Data from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission have shown promise for the retrieval of soil moisture, and many soil moisture products using CYGNSS data have been developed. In this work, we present a merged product that combines several CYGNSS soil moisture products using a minimum variance estimator (MVE). The MVE identifies an optimal weighted averaging scheme based on the error covariance characteristics of the CYGNSS soil moisture products. The error covariance matrix is computed using two reference datasets: soil moisture data from the Soil Moisture Active Passive (SMAP) radiometer and in situ soil moisture data. The results from each of these provide insights into both the performance of the merged product and the individual input CYGNSS products. Overall, the merged product offers better performance than any individual CYGNSS product while also offering better temporal resolution than SMAP. The results of this work also demonstrate that the use of the MVE is a compelling technique for soil moisture applications.

Radiometers play a crucial role in providing accurate geo-physical information, relying heavily on precise calibration for both radiometric accuracy and spectral consistency. Radiometers consistently allocate time and hardware resources to calibration, resources that could otherwise be utilized for environmental sensing. In addition, calibration faces challenges such as frequency dependence and environmental influences, requiring to the need for innovative solutions. In this study, advancements in deep learning (DL) techniques are utilized, using NASA’s Soil Moisture Active Passive (SMAP) satellite data to create a DL-based radiometer calibrator. The use of 2-D spectral features as input in a convolutional neural network shows promising results with high correlation and low error. Notably, ancillary features like internal thermistor temperature prove accurate for estimating antenna temperature. This compensates for changes in receiver noise temperature and short-term gain fluctuations, even when there’s no reference load or noise diode power. The proposed calibration technique, emphasizing reduced reference information, holds significant potential for a higher number of antenna scene observations within a footprint.

Unmanned Aircraft Systems (UAS) play an essential role in providing high-resolution information for precision agriculture (PA). Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) from a UAS can provide higher spatial and temporal resolution for soil moisture (SM) retrievals. This study summarizes and analyzes of a three-year-long field campaign including comprehensive GNSS-R and ancillary data from crop fields. The field data collections were conducted on 210 by 110 m (2.31 ha) corn and cotton fields over 3 years from 2021 to 2023. The results indicate that high-resolution SM measurement can be achieved with a low-cost GNSS-R system onboard a mid-size UAS platform for use in PA applications.

Passive remote sensing through microwave radiometry has been utilized in Earth observation by estimating several geophysical parameters. Because of the low noise floor associated with the instrument (i.e., radiometer), the received geophysical emission is sampled in a protected band dedicated to remote sensing. This protected L-band occupying 1400-1427 MHz is also exciting and ideal for science because of lower attenuation from the atmosphere. This reason has also made this microwave region ideal for next-generation (xG) wireless communication. 5G cellular systems support two frequency ranges FR1 (0.45 GHz–6 GHz) and FR2 (24.45 GHz-52.6 GHz). Although operating bands are prohibited from conducting any up-link or down-link operations in the protected portion of the L-band, out-of-band (OOB) emissions can still have a significant impact on passive sensors because of the high sensitivity requirements related to science. This study will demonstrate a unique physical testbed that has the capability to observe in-band and OOB emissions in a protected anechoic chamber. Flexibility on transmitted waveforms and the potential to analyze raw measurements (IQ samples) of radiometers will help in designing onboard radio frequency interference (RFI) processing along with the coexistence of communication and passive sensing technologies.

The success of microwave radiometry depends on how accurately it can measure the natural emission of the Earth without the effects of unwanted signals. The consequence of unwanted signals in radiometers is known as radio frequency interference (RFI). The high intensity of these corrupted signals, along with wider bandwidth and longer duration, may jeopardize the overall success of a mission. These reasons resulted in a need for a robust RFI detection algorithm that will enable the mitigation of the contaminated portions of the measurements. Attributes related to RFI could be very dynamic, making it very difficult to detect with a particular algorithm. To address this issue, deep learning (DL) could be an attractive solution to detect RFI with the help of time-frequency analysis, i.e., spectrograms of the received measurement. This study aims to detect and localize RFI in a particular time-frequency bin of spectrograms with the help of DL to retrieve the non-contaminated portion of the measurements.

Satellite-based remote sensing observations play an important role in retrieving soil moisture over the earth’s surface. NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission has gained attention as it uses the Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) which can provide higher spatial and temporal resolution. Research is going on to improve retrieval algorithms using CYGNSS observation. In addition to the CYGNSS observations, different land surface products are leveraged to characterize the underlying surface conditions. The most commonly used features are from the Normalized Difference Vegetation Index (NDVI) and the Vegetation Water Content (VWC) from Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. Since the MODIS satellite operates on optical bands that can be greatly affected by cloud coverage, this study proposes using the SENTINEL-1 satellite which offers all-weather, day, and night measurement capability. This study utilized the SENTINEL-1 cross ratio of VH/VV as an alternative to MODIS-based vegetation indices. The results of the study showed that the SENTINEL-1 cross ratio of VH/VV can be significantly useful in CYGNSS-based SM retrieval models by including the effect of vegetation.

Passive microwave remote sensing plays an essential role in providing valuable information about the Earth’s surface, particularly for agriculture, water management, forestry, and other environmental fields. One of the key requirements for precision agricultural applications is the availability of field-scale high-resolution remote sensing data products. With the recent development of reliable unmanned aircraft systems (UAS), airborne deployment of remote sensing sensors has become more widespread to provide such products. With this in mind, we developed a UAS-based dual H-pol (horizontal) and V-pol (vertical) polarized radiometer operating in L-band (1400-1427 MHz). The custom dual-polarized antenna acquires surface emission response through a software-defined radio (SDR). This SDR-based system provides full control over the data acquisition parameters such as bandwidth, sampling frequency, and data size. Radio frequency interference (RFI) poses a significant challenge in radiometric measurements, requiring post-processing of the full-band radiometer data to identify and eliminate RFI-contaminated measurements, thus ensuring accurate Earth emission readings.. In this paper, we implemented near-real-time RFI detection onboard during the flight to accelerate the post-processing. The altitude and the speed of the UAS can be varied to achieve desired ground resolution for the measurement. This paper presents the full custom design and development of a lightweight SDR-based UAS-borne radiometer for precision agriculture. Additionally, we introduce the concept of an agile radiometer implemented from a small UAS that can serve as a testbed for both current and future spaceborne missions.

Soil Moisture Active Passive (SMAP) is a NASA's earth observing satellite which is used for global scale soil moisture measurement and differentiating frozen/thawed state. It is employed in 1400–1427 MHz protected band which uses L-Band radiometer for the quantification. But increasing number of wireless equipment such as air surveillance radar signals and 5G communication are making it harder to protect the radiometer microwave sensing in this secured spectrum. These technologies are responsible for the Radio Frequency Interference (RFI) in SMAP's passive observation. In this study, a novel deep learning architecture is developed that uses convolutional neural network (CNN) to predict RFI. Our model uses SMAP's level 1A raw antenna counts as well as level 1B quality flags to dynamically label these antenna raw measurements as RFI contaminated and RFI free footprints. This example study shows around 94% accuracy in detecting RFI and such result may recommend a lucrative technique in detecting RFI.

This paper explores the retrieval of snow water equivalent (SWE) through the use of machine learning techniques and active radar data collected over the 2020 SnowEx campaign. The retrieval makes use of active radar measurements provided by NASA's SWESARR instrument for direct sensing of snowpack sensitivity to SWE. The example results show that an RMSE of 1.93 cm can be obtained through a combined use of SAR data with sufficient ancillary data. Such results may indicate successful SWE estimation by means of pairing spaceborne SAR measurements with sufficient auxiliary information.

This paper presents a practical technique to estimate surface reflectivity using two sets of Global navigation satellite sys-tem (GNSS) measurements. A down-facing smartphone (at-tached to a ground plate) on a Unmanned Aircraft Systems (UAS) collects reflected signals while another identical phone is located on the ground that provides reference data in an open area. Both drone and ground units are rotated with a constant speed to mitigate radiation pattern irregularities of smartphone's in-built GNSS antenna. Reflectivity at vari-ous elevation angles and locations are obtained by taking the logarithmic difference between measurements (GNSS carrier-to-noise density ratio C / No) on the UAS and in the open area. The estimated reflectivity can be utilized for quantification of surface soil moisture and vegetation water content that is needed for various precision agriculture and spaceborne product validation efforts.

Hyperspectral sensors acquire data with a large number of spectral bands. These large number of bands make the processing computationally expensive and difficult in many real-world applications. In addition, with the spatial dimensions, the volume of the data creates problems for cases where the applications permit only limited resources both in terms of hardware computational and storage requirements. To avoid these limitations, band selection plays very pivotal role for many applications. Existing techniques utilize redundancy, clus-tering, sparsity, ranking type criteria for band selection. We propose an end-to-end deep learning pipeline together with a constrained measurement learning structure to select bands in a data driven manner to optimize directly the final task, which is the classification accuracy for this paper. Our results on a publicly available hyperspectral dataset show that the proposed data-driven approach provides higher classification accuracy compared to the existing state-of-art methods for the same number of bands utilized.

NASA Cyclone Global Navigation Satellite System (CYGNSS) mission has gained attention within the land remote sensing community for estimating soil moisture (SM) by using the Global Navigation System Reflectometry (GNSS-R) technique. CYGNSS constellation generates Delay-Doppler Maps (DDM) that contain valuable earth surface information from GNSS reflection measurements. Existing approaches use predefined features from DDMs to estimate SM. This pa-per presents a deep-learning framework to learn optimal features from DDMs for estimating SM. The proposed approach is applied over the Continental United States (CONUS) by leveraging CYGNSS DDM observations with ancillary re-motely sensed geophysical data. The model is trained and evaluated using the Soil Moisture Active Passive (SMAP) mission's enhanced SM products at a$9\text{km}\times 9\text{km}$resolution with vegetation water content less than$5kg/m^{2}$. The mean unbiased root-mean-square difference (ubRMSD) between CYGNSS and SMAP SM retrievals from 2017 to 2020 is 0.0362$m^{3}/m^{3}$with a correlation coefficient of 0.9309 over 5-fold cross-validation.

The past decade has seen great advancements in speech recognition for control of interactive devices, personal assistants, and computer interfaces. However, deaf and hard-of-hearing (HoH) individuals, whose primary mode of communication is sign language, cannot use voice-controlled interfaces. Although there has been significant work in video-based sign language recognition, video is not effective in the dark and has raised privacy concerns in the deaf community when used in the context of human ambient intelligence. RF sensors have been recently proposed as a new modality that can be effective under the circumstances where video is not. This article considers the problem of recognizing a trigger sign (wake word) in the context of daily living, where gross motor activities are interwoven with signing sequences. The proposed approach exploits multiple RF data domain representations (time-frequency, range-Doppler, and range-angle) for sequential classification of mixed motion data streams. The recognition accuracy of signs with varying kinematic properties is compared and used to make recommendations on appropriate trigger sign selection for RF-sensor-based user interfaces. The proposed approach achieves a trigger sign detection rate of 98.9% and a classification accuracy of 92% for 15 ASL words and three gross motor activities.

Current research in the recognition of American Sign Language (ASL) has focused on perception using video or wearable gloves. However, deaf ASL users have expressed concern about the invasion of privacy with video, as well as the interference with daily activity and restrictions on movement presented by wearable gloves. In contrast, RF sensors can mitigate these issues as it is a non-contact ambient sensor that is effective in the dark and can penetrate clothes, while only recording speed and distance. Thus, this paper investigates RF sensing as an alternative sensing modality for ASL recognition to facilitate interactive devices and smart environments for the deaf and hard-of-hearing. In particular, the recognition of up to 20 ASL signs, sequential classification of signing mixed with daily activity, and detection of a trigger sign to initiate human-computer interaction (HCI) via RF sensors is presented. Results yield %91.3 ASL word-level classification accuracy, %92.3 sequential recognition accuracy, 0.93 trigger recognition rate.

Global soil moisture mapping at high spatial and temporal resolution is important for its related meteorological, hydrological, and agricultural applications. Using the L-band signals, several satellite-based microwave sensors are providing global soil moisture retrievals at a spatial resolution of about 40 km and a revisit time of 2–3 days. Recent research shows that the forward scattered Global Navigation Satellite System (GNSS) signals at L-band can convey high-resolution information of land surface conditions, including surface soil moisture. However, these signals are often affected by complex land surface characteristics and the bistatic nature of GNSS-R technique, leading to nonlinear relation between the signals and surface soil moisture. In this work, a machine learning (ML) approach is used to map quasi-global soil moisture from Cyclone GNSS (CYGNSS) observables. Specifically, several land surface parameters are obtained and used in combination with CYGNSS data in the ML model by using the Soil Moisture Active Passive (SMAP) data as reference. A good performance of the ML method is achieved with median ubRMSDs of 0.0426 m3/m3and 0.034 m3/m3for global coverage and regions with vegetation water content less than 4 kg/m2, respectively. Moreover, an independent evaluation of the CYGNSS data against in-situ measurements suggests that the overall accuracy of CYGNSS soil moisture is comparable with SMAP data. With an increased sampling frequency of CYGNSS, the generated products can supplement current global soil moisture database. In addition, the ML-based CYGNSS products are published via a website portal for future users11https://www.gri.msstate.edu/research/ssm/.

High Spatio-temporal soil moisture is essential for many meteorological, hydrological, and agricultural applications and studies. Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising opportunity for high-resolution soil moisture retrievals. NASA's Cyclone Global Navigation Satellite System is a preeminent GNSS-R application that offers high spatial and temporal resolution observations from Earth's surface. However, the quasi-random sampling of land surface by the CYGNSS constellation circumvents obtaining fully observed daily soil moisture predictions. This work investigates multidimensional spatial and temporal interpolation of the CYGNSS soil moisture estimates using methods such as linear, nearest, and natural interpolation. The results indicate that the interpolation error (RMSE) was 0.032$m^{3}/m^{3}$, 0.038$m^{3}/m^{3}$, and 0.030$m^{3}/m^{3}$for linear, nearest, and natural interpolation, respectively. The results also show that interpolated and observed CYGNSS SM values have the similar performance metrics when validated with the SMAP 9-km gridded SM product.

Frequencies in the Very-High (VHF) to Ultra-High (UHF) range show potential for the remote sensing of soil moisture within the root-zone. This paper analyzes the Cramer-Rao Lower Bound (CRLB) for estimating soil moisture parameters using the SoOp Coherent Bistatic Scattering Model (SCoBi). CRLB defines the best achievable estimation variance for any unbiased estimator, hence allowing to identify optimal measurement configurations for soil moisture estimation. For different frequency, polarization and direction values SCoBi can model specular scattering surface reflection coefficients. Initial CRLB analysis are carried out using different combinations of a maximum of 120 measurements. The results indicate that surface soil moisture can reliably be measured while soil moisture values at 40 cm depth can be estimated within ± 4% accuracy if the surface and subsurface soil moisture is below 32.5% VSM. Its also shown that dual frequency measurements of soil moisture can greatly reduce the CRLB compared to using a single frequency.

Raw Global Navigation Satellites Systems (GNSS) data have been directly accessible from mass-market devices running the Android Nougat (or newer) operating system since late 2016. The availability of GNSS raw data made possible to investigate feasibility of using in-built GNSS chipsets within smartphone devices as passive radar receivers for the purpose of land remote sensing. In this study, we integrate smart-phones into small Unmanned Aircraft Systems (UAS) to collect reflected GNSS raw data for the purpose of mapping top 5-cm soil moisture. The reflected GNSS signals collected by the smartphones show high correlation with spatial features on the ground such as ponds, crops, and small creeks. To determine the quality of smartphone in-built antenna and chipset, we conducted several experiments. The results show that (1) the radiation pattern of smartphone's GNSS antenna are observed to be highly irregular, but time-invariant, and (2) internal GNSS chip produces observables of sufficient quality when the GNSS smartphone reflected signals are compared with a high quality custom-built dual channel receiver. This paper summarizes the experimental findings and challenges that need to be resolved in order to use the GNSS-Reflectometry (GNSS-R) technique via ubiquitous smartphones from small UASs.

High spatio-temporal soil moisture is critical for the understanding of land-atmosphere interactions and affects meteorological, hydrological and agricultural applications. Currently, most satellite-based microwave sensors provide global soil moisture retrievals at ~40 km spatial and 2-3 days temporal resolution. Using the forward scattered L-band Global Navigation Satellite System (GNSS) signals, surface soil moisture can be estimated at higher spatial and temporal scales. However, due to the complex land surface characteristics and bistatic nature of GNSS signals, the retrieval algorithms for deriving surface soil moisture from GNSS signals are still under development. In this work, a machine learning (ML) algorithm has been used for estimating soil moisture from Cyclone Global Navigation Satellite System (CYGNSS) measurements. The in-situ data from International Soil Moisture Network and global soil moisture data from Soil Moisture Active Passive (SMAP) have been deployed as the reference data in the ML algorithm. In particular, various remote sensing-based land surface parameters have been included and facilitate a robust soil moisture retrieving process. The proposed approach has achieved an ubRMSD of 0.0523 m3/m3between the retrieved soil moisture from CYGNSS and in-situ measurements in a 5-fold cross-validation over 129 ground-based soil moisture sites, suggesting a satisfactory performance of the ML-based approach. Moreover, the global median ubRMSD of 0.042 m3/m3is obtained between SMAP and CYGNSS ML predictions. Surface soil moisture can be retrieved at ~9 km spatial and 1-2 days temporal scales through the presented framework.

The spectrum scarcity at sub-6 GHz spectrum has made millimeter-wave (mmWave) frequency band a key component of the next-generation wireless networks. While mmWave spectrum offers extremely large transmission bandwidths to accommodate ever-increasing data rates, unique characteristics of this new spectrum need special consideration to achieve the promised network throughput. In this work, we consider the off-grid targets (basis mismatch) problem for mmWave communications. The off-grid effect naturally appears in compressed sensing (CS) techniques adopting a discretization approach for representing the angular domain. This approach yields a finite set of discrete angle points, which are an approximation to the continuous angular space, and hence degrade the accuracy of related parameter estimation. In order to cope with the off-grid effect, we present a novel parameter-perturbation framework to efficiently estimate the channel and the covariance for mmWave networks. The proposed algorithms employ a smart perturbation mechanism in conjunction with a low-complexity greedy framework of simultaneous orthogonal matching pursuit (SOMP), and jointly solve for the off-grid parameters and weights. Numerical results show a significant performance improvement through our novel framework as a result of handling the off-grid effects, which is totally ignored in the conventional sparse mmWave channel or covariance estimation algorithms.

The potentiality of Signals of Opportunity (SoOp) over land can be investigated by advanced forward and inverse modeling and simulation tools to provide viable measurements for Earth science data products over land. This research investigates various inversion techniques that can leverage SoOp sources for land-based Earth science measurements by applying them to simulated soil moisture profiles over bare- and vegetated- soils. Forward modeling is accomplished using Mississippi State University’s Signals of Opportunity Coherent Bistatic Scattering Model (SCoBi), a new, open-source electromagnetic scattering model that can determine coherent received signals at a receiving antenna through application of Maxwell’s equations at discrete scattering soil layer boundaries in conjunction with the distorted Born approximation to describe vegetation propagation and scattering. The results of the forward model are used in various inverse methods to investigate the potentiality of using multiple SoOp sources for Soil Moisture Profile (SMP) retrieval. Multiple SMPs are analyzed by SCoBi to determine the sensitivity of soil moisture variation to SoOp transmitter characteristics such as polarization and elevation angle. Simultaneously, SoOp measurements conducted at Purdue University’s Agronomy Center for Research and Education (ACRE) are used to determine the impact that changes in both physical SMPs and vegetation canopies have on the scattered SoOp. The characteristics of the scattering surfaces, vegetation, and SMPs at the ACRE facility are modeled within SCoBi to observe patterns and relationships captured in reflectivity measurements that are caused by vegetation growth periods as well as rain and drought effects manifested by changing SMPs.

NASA’s Cyclone Global Navigation Satellite System (CYGNSS) receives the forward scattered L-band GNSS signals between ±37° latitudes. The received signals over land are previously shown to be highly sensitive to surface soil moisture (SM). Assuming coherent reflections over land, the CYGNSS bistatic radars can provide a spatial resolution of around 7 × 0.5 km and a revisit time of 1-2 days. SM retrieval at such a high spatio-temporal resolution could help advance hydrometeorology and agriculture applications. This study examines case scenarios for determining the relations of CYGNSS-deliverables and available SM data as well as specifying the requirements for CYGNSS-derived SM retrieval. Preliminary results demonstrate moderate correlation between CYGNSS measurements and SMAP SM. However, the results also show that accurate derivation of high spatio-temporal SM products from CYGNSS measurements is a challenging problem due to the heterogeneous land covers, varying topography, and surface roughness.

In spite of more than 50 years of airborne radar soundings of Antarctic ice by the international community, there are still large gaps in ice thickness data. We propose a CubeSat satellite mission for complete sounding and imaging of Antarctica with 50 CubeSats integrated with a VHF radar system to sound the ice and image the ice-bed. One of the major challenges in orbital sounding of ice is off-vertical surface clutter that masks weak ice-bed echoes. We must obtain fine resolution both in the along track and cross track directions to reduce surface clutter. We can obtain fine resolution in the along track direction by synthesizing a large aperture by taking advantage of the forward motion of a satellite. However, we need a large antenna-array to obtain fine resolution in the cross track direction. We propose a train of 50 CubeSats with optimized offset position to obtain a 500-m long aperture and also coherently combine data from multiple passes of the train to obtain a very large aperture of 1–2 km in the cross track direction. Our initial analysis shows that we can obtain measurements with horizontal resolution of about 200 m and vertical resolution of about 20 m. The CubeSat will carry a transmitter and receiver with peak transmit power of about 50 W. We will synchronize all transmitters and receivers with a Ka-band system that serves as a communication link between the earth and Cubesats to downlink data and as command and control for the CubeSats.

Synthetic Aperture Radar (SAR) has significant importance in many remote sensing applications. Errors due to the platform motion or measurement model uncertainties can cause degradations in the constructed SAR images. Most of the methods deal with the phase errors which cause defocusing on the image. For efficient processing of the measurements, they discretize the fast time-slow time plane and then employ autofocus algorithms on this discrete grid. However, the reflectors which are not placed exactly on the grid degrade the image quality considerably. This is the most probable case in the practical SAR operation and it causes blur or spark like affects on the image. This is called the off-grid target problem. In this work, a Compressed Sensing based technique is developed which constructs spotlight mode SAR image, handles the off-grid target problem and makes autofocus simultaneously. A gradient descent type iterative solution is used.

Micro-Doppler radar signatures have great potential for classifying pedestrians and animals, as well as their motion pattern, in a variety of surveillance applications. Due to the many degrees of freedom involved, real data need to be complemented with accurate simulated radar data to be able to successfully design and test radar signal processing algorithms. In many cases, the ability to collect real data is limited by monetary and practical considerations, whereas in a simulated environment, any desired scenario may be generated. Motion capture (MOCAP) has been used in several works to simulate the human micro-Doppler signature measured by radar; however, validation of the approach has only been done based on visual comparisons of micro-Doppler signatures. This work validates and, more importantly, extends the exploitation of MOCAP data not just to simulate micro-Doppler signatures but also to use the simulated signatures as a source ofa prioriknowledge to improve the classification performance of real radar data, particularly in the case when the total amount of data is small.

A novel expectation maximization based matching pursuit (EMMP) algorithm is presented. The method uses the measurements as the incomplete data and obtain the complete data which corresponds to the sparse solution using an iterative EM based framework. In standard greedy methods such as matching pursuit or orthogonal matching pursuit a selected atom can not be changed during the course of the algorithm even if the signal doesn't have a support on that atom. The proposed EMMP algorithm is also flexible in that sense. The results show that the proposed method has lower reconstruction errors compared to other greedy algorithms using the same conditions.

Ground-penetrating radars (GPRs) show promising results for subsurface buried target detection. However, the online detection as the GPR scans a region is a difficult problem, and the best performance requires to know the characteristics of the clutter and noise which affect the used test statistics in detection. In statistical detection methods developed for GPR, mostly Gaussian clutter assumption is used mainly due to its simplicity. In this letter, a low-complexity goodness-of-fit test suitable for online GPR detection is applied to experimental GPR data sets to determine the best clutter distribution defining the data test statistic. The distributions of A-scan energies after background subtraction are determined from different experimental data taken over notarget regions. The obtained results show that the GPR clutter for the tested experimental data is mainly gamma distributed than Gaussian. The demonstrated procedure can be applied to any GPR data set for the determination of the background distribution, for target detection, and for selecting detection thresholds properly for GPR applications and more realistic GPR clutter generation simulations.

Recent results in compressive sensing (CS)-based subsurface imaging showed that, if the target space is sparse, it can be reconstructed with many fewer number of measurements from a stepped frequency ground penetrating radar (GPR). One of the problems in this CS subsurface imaging is the surface reflections. Previous work dealed with surface reflections using a model dictionary generated from the target space excluding specifically the near surface region. While this works fine for some applications, it might lack the imaging of near surface targets. Removing the surface reflections with standard methods is not directly applicable since only very few and random measurements in the frequency domain are taken. This letter provides a simple surface reflection method using compressive measurements, that can be used for nonplanar surfaces. It is observed in both simulated and experimental GPR data that the CS-based imaging method is more robust and can find shallow targets using the surface-reflection-removed data.

Sparsity of target space in subsurface imaging problem is used within the framework of the compressive sensing (CS) theory in recent publications to decrease the data acquisition load in practical systems. The developed CS based imaging methods are based on two important assumptions; namely, that the speed of propagation in the medium is known and that potential targets are point like targets positioned at discrete spatial points. However, in most subsurface imaging problems these assumptions are not always valid. The propagation velocity may only be known approximately, and targets will generally not fall on the grid exactly. In this work, the performance of the CS based subsurface imaging methods are analyzed for the above defined problems and possible solutions are discussed.

Joint processing of sensor array outputs improves the performance of parameter estimation and hypothesis testing problems beyond the sum of the individual sensor processing results. When the sensors have high data sampling rates, arrays are tethered, creating a disadvantage for their deployment and also limiting their aperture size. In this paper, we develop the signal processing algorithms for randomly deployable wireless sensor arrays that are severely constrained in communication bandwidth. We focus on the acoustic bearing estimation problem and show that when the target bearings are modeled as a sparse vector in the angle space, low dimensional random projections of the microphone signals can be used to determine multiple source bearings by solving an ℓ1-norm minimization problem. Field data results are shown where only 10 bits of information is passed from each microphone to estimate multiple target bearings.

Compressive Sensing (CS) is an emerging area which uses a relatively small number of non-traditional samples in the form of randomized projections to reconstruct sparse or compressible signals. This paper considers the direction-of-arrival (DOA) estimation problem with an array of sensors using CS. We show that by using random projections of the sensor data, along with a full waveform recording on one reference sensor, a sparse angle space scenario can be reconstructed, giving the number of sources and their DOA’s. The number of projections can be very small, proportional to the number sources. We provide simulations to demonstrate the performance and the advantages of our compressive beamformer algorithm.

Compressive Sensing (CS) uses a relatively small number of non-traditional samples in the form of randomized projections to reconstruct sparse or compressible signals. The Hough transform is often used to find lines and other parameterized shapes in images. This paper shows how CS can be used to find parameterized shapes in images, by exploiting sparseness in the Hough transform domain. The utility of the CS-based method is demonstrated for finding lines and circles in noisy images, and then examples of processing GPR and seismic data for tunnel detection are presented.

A new data acquisition and imaging method exploiting the sparsity of the target space is presented for ground penetrating radar (GPR) imaging. Sparsity is enforced by solving a convex l1minimization problem which uses a very small number of random measurements. The method can greatly reduce the data acquisition time while producing sparse target space images. Simulation and experimental data results are provided to show that the method has excellent resolution and is robust to noise and random spatial sampling.

The Radon Transform (RT) is known to be effective in detecting lines in noisy images, but it is not capable of detecting curves unless the curve parametrization is given. In this paper, partial Radon transforms (PRT) are investigated as a tool to detect curved features such as underground tunnels in ground penetrating radar (GPR) images. The algorithm applies the Radon Transform to small batches of the total image and updates the tunnel position parameters as new batches are used. Missing data, as well as finding the ends of tunnels can be handled with the proposed algorithm. Performance analysis is given for various signal-to-noise ratios (SNR) and batch sizes. The effect of the curvature level on the performance is also analyzed.

A multistatic ground-penetrating radar (GPR) system has been developed and used to measure the response of a number of targets to produce data for the investigation of multistatic inversion algorithms. The system consists of a linear array of resistive-vee antennas, microwave switches, a vector network analyzer, and a 3-D positioner, all under computer control. The array has two transmitters and four receivers which provide eight bistatic spacings from 12 to 96 cm in 12-cm increments. Buried targets are scanned with and without surface clutter, which is a layer of rocks whose spacing is empirically chosen to maximize the clutter effect. The measured responses are calibrated so that the direct coupling in the system is removed, and the signal reference point is located at the antenna drive point. Images are formed using a frequency-domain beamforming algorithm that compensates for the phase response of the antennas. Images of targets in air validate the system calibration and the imaging algorithm. Bistatic and multistatic images for the buried targets are very good, and they show the effectiveness of the system and processing.

To investigate the problem of detecting and imaging underground tunnels, an experimental system that utilizes seismic waves has been constructed. Seismic reflections from the tunnel are transformed into a 3D image using a synthetic aperture time-delay backprojection algorithm. Results from experimental data show that the tunnel is directly visible in the backprojected image. Nevertheless, tunnels with low signal to noise ratio (SNR) are located using 2D and 3D Radon Transforms followed by a detection algorithm. A simulation is performed on the performance of the Radon transform for detecting lines in noisy images and it is shown how lines in very low SNR images can be detected. Also it is observed that longer lines have higher probability of detection at the same noise level.

An experimental system to collect co-located ground penetrating radar (GPR) and seismic data was developed to investigate possibilities of using the sensors individually or in a cooperative manner to detect shallow tunnels. These sensors were chosen because they sense very different physical properties. The seismic sensor is sensitive to the differences between the mechanical properties of a tunnel and the soil while the GPR is sensitive to the dielectric properties. Raw and processed data from both sensors are presented.

The imaging of subsurface targets using ground penetrating radar (GPR) is becoming an increasingly important area of research. Conventional image formation techniques expend large amounts of computation to resolve a region fully, even a region of clutter. However, by using multi-resolution techniques, e.g, quadtree algorithms, potential targets and clutter can be discriminated in a computationally efficient way. Prior work has focused on the development of 2D quadtree algorithms for surface targets. For mine detection, target depth adds another dimension; thus, we have developed a 3D quadtree algorithm, and applied a multi-stage detector that uses the energy change between quadtree stages to discriminate target and clutter regions. This algorithm is then tested on computer-generated data, as well as experimental data collected from a model mine field. Results show that target location information can be obtained even under near field and small aperture conditions.

An experimental system to collect co-located ground penetrating radar (GPR), electromagnetic induction (EMI), and seismic data was developed to investigate the possibility of using the sensors in a cooperative manner and to investigate the benefits of the fusion of the sensors. These sensors were chosen because they can sense a wide range of physical properties. The seismic sensor is sensitive to the differences between the mechanical properties of a landmine and the soil while the GPR is sensitive to the dielectric properties, and the EMI sensor is sensitive to the conductivity.