatus pressurized with pure CO2 gas and both synthetic and natural glassy basalts specimens to simulate chemical weathering on the surface of Venus for a duration of two weeks. The extent of alteration was described from the surface of samples to depth using Rutherford Backscatter Spectroscopy (RBS), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) techniques. We describe the alteration zones of reacted basalt specimens and report an enrichment of divalent cation species at the near surfaces of basalts; Ca2+ was enriched by ~5 wt% and Fe2+ was enriched by ~1–2 wt%. The enrichment at the near surface favors the production of iron (Fe) oxide(s) and carbonates on the surfaces, which form discontinuous coatings on all reacted samples in two weeks duration. Our results aid in the interpretation of radar emissivity data by constraining which alteration products should be present on the surface and suggesting timeframes necessary for their detection. Assuming that radar emissivity is able to discern weathered basalt, especially those dominated by carbonates and/or semiconducting minerals Fe oxide(s), our results suggest that basalts at Idunn Mons in Imdr Regio previously thought to be anywhere from 2.5 million to a few years old (Smrekar et al., 2010; D'Incecco et al., 2017; Filiberto et al., 2020; Cutler et al., 2020) could instead be as young as 65,000 years to 110,000 years depending on basalt type.

Recognition of void defects in airport runways using ground-penetrating radar and shallow CNN

The interpretation of ground-penetrating radar (GPR) data usually depends on manual work, thus yielding unsatisfactory accuracy when processing complicated cases. To address this issue, the finite-difference time-domain method was adopted to explore the void-related features in GPR images, and guide the field survey to locate the void area. On this basis, a dataset including 811 void features was established from 10 airport runways using an 800 MHz ground-coupled GPR system. Data augmentation was used to enlarge the dataset and train four configurations of a mixed convolution neural network (CNN) model with feature extractors (ResNet18 and ResNet50) and object detectors (YOLOv2 and Faster R-CNN). To make the detection process automatic, an incremental random sampling (IRS) approach was used to generate images from GPR data and then fed into the trained model. The experimental results demonstrated that the shallow ResNet18-YOLOv2 with the IRS method is a promising strategy for void detection in airport runways.

A Multidisciplinary Method to Support the Evolution of NWS Weather Radar Technology

Abstract The highly successful fleet of Weather Surveillance Radar-1988 Doppler (WSR-88D) radars is approaching its end of service, and research efforts are under way to inform a decision toward a possible WSR-88D replacement. A methodology to link radar design characteristics to impacts on how radar data are used to diagnose hazardous weather was developed through a unique partnership between radar-engineering innovations in radar simulations and National Weather Service (NWS) radar data interpretation expertise. Deep commitment to two-way learning across disciplines resulted in a methodology that is both efficient and highly relevant to the NWS hazardous weather warning program. The methodology presented in this paper is a model for revealing complex trade-offs between weather radar characteristics and their resultant impact on NWS data interpretation for threat identification. This qualitative methodology is presented in the context of a broader proof-of-concept study from which it was developed. Adapted for further research, it can support the crucial role of deriving quantitative radar design criteria that balance the trade-offs among radar capabilities, cost, and impact to users. That is, the proposed methodology supports the evaluation of candidates for a potential WSR-88D replacement and any necessary major system upgrades in the interim. Significance Statement Defining the requirements for an operational weather radar system is ideally achieved with clearly identified trade-offs among cost, radar design characteristics, and impacts on user data interpretation. This work is an advancement of the historic evolution of weather radar development to support the U.S. National Weather Service (NWS) mission, based on collaboration among researchers, radar engineers, and NWS forecasters. The methodology presented here is an adaptable tool for revealing these essential, but complex trade-offs, providing a roadmap for further studies toward the next-generation NWS weather radar fleet.

Investigation of the hydrodynamics of man-made water by remote methods (on the example of southern Kryvbas)

The purpose of this study was to identify the degree and nature of man-made changes in the condition of the hydrogeological structure of the upper part of the earth's crust in the south of the Kryvyi Rih iron ore basin (Kryvbas). Another goal was the search and localization of sources of highly mineralized waters of the studied territory. To understand the nature of the watering (dewatering) of the section, the results of mapping the deformation of the terrain on the Sentinel-1 IW SLC space radar images were used. The resulting changes in the relief of the area were compared with the results of repeated geoelectrical observations, which showed zones of the day surface to the south of the "Livoberezhny" dumps, which are subject to subsidence and have areas of local flooding and waterlogging. They are mainly associated to the complex structure of zones of tectonic disturbances, zones of intersection of Precambrian faults of sub-meridional extension and west-northwest, which affect the distribution and localization of mineralized waters. Analysis of the data of hydrogeological, hydrological, engineering-geological and geophysical studies of the territory at the end of the 20th and beginning of the 21st centuries showed the degree of change in the hydrogeological environment over several decades. A comprehensive interpretation of space radar and geophysical survey data made it possible to assess the physical processes that take place from the surface to depths of tens of meters below it. The carried out geoelectrical research made it possible to establish the width of the rock water table in the upper part of the section, which ranges from 50 to 150 m, and the presence of a local depression on the roof of the foundation, under pond "Lebiazhyi". The latter is due to the specificity of the connection (knot) of differently oriented faults, where highly mineralized waters preserved in the Kryvyi Rih fault system are still stored and accumulated in the form of lenses. The conclusion is the integration of remote methods that study various properties of the geological environment increase the possibility of operational and scientifically based identification of problems of flooding of the territory and allow to control safe activities in the zone of influence of man-made objects.

Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks

Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarcity of labelled GPR data. We evaluate the proposed model using Frechet Inception Distance (FID) evaluation index and compare it with other existing GAN models and find it outperforms the other two models on a lower FID score. In addition, the adaptability of the LSGAN-generated images for GPR data augmentation is investigated by YOLOv4 model, which is employed to detect rebars in field GPR images. It is verified that inclusion of LSGAN-generated images in the training GPR dataset can increase the target diversity and improve the detection precision by 10%, compared with the model trained on the dataset containing 500 field GPR images.

A Mutual Information-Based Self-Supervised Learning Model for PolSAR Land Cover Classification

Recently, deep learning methods have attracted much attention in the field of polarimetric synthetic aperture radar (PolSAR) data interpretation and understanding. However, for supervised methods, it requires large-scale labeled data to achieve better performance, and getting enough labeled data is a time-consuming and laborious task. Aiming to obtain a good classification result with limited labeled data, we focus on learning discriminative high-level features between multiple representations, which we call mutual information. As PolSAR data have multi-modal representations, there should have strong similarity between multi-modal features of the same pixel. In addition, each pixel has its own unique geocoding and scattering information. Hence, every pixel has great difference from other pixels in a specific representation space. Based on the above observations, this article proposes a mutual information-based self-supervised learning (MI-SSL) model to learn an implicit representation from unlabeled data. In this article, the self-supervised learning idea is first applied to PolSAR data processing. Furthermore, a reasonable pretext task, which is suitable for PolSAR data, is designed to extract mutual information for classification tasks. Compared with the state-of-the-art classification methods, experimental results on four PolSAR data sets demonstrate that our MI-SSL model produces impressive overall accuracy with fewer labeled data.

Simulation of Martian Near-Surface Structure and Imaging of Future GPR Data From Mars

Three upcoming Martian missions will deploy a ground-penetrating radar (GPR) to reveal the fine-resolution subsurface structure and dielectric properties of materials beneath the surface. Numerical forward simulations of radar echo using a model of the near-surface structure at the landing site can provide a valuable reference for processing and interpretation of future radar data collected on Mars. In this study, based on the geological information of the Jezero crater, a detailed stratigraphic model of the near-surface structure is derived, which includes several key features, for example, the randomness of the medium, terrain, and cracks. To identify correctly the reflections of subsurface interfaces and fractures from the radar image, a <inline-formula> <tex-math notation="LaTeX">$v(z$ </tex-math></inline-formula>) f-k migration is carried out, the performance of which is evaluated using the GPR data obtained near Antarctic Zhongshan Station since the electrical properties of Antarctic glaciers and Martian materials are to some extent comparable. The results in this work show that compared with common migration algorithm, the <inline-formula> <tex-math notation="LaTeX">$v(z$ </tex-math></inline-formula>) f-k method not only improves the clarity of radar image but also provides the permittivity profiles to infer the composition of the substrate, leading to a better understanding of Martian near-surface geology.

Multitemporal Polarimetric SAR Change Detection for Crop Monitoring and Crop Type Classification

The interpretation of multidimensional Synthetic Aperture Radar (SAR) data often requires expert knowledge. In fact, it requires to simultaneously consider several time series of polarimetric features to understand the physical changes of a target and its temporal evolution. To characterise the changes over time, Multitemporal Polarimetric SAR (MTPolSAR) change detection has been introduced in the literature in [1] and [2]. However, previous methods either only exploit intensity of changes or the resulting changed scattering mechanisms are not guaranteed to represent physical changes of the target. This paper presents a variation in the change detector used in [2] based on the difference of covariance matrices that characterise the polarimetric information, allowing for an intuitive representation and characterisation of physical changes of a target and its dynamics. We show the results of this method for monitoring growth stages of rice crops and present a novel application of the method for crop type mapping from MT-PolSAR data. We compare its performance with a neural network-based classifier that uses time series of PolSAR features derived from a target covariance matrix decomposition as input. Experimental results show that the classification performance of the proposed method and the baseline are comparable, with differences between the two methods in the overall balanced accuracy and the F1-macro metrics of around 2% and 3%, respectively. The method presented here achieves similar classification performances of a traditional PolSAR data classifier while providing additional advantages in terms of interpretability and insights about the physical changes of a target over time.

General Two-Stage Model-Based Three-Component Hybrid Compact Polarimetric SAR Decomposition Method

Polarimetric decomposition is an effective way for synthetic aperture radar (SAR) data interpretation. As for hybrid compact polarimetric (HCP) SAR, there are mainly two different types of decomposition methods, including wave-dichotomy-theorem-based and model-based. However, the existing methods are not universal because they cannot estimate volume scattering component accurately in different scenes. In order to solve this problem, a general two-stage model-based three-component HCP decomposition method (GTM) is proposed by involving Arii volume scattering model. In the first stage, a method is designed to identify the dominant scattering mechanism (surface, dihedral, and volume scattering mechanism) of each pixel according to the characteristics of the three scattering models. Then, the GTM method is solved when the three basic scattering mechanisms are dominant, respectively, in the second stage. Two simulated HCP datasets are used to verify the performance of the decomposition method. The results show that GTM can more accurately identify the dominant scattering mechanism in different regions than existing methods, and it can obtain closer results to the quad-polarimetric method.

Algorithm for processing and interpretation of ground-penetrating radar data in the study of permafrost geocryological structures

Relevance. Ground ice is one of unstable components of the cryolithozone and occupies 400–600 km3 in total. Changes in the conditions of heat transfer of ground ice under the influence of external or internal factors can lead to the formation of thermokarst sinkholes, solifluction mudslides, cryogenic landslides and other dangerous cryogenic phenomena in the frozen rock mass. Various methods are used to study underground ice. You can distinguish the method of GPR. Its advantages are efficiency and high detail of measurements. However, the process of identifying underground ice based on the results of processing and interpreting ground-penetrating radar data is currently not formalized in the form of an algorithm. Purpose of the work. Rapid mapping of geocryological structures requires the development of an appropriate algorithm (sequence of formalized procedures) for the processing and interpretation of ground penetrating radar data. Methodology of the algorithm is based on the analysis of characteristics of GPR traces, the use of fast Fourier transform to calculate the spectra of GPR traces and further analysis of the results. Results of the study. The developed algorithm will make it possible to quickly study the structural features of the frozen rock massif with the release of layer ice. The algorithm was tested on the results of physical and computer modeling of ground-penetrating radar measurements of rock mass with ice included. Conclusions. The use of algorithms for processing and interpretation of ground-penetrating radar data for the operational mapping of geocryological structures of frozen rock massifs makes it possible to reasonably and rationally apply a set of measures to prevent the occurrence of unfavorable geotechnical processes or to reduce economic losses when negative geocryological processes are activated.

Modeling the Dielectric Properties of Minerals From Crystals to Bulk Powders for Improved Interpretation of Asteroid Radar Observations

AbstractPlanetary radar has provided a growing number of data sets on the inner planets and near‐Earth and main belt asteroid populations in the solar system. Physical interpretation of radar data for inference of surface properties requires constraints on the constitutive parameters of the material making up a given surface. In this study, the complex permittivity of seven minerals as a function of frequency and porosity is measured using the coaxial transmission line method to determine the mixing equation that best describes the relationship between the real part of the complex permittivity of single mineral crystals and granular mineral powders. We find the Looyenga‐Landau‐Lifshitz and Bruggeman symmetric mixing equations to describe our experimental results with the highest accuracy. The variation in the real part of the permittivity of solid mineral crystals between different minerals is shown to depend on the grain density and the chemical composition of the minerals. These mixing relationships are incorporated into an asteroid radar model and used to calculate the porosity in the near‐surface of seven asteroids visited by robotic spacecraft using Earth‐based radar observations. The results of the asteroid radar model support the presence of significant porosity in the boulders on the surface of asteroid 101955 Bennu. This research highlights the ability of radar to measure the porosity on asteroid surfaces and provides theoretical and experimental justification for the inversion of permittivity to bulk density assumed by the asteroid radar model.