- Open Access
Detection of buried objects using reflected GNSS signals
© Notarpietro et al.; licensee Springer. 2014
- Received: 20 December 2013
- Accepted: 14 August 2014
- Published: 27 August 2014
The use of reflected Global Navigation Satellite System (GNSS) signals for sensing the Earth has been growing rapidly in recent years. This technique is founded on the basic principle of detecting GNSS signals after they have been reflected off the Earth's surface and using them to determine the properties of the reflecting surface remotely. This is the so-called GNSS reflectometry (GNSS-R) technique. In this paper, a new application regarding the detection of metallic buried objects is analyzed and it is validated through several experimental campaigns. Although the penetration depth of GNSS signals into the ground is not optimal and depends on the soil moisture, GNSS signals can likely interact approximately with the first 10 cm of the ground and therefore can be reflected back by any metallic object buried on the first terrain layer. A very light and low-cost GNSS receiver prototype based on a software-defined radio approach was developed. This receiver can be used as a payload on board small drones or unmanned aerial systems to detect metallic objects (mines or other explosive devices). A signal processing tool based on an open-loop GNSS signal acquisition strategy was developed. The results of two experiments which show the possibility of using GNSS-R signals to detect buried metallic objects and to provide an estimate of their dimensions are discussed.
- GNSS reflectometry
- Software-defined radio
- Signal to noise ratio
Remote sensing using Global Navigation Satellite System (GNSS) signals (which include, for example, the US GPS and its updates, the Russian GLONASS, the future European Galileo, the Chinese COMPASS) has become more and more popular in the last few decades to analyze the characteristics of the electromagnetic waves in the media in which they propagate in. Applications for water vapor monitoring and atmospheric/ionospheric profiling are nowadays operatively adopted (a review is provided in  for ground-based applications and in  for space-based applications). Another application has recently emerged: the use of reflected GNSS signals to extract information about the Earth's surface, named GNSS reflectometry (GNSS-R) [3, 4].
The concept was first put forward as an alternative technique for ocean altimetry . Later, the same principle was demonstrated as a useful tool to sense ocean roughness . Exploiting a bistatic geometry approach, the GNSS satellites act as transmitters while an aircraft or a low Earth orbit satellite is the receiving platform. Comparing it with other existing satellite scatterometric, radiometric, and radar applications, GNSS-R remote sensing has several advantages. Firstly, thanks to the global and full-time coverage provided by GNSS satellites, the use of these signals as sources of opportunity allows very dense multi-static radar measurements at L band. Secondly, its passive working principle requires no transmitters except GNSS satellites, thus enabling the system to be light, compact, and cheap. Thirdly, since L band signals are used, the technique works in all-weather conditions and is suitable for altimetric applications (see e.g. [7, 8]) and for sensing nearly all surfaces, such as sea state and wind over sea (see e.g. [9–13]), snow (see e.g. [14–16]), vegetation coverage (see e.g. [17–22]), and soil moisture (see e.g. [23–26]).
A new application based on the possibility of detecting the presence of an object on the terrain or just under it, exploiting the penetration capabilities of electromagnetic energy within the soil, which are inversely proportional to the carrier frequency, is analyzed in this paper. One current application is in the military field, in particular, to detect the presence of improvised explosive devices (IEDs) and pressure-activated mines. Mines and IEDs are often hidden on the terrain or inside the vegetation or are buried within the first few centimeters below the surface, since their devastating effects depend of course on their insertion depth.
L band signals (GNSS carrier frequencies are within this band) are not impacted by atmospheric attenuation and normally have a good penetration through vegetation . At 1.5 GHz, the penetration depth varies from approximately 10 cm to 1 m for soil condition ranging from saturated to dry. In practice, the L band signal can interact with the first 10 cm, depending on the soil moisture level and incidence direction [28, 29]. In particular, in the case of almost dry soil, the penetration depth of active systems like GPS or a SAR was found to be around 10 cm  or 7 cm  respectively. Accordingly to , for passive L band remote sensing, the penetration depth varies from 10 cm to 1 m depending on whether the soil is wet or dry. These values are upper-bound values that can be used when the soil is homogeneous, as in the case of our first experiment (dry or wet sand). With a nonuniform moisture profile, a ‘soil moisture sensing depth’ definition  could be used and its approximation of one tenth of a wavelength in the medium would lead to less than 2 cm at 1.4 GHz. However, the penetration depth is strongly influenced by the soil density, soil moisture, and composition, and many models of soil can be considered and more realistic evaluation performed.
For the detection of mines that are hidden in the superficial layer of the ground (explosive devices are hidden in the first few cm below the surface in order to make their devastating effects as effective as possible), this penetration capability is enough. Generally, complicated and expensive devices are used to detect explosive objects [32, 33]; most of them work very well, but they need the human presence on the field to move the detector.
In this paper, the capability of GNSS-R signals to detect buried metallic objects is investigated through the use of a very simple and low-cost software receiver. This receiver is relatively light and can be mounted on board a remotely controlled unmanned aerial vehicle (UAV), thus avoiding the human presence in the field. The receiver was connected to a left-hand circularly polarized (LH) antenna to collect signals reflected from the ground. Surface roughness was not taken into account and the reflected signal power was estimated considering only coherent power. An open-loop approach was used for deriving signal to noise ratio (SNR) time series related to the reflected GPS signals.
Two prototypes were developed. The first was a software receiver, and the second was a more compact prototype suitable for use on board UAVs based on a Hackberry board to manage the receiver front end and store the raw data. The post-processing was done using a standard laptop. Several measurement campaigns were carried out with and without a metal object consisting of a metal plate. The first measurement campaign described in this work was performed in static conditions on sandy terrain to check the functionality of the system and the sensitivity of the results to the presence of the metal obstacle. In the second measurement campaign, the antenna moved along a given path, mimicking a flight. The results obtained highlight the possibility of using GNSS-R signals not only to detect buried metallic objects but also to estimate their dimensions.
This paper is organized as follows. In ‘Section 2,’ the microwave properties of soil and the potential of buried object identification are described. In ‘Section 3,’ the receiver hardware and the signal processing and post-processing are detailed. The various measurement campaigns and results are discussed in ‘Section 4,’ while in ‘Section 5,’ our conclusions and future work are highlighted.
The dielectric properties of wet soil have been studied by several authors (e.g. [27, 34]). These properties depend on water content and soil texture and on the carrier frequency of the signal used for monitoring purposes. The high dielectric constant of water significantly increases both the real and imaginary parts of the soil's dielectric constant as the water volumetric concentration increases. The dependence on soil type (or ‘texture’) is due to the different percentages of water bound to the surface of the different particles characterizing the soil. Bound water particles exhibit less freely molecular rotation at microwave frequencies and hence are characterized by smaller dielectric effects than the free water in the pore spaces. This is most evident in clay soils, which have greater particle surface areas and affinities for binding water molecules and hence are capable of holding greater percentages of bound water. The dependence of dielectric constant for a sandy soil on the signal carrier frequency is reported in . The real part is almost constant below 5 GHz, while the imaginary part is strongly frequency dependent. As reported in , this frequency dependence can be taken into account considering the penetration depth which depends on the moisture volumetric concentration and on the wavelength. At the L1 carrier frequency of the GPS signal (1,575.42 MHz), penetration depths decrease from 1 m to 10 cm, from dry soil to 30% water concentration. The penetration depth also depends on the elevation angle of the antenna. Since the nadir incidence is the best case, in our experiments, the antenna boresight was aligned very close to the nadir direction (approximately 5° off the nadir).
A summary of GPS system characteristics can be found in . Each GPS satellite broadcasts a carrier signal at 1,575.42 MHz, referred to as ‘L1,’ modulated by a civilian code (the so-called Coarse Acquisition code). Additionally, another code is broadcasted through a carrier frequency of 1.2276 GHz (L2) for military use, but reception of this signal requires complicated signal processing since it is encrypted. Even if at the time of the experiment few satellites started the transmission of the new civilian L2C signal, all the algorithms were based on the processing of the Coarse Acquisition (C/A) code. Therefore, only the GPS L1 carrier signals were used in our bistatic radar remote sensing experiment. The signals are encoded with timing and navigation information and transmitted with right-hand circular polarization (RH). The receiver can then calculate the positions of the transmitting satellites and use this information to calculate its own position and GPS time. A low-gain, quasi-hemispherical, zenith patch antenna is normally used to receive the direct signals. The GPS signals are also reflected off by the Earth's surface and can be received by a nadir-viewing antenna at a further delay with respect to the direct signal. After reflection, the scattered signal is predominantly LH for typical incidence far away from the Brewster angle. A low-gain, quasi-hemispherical, LH nadir antenna was used to measure the scattered signal. This antenna was chosen in order to have more flexibility in the measurements of signals characterized by different angles of incidence and because the geometry slowly changes with transmitter and receiver positions. Even if the cross-pol level of our antenna was not very good (approximately -15 dB), the RH component of the reflected signals (generated by scattering phenomena inside the glistening zone) is expected to be from -10 to -20 dB lower than the LH one. This means that the contribution due to the RH power available at the output of the LH antenna is a very small (and negligible) fraction of the wanted LH component. Other important figures of merit to be considered for the choice of the antenna are the half-power beamwidth (HPBW; and its projection on the ground, i.e., the antenna footprint) and the entire antenna's radiation pattern. The HPBW should be as wide as possible, in order to be able to contemporaneously acquire as many reflected signals as possible. The signals can then be easily separated on the base of the Pseudo Random Noise (PRN) code modulating the GPS L1 frequency (called C/A code), which uniquely characterizes the transmitted signal.
It has to be noted that only a portion of the footprint will be ‘sensitive’ to the reflected signal, namely the first Fresnel zone, which is the projection on the ground of the first Fresnel ellipsoid defined considering the geometry and the wavelength . The majority of the reflected power is generated within this area, particularly when the terrain can be considered flat at the used wavelength. If scattering over a rough surface occurs, a wider area (the so-called glistening zone) should be taken into account.
Even though the antenna allows simultaneous reception of both the polarized components of the reflected signal, only the LH one was processed in these experiments. The processing of the RH component can provide some interesting contribution to minimize surface roughness effects when the goal is to remotely sense some geophysical parameter of the surface. The hypothesis that for moderately rough surface the ratio of two orthogonal polarizations does not depend on the surface roughness was formulated by . Recently, the fact that both reflection coefficients for reflected LH and RH are sensitive to surface roughness but their ratio is seen to be independent from the roughness was experimentally proved by .
In our case, the goal was to detect objects with some metallic part, just under the ground surface. In this situation, the signal received after the scattering from the metallic part is strong enough to be detected even if the object is placed under a very rough surface.
Another important hardware choice concerns the radio frequency front end circuit. The SiGe GN3S Sampler v2, developed from the Colorado Center for Astrodynamics Research, was used . It is composed of two main integrated circuits. The first one is an application-specific integrated circuit (ASIC), which basically amplifies the incoming radio frequency (on the L1 GPS bandwidth), filters it, down-converts it from the GPS carrier frequency to an intermediate frequency of 38.4 MHz, and samples it (with a sampling rate 8.1838 MHz, which can provide up to eight samples per code chip of the modulating C/A code). Two bits for representing both the in-phase and the quadra-phase samples of the signal component are used and are sent to the second circuit, the microcontroller, which transfers in real time the ASIC-generated samples into a USB.
3.2 Signal processing and post-processing software
where k is the Boltzmann constant, k = 1.380 × 10-23 J/K; TN is the estimate of the receiver noise equivalent temperature (which can be approximated as TN = (NF - 1) 290), NF (dB) being the receiver noise figure (it can be estimated in the range of 1.0 to 2.5 dB); and B w = 1/TI is the signal bandwidth determined by the coherent integration time TI (1 ms in our case). It results in PN = - 176.3 dB W. The antenna's temperature (TA) was not taken into account in the input noise power evaluation because the measurements were carried out to detect the metallic object and to estimate its dimension by evaluating the relative increase (or decrease) of the SNR, without changing the experimental setup.
The estimated total received power PS (coherent signal power) can be derived from Equation 3. Even if only the value of the correlation peak was used to estimate SNR, this open-loop approach allowed us to develop and implement the software procedure to evaluate the entire autocorrelation function, whose knowledge could be used in the future for other GNSS-R applications, more oriented to the remote sensing of surface parameters. As far as the detection of buried objects is concerned, the estimation of the SNR time series is enough, as it will be discussed in ‘Section 5.’
Piazza d'Armi, Turin, Italy, 16 July, 2013, antenna in a static position, compact receiver, sandy terrain
Montoro, Avellino, Italy, 22 August, 2013, moving antenna, PC-based receiver, grass terrain
All the experiments were carried out considering as a target a circular metal disk (28-cm diameter) object. The dimensions of this object are comparable to those of an improvised explosive device or a pressure-activable mine.
A MATLAB tool to predict the positions of all the specular reflection points automatically projected on a Google Earth map for any GPS signal available was developed. The specular reflection points can be found on the basis of the receiver position and the predicted GPS satellite orbits (downloaded from CALSKY website - http://www.calsky.com - and based on the predicted IGS orbits). Knowledge of the expected positions of available reflections given by this tool was fundamental for the planning of the measurement campaigns. The antenna used was a commercial device, manufactured by Antcom . It is an active L1/L2 RH/LH antenna (PN 4261215), characterized by a HPBW of 140° (maximum gain 3.5 dB). The antenna was fixed on a plastic-wood structure in order to perform the measurements at a constant height (3 m) from the ground and in far field conditions.
4.1 Piazza d' Armi experiment (16 July, 2013)
A1 - from 2:55 to 2:56 p.m. (local time), the metallic plate was placed on dry soil far away from the expected first Fresnel zone.
A2 - from 3:00 to 3:01 p.m., the metallic plate was removed from the antenna footprint.
A3 - from 3:09 to 3:10 p.m., the metallic plate was buried under the dry soil.
A4 - from 3:12 to 3:13 p.m., the metallic plate was placed on dry soil.
A5 - from 3:14 to 3:15 p.m., the metallic plate was buried under completely wet soil.
Statistical characterization of the SNR estimates
A1 - metal object on dry sand not in the Fresnel zone
A2 - dry sand
A3 - metal object under dry sand
A4 - metal object over dry sand
A5 - metal object under wet sand
The first two measurements (A1 and A2) were performed to verify the correct operation of the software receiver in terms of data acquisition. In the first one (A1), the metal plate was not inside the first Fresnel zone, but it was in the antenna footprint. Therefore, the SNR estimate of +2 dB (mean value) also takes into account some of the power scattered out from the specular direction by the metal plate. During the second time slot (A2), the object was removed, but an unexpected event occurred in the receiver hardware around the 200th sample. In this case, a more realistic statistical figure for the estimated SNR would be around -1 dB (also the std figure shown in Table 1 is not representative). The presence of the metallic plate over dry soil (A4) or just buried under it (A5) produces a significant increase in the received power (from around -1 dB without any object to 5 or 7 dB). This increase in the SNR should be produced by the metallic object only, since the ground in the (coherent) Fresnel zone (and in the noncoherent - glistening - zone) did not change. In conclusion, in the case of dry terrain, where the penetration depth allows more electromagnetic energy to reach the metal plate and to be reflected back towards the receiver, a good sensitivity of the receiver was observed. In fact, a level of 5.1 ± 1 dB was measured when the metal plate was buried under the sand, while there was a stronger 6.9 ± 1.3 dB when it was simply placed above the sand.
A noticeable increase of a further 5 dB was observed in the case of completely wet sand (A5). This higher contribution to the received power is probably due to the increase of the dielectric constant of the terrain due to the presence of water. Several experiments were done before (but not reported here), but in all of them, the increase of the real part of the dielectric constant due to the water content strongly impacts the detection capability of the receiver.
4.2 Montoro experiment (22 August, 2013)
The metal plate was positioned 5 m away from the starting point (1 m away from the ending point) on a portion of ground on which a contribution to the reflection of the signal coming from PRN 24 was expected.
In this case, the effects due to vegetation canopy and grass coverage should be taken into account. The estimation of the quantitative impact is very difficult, being a combination of incidence angle, wavelength, biomass volume, height, and loss component induced by the dielectric constant of water-containing stalks and leaves. In addition to the theoretical approach described by Ulaby et al. (see [44, 45]), a detailed analysis is presented in [22, 23]. As a first approximation, an average reduction of the SNR of 2 dB due to the effect of vegetation will be taken into account.
Three ‘flights’ were performed:
B1 - from 8:50 to 8:51 a.m. without the 28-cm-diameter metal plate
B2 - from 8:52 to 8:53 a.m., with the metal plate placed on the soil
B3 - from 8:54 to 8:55 a.m., with the metal plate buried approximately 4 cm under the soil
In this paper, a new application of GNSS-R technique for the detection of buried objects was investigated. A LH antenna was used to collect reflected GPS signals by a software-defined radio GPS receiver. The effects of surface roughness and vegetation canopy were neglected and the reflected signal power estimated considering only coherent power. An open-loop approach was used for deriving the SNR time series related to the reflected GPS signals.
Two prototypes were developed. The first was a software receiver, and the second a more compact prototype suited for onboard UAV applications. A Hackberry board was used to manage the receiver front end and to store the raw data. The post-processing was done using a standard laptop. Two measurement campaigns were carried, out and the variation of the SNR level due to the presence of a metallic object was investigated. The first measurement campaign was performed in a static condition on a sandy terrain to check the functionality of the system. Note that the presence of the metallic object was detected also in the case of wet terrain. In this case, the effect due to the increase of the dielectric constant characterizing the ground may hide the effect derived from the metallic object. In the second measurement campaign, the antenna was moving along a given path and the possibility of detecting the object dimensions was highlighted. The results show the possibility of adopting this technique on board an UAV, remotely controlled. In this case, the flying direction could be modified in order to better understand the position and shape of the object. Some other measurement campaigns are foreseen during the forthcoming seasons. In order to remove the strong assumption of a smooth terrain, a new prototype capable of collecting reflected signals on both the polarizations (LH and RH) is going to be developed and a deeper analysis on the effect of vegetation canopy and its water content, terrain texture, and composition will be addressed in a future work.
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