Passive Sensing of Electrically Conductive Concrete for Lateral Vehicle PositioningAuthor 1: Sachindra Dahal, PhD. Student, University of Illinois, Urbana, IL, USA

Author 2: Jeffery Roesler, Professor, University of Illinois, Urbana, IL, USA

For the corresponding author: sdahal2@illinois.edu

KEYWORDS: Passive sensing, concrete, lateral positioning, autonomous vehicles, electric conductance, vehicle to infrastructure

Conflict of Interest: None

1. ABSTRACT

Autonomous vehicles (AV) offer multiple safety benefits for drivers and road agencies. Current AV technology allow for vehicle control, guidance, and navigation as well as communication with other vehicles and roadside infrastructure. To see significant penetration of Level 4 or 5 AV without compromising safety, redundant vehicle to infrastructure sensing capabilities are necessary especially during severe weather conditions. Existing vehicle technology is not able to communicate with the concrete and asphalt pavements without embedded sensors. An eddy current technique is proposed that detects local changes in the concrete’s electrical conductance so that AV can determine their lateral lane position. Concrete slab specimens with varying dimensions and dosages of steel-fiber reinforced concrete (SFRC) were tested under normal and adverse surface conditions (standing water or ice) as well as separation distance from the transmitter coil. The longitudinal segment of SFRC’s material was successfully located as the coil moved laterally across the surface even under these adverse surface conditions. This pilot study demonstrates a reliable and robust technique using changes in the concrete’s electrical conductance to provide lateral positioning redundancy to AV control and guidance.

2. INTRODUCTION

Connected and autonomous vehicles (AV) are on the verge of significantly altering the means passengers, goods, services, and vehicles interact. Adoption of AV technologies have reported benefits such as improving driver/passenger safety, greater roadway capacity, and reducing traffic congestion and vehicle fuel consumption [1]–[5]. AV employ a suite of sensors like RADAR, LIDAR, GPS, cameras, and ultrasonic sensors to perceive the roadway/roadside environment in order to control, guide, and navigate the vehicle independently of the driver [5], [6]. Although, millions of miles have been driven by AV using these advanced sensors [1], [5], [7], one of the main challenges of large-scale implementation of AV is the robustness of these systems in adverse weather conditions. Most of the existing AV sensors have severe limitations during weather such as heavy rain, snow, fog, and ice.

Currently, AV use GPS coordinates and cameras, to maintain the vehicle’s position in the travelling lanes [8]–[10]. One of the problems AV have during adverse weather is maintaining their lateral position within the lane. GPS signals worsen [11]–[14] and lane markings are not clearly visible during severe weather. A solution that enables the pavement and vehicle to interact and determine its lateral position in these conditions would provide reliable, robust, and safe vehicle operation.

Pavements are currently designed and constructed without consideration of active or passive communications with AV. Development of pavements to enhance communication with the AV is necessary for safe and large-scale deployment. Pavements can adopt active or passive methods to connect and communicate with AV. Active communication methods for AV embed sensors such as transponders or RFID in the pavement or install sensors in the roadside infrastructure in order to assist in vehicle guidance. Passive communication methods can be defined as modification of the roadway such as lane markings or pavement material properties to create a unique and repeatable signature that AV can identify accurately.

Figure 1. Modifying pavement signature (markings or materials) to detect lateral roadway position of AV.

The robustness of AV control and guidance depend on integration of redundant information from various sensors [15]. To assist AV lateral positioning and subsequently control and guidance, existing and new pavements can be modified by changing certain properties in their transverse profile. Figure 1 shows two examples of such markings containing ferromagnetic material or

paving material modifications in the travelled way. This paper focuses on a passive sensing solution using a targeted amount of steel-fiber reinforced concrete (SFRC) to detect the lateral position of a vehicle. The electrical conductance signature of the embedded SFRC strip (See figure 1) will enable lateral position detection using an induction-based eddy current coil.

3. RESEARCH SIGNIFICANCE

This research proposes an innovative approach to allow existing vehicles or AV to passively communicate with the pavement surface, which offers a redundant control and guidance system for lane keeping. Pavement and vehicle communication are required in adverse weather conditions when sensors critical for lane keeping like GPS and cameras are less reliable. This paper explores use of embedded steel-fiber reinforced concrete (SFRC) strips and its electrical signature to detect the lateral position of vehicles in the lane for both normal and adverse environmental conditions. To evaluate the concept, a slab with standard paving concrete and a more electrically conductive mixture containing steel fibers was constructed in lab and tested with induction-based eddy-current sensor. This passive sensing approach is used to detect the location and strength of the SFRC material strip signal in normal and adverse weather conditions (standing water and ice). With a paving material solution for passive sensing of lateral position, future pavement rehabilitations or new designs could include more electrically conductive material in targeted positions in the pavement cross-section.

4. METHODOLOGY

Induction-based Eddy Current Technique

Eddy currents are induced within a conductor when it is subjected to changing magnetic fields. Devices such as metal detectors use pulse induction techniques to create a varying magnetic field so that the location of electrically-distinct objects such as conducting metals can be identified. These techniques have been used to detect buried jewelry, historic artifacts, landmines, or unexploded ordinance [16]. This paper employs a similar pulse induction system to determine the potential of detecting electrically-distinct material (SFRC) under various geometries, quantities, and environmental conditions when placed as a notch material in concrete slab specimens.

The pulsating sensor consists of a transmitter coil with multiple loops of wire, forming an inductor. When electricity flows through the transmitter coil, a magnetic field is created around the coil. A pulse of DC voltage is generated to briefly energize the coil creating a static magnetic field, then the voltage is rapidly cutoff to collapse the magnetic field at higher rate. These voltage pulses are generated and collapsed rapidly, as seen in Figure 2, by a negative input voltage (known as “flyback” voltage) about 205 times a second (i.e., 205 Hz). When the voltage rapidly collapses to “flyback” voltage, the rate of change of magnetic field is highest and induces eddy currents in the metal target. The “flyback” voltage decays back exponentially to zero. When there is an eddy current induced in the nearby metallic target, the eddy current acts as secondary magnetic field that alters the flyback voltage decay curve. The subtle voltage changes in the coil

during this decay period because of the eddy current in the metallic target is amplified and stored in a capacitor for each pulse. The presence of the secondary magnetic field from the metallic target increases the charge stored in the capacitor, which otherwise decays to zero voltage in absence of secondary magnetic field. The output of the circuit is a DC voltage with nominal value of about zero when no metallic target is present and a rise in the voltage in the presence of a secondary magnetic field [16]–[18].

A pulse induction circuit with coil size of 25.4 cm (10 inch) was used to detect SFRC material embedded in the surface of a concrete slab specimen. Figure 3 shows the pulse generating circuit, and sensor coil mounted on a motorized frame. The analog output voltage of the charge stored in the capacitor was collected at a rate of 500 samples per second.

Figure 2: Typical pulse generated by pulse induction circuit showing short pulse and rapid collapsing of signal to around -12 voltage.

Figure 3: Pulse induction circuit (left) that connects to the motorized induction-based eddy current coil (right) over the slab specimen.

Laboratory Slab Specimen Preparation and Testing

Three concrete slab specimens were cast in the laboratory with dimensions of 53.3x45.7x15.2 cm (21x18x6 inch) as shown in Figure 4. Each slab specimen was created with a notch on surface, which could be subsequently filled with an electrically conductive concrete prism. The notch dimension and resulting concrete prism were dimensioned to evaluate the effect of different volumes of electrically-distinct material. The various prism dimensions, 3.8 cm x 3.8 cm (1.5-inch x 1.5-inch), 6.4 cm x 6.4 cm (2.5-inch x 2.5-inch), and 8.9 cm x 8.9 cm (3.5-inch x 3.5-inch) were inserted into the appropriate slab notch size during testing. Three concrete prisms for each dimension were made of SFRC with volume fractions of 0.50%, 0.75%, and 1.0%.

Figure 4. Notched concrete slab with electrically-distinct concrete prism.

In addition to the steel fiber content and the volume of electrically-distinct material, the effect of the coil height above the surface and the concrete’s surface condition (dry/normal or adverse such as water or ice) was evaluated. Normal surface condition was defined when the concrete surface is dry, whereas adverse conditions were simulated by placing standing water or ice of two different depths [2.5 cm (1 inch) and 5.1 cm (2 inch)]. The sensor coil was placed at three different heights: 12.7 cm (5 inch), 15.2 cm (6 inch), and 17.8 cm (7 inch) above the top surface of the slab.

The eddy current-based sensor coil is mounted on a frame as shown in the Figure 3. The frame consists of a stepper motor that moves the sensor coil at constant speed and fixed height above the slab. When the eddy current coil moves laterally across the slab width, the coil first moves over the normal concrete followed by interaction with the electrically-distinct prism at the center of the slab, and finally again over the top of the normal concrete. While moving over the normal concrete no eddy current is generated and therefore almost no output voltage signal is recorded. However, as the coil approaches the electrically-distinct SFRC in the transverse direction, the voltage stored in the coil’s capacitor circuit increases and then depletes as the transmitter coil moves away. The magnitude of signals obtained depends on combination of distance between the coil and sample (coil height), notch dimensions, percentage of steel fibers in concrete prism, and

surface conditions on top of the concrete slab sample (normal or adverse conditions). To quantify the effect of each variable on magnitude of signal obtained, sensitivity tests were done by changing each variable at three levels: low, medium, and high as shown in Table 1. The low signal level is a condition where signals are expected to be weaker than medium and high signal level of each variable. To illustrate effect of each signal response level, results of low and medium responses are normalized to that of the high signal response level.

Table 1: Three levels of signal sensitivity tested for each independent variable.

Variables LevelHigh Medium Low

Height of coil 12.7cm (5 inch) 15.2 cm (6 inch) 17.8cm (7 inch)Square Notch size 8.9cm (3.5 inch) 6.4cm (2.5 inch) 3.8cm (1.5 inch)Steel fiber content 1.00% 0.75% 0.50%

Surface Water 0cm (0 inch) 2.5cm (1 inch) 5.1cm (2 inch)Surface Ice 0cm (0 inch) 2.5cm (1 inch) 5.1cm (2 inch)

Table 2 lists the five cases evaluated for each of the four variables (height of coil, notch size, fiber content, and surface condition) and at three sensitivity levels. The automated sensor coil scans the slab 20 times at the low level followed by medium and high signal level. Figure 5 displays the acquired signals (voltage stored in coil’s capacitor circuit) for three coil heights and a notch dimension of 3.5 inch x 3.5 inch containing 1.0% SFRC for normal condition, which corresponds to Case 1 for the coil height variable. Similar signals were obtained for each case listed in table 2 for each of the four variables. The cumulative area under these 20 responses is computed for all three output signals corresponding to the three sensitivity levels. The computed cumulative area of the high level is normalized to 100 with the medium and low normalized to the high level for each variable case. The normalized value of each of three sensitivity levels (high, medium, and low) are averaged for the five cases of one variable to obtain a representative effect with change in sensitivity level from high to low.

Table 2: Five cases evaluated per variable with three levels of signal sensitivity for each case.

Variable Cases Coil Height, cm (inch)

Notch size, cm (inch)

Steel fiber content Condition

Coil Height

1 Three Levels 8.9 (3.5) 1.00% Normal only2 Three Levels 6.4 (2.5) 1.00% Normal only3 Three Levels 3.8 (1.5) 1.00% Normal only4 Three Levels 6.4 (2.5) 0.75% Normal only5 Three Levels 8.9 (3.5) 0.75% Normal only

Notch Dimension

1 12.7 (5) Three Levels 1.00% Normal only2 12.7 (5) Three Levels 0.75% Normal only3 15.2 (6) Three Levels 1.00% Normal only4 15.2 (6) Three Levels 0.75% Normal only5 17.8 (7) Three Levels 1.00% Normal only

Steel fiber content

1 12.7 (5) 3.8 (1.5) Three Levels Normal only2 12.7 (5) 6.4 (2.5) Three Levels Normal only3 12.7 (5) 8.9 (3.5) Three Levels Normal only4 15.2 (6) 6.4 (2.5) Three Levels Normal only5 15.2 (6) 8.9 (3.5) Three Levels Normal only

Adverse condition:

Water

1 12.7 (5) 8.9 (3.5) 1.00% Three Levels 2 12.7 (5) 6.4 (2.5) 1.00% Three Levels 3 15.2 (6) 8.9 (3.5) 1.00% Three Levels 4 15.2 (6) 8.9 (3.5) 0.75% Three Levels 5 15.2 (6) 6.4 (2.5) 1.00% Three Levels

Adverse condition: Ice

1 12.7 (5) 8.9 (3.5) 1.00% Three Levels 2 12.7 (5) 6.4 (2.5) 1.00% Three Levels 3 15.2 (6) 8.9 (3.5) 1.00% Three Levels 4 15.2 (6) 8.9 (3.5) 0.75% Three Levels 5 15.2 (6) 6.4 (2.5) 1.00% Three Levels

Figure 5. Capacitor output for twenty scans at three coil heights (5, 6, and 7 inches) for 8.9 cm (3.5 inch) square notch, 1.0% steel fiber content, and normal surface condition.

5. RESULTS

Figure 6 presents representative signals (non-normalized) obtained from one scan over the slab at the three sensitivity levels for each variable. As expected, the signal strength depends on the lateral position of the sensor coil. The signal is highest when coil center is above the electrically-conductive concrete prism and goes close to zero as the coil center is offset 10 to 15 cm from center of the notch material. In general, the signal peak and area under the curve decreases as the sensitivity level goes from high to low for a given variable. The normalized values for each signal sensitivity levels (high, medium, and low) were averaged for the five cases under each variable and presented in Figure 7.

(a) Coil Height (b)Notch dimension

(c)Steel Fiber content (d) Adverse Condition: Water

(e) Adverse condition: Ice

Figure 6. Signal response of one scan over the slab with SFRC prism for three sensitivity levels for each of the five variables; a) coil height, b) notch dimension, (c) steel fiber content, (d) standing water on surface, and (e) ice on surface.

PrismPrism

Prism Prism

Prism

Figure 7. Normalized signal strength versus variable levels for 5 variables.

Effect of coil height

As expected, the signal strength depended on the distance between the electrically conductive concrete material and the coil as seen in Figure 6a for coil heights of 12.7 cm, 15.2 cm, and 17.8 cm. When the sensor coil is near the SFRC, the signal strength is higher as the coil induces more eddy current in the target material. The signal attenuates from a peak voltage of 105 mV at 12.7 cm (5 inch) height to 85 mV at 15.2 cm (6 inch) height, and to 58 mV at 17.8 cm (7 inch) height. As seen in Figure 7, average normalized signal from coil heights of 15.2 cm (6 inch) and 17.8 cm (7 inch) was 61.9% and 23.9%, respectively of the average normalized signal from coil height of 12.7 cm (5 inch).

Effect of SFRC material dimension and fiber content

A larger notch dimensions increased the volume of the electrically-distinct target material and thus, the signal is higher for larger notch dimension (Figure 6b) because there are more steel fibers for the same volume. Figure 7 shows the sensitivity of the notch dimension on the signal strength. The signals from notch sizes of 6.4 cm (2.5”) and 3.8 cm (1.5”) were 74.5% and 11.8%, respectively of the signal obtained from largest notch size of 8.9 cm (3.5”).

As the SFRC volume fraction increased for the same prism dimension, the response signal was expected to increase. Higher eddy currents are induced in the samples with higher steel fiber content as seen in Figure 6c. As expected and shown in Figure 7, the steel fiber contents of 0.75% and 0.50% had signal strengths of 77.1% and 37.4%, respectively, compared to the signal from 1.0% steel fiber content.

Effect of adverse surface conditions

Two adverse weather (surface) conditions were simulated by placing standing water and ice on the surface of the slab. Sensor coil scanning tests were run on the slabs with the following adverse surface conditions: water or ice depth at 2.5 cm (1”) or 5.1 cm (2”) compared to normal (dry) surface conditions.

Water has relative electrical permittivity (εr) or dielectric constant of around 80 compared to that of one for air. A water layer between the coil and the target will attenuate the response signal. Figure 6d plots typical signals without water on top compared to standing water of thicknesses 2.5 cm (1 inch) and 5.1 cm (2inch). Normalizing the signal without water (level high) to 100% strength, the standing water of thickness of 2.5 cm (1 inch) attenuated the average signal to 82.8% and the standing water of thickness of 5.1 cm (2 inch) attenuated the signal to 76.9% (See Figure 7). Despite water on the surface of the specimen, the signal was only attenuated by 23% at a 5.1 cm (2 inch) depth of standing water, and the SFRC prism location was detected.

Unlike water, ice has relative permittivity of 3, which is much more comparable to air (εr=1). Past researches have shown that ice has negligible signal attenuation of electromagnetic wave at lower frequency level [19]–[21]. The eddy current coil was equally effective through ice as observed in Figure 6e for two ice thicknesses relative to the normal (dry) surface condition. The 2.5 cm (1”) of ice had the signal of 100.5% and 5.1 cm (2”) of ice had the signal of 102.7 % compared to signal in normal condition as shown in figure 7.

6. DISCUSSION

In order for this induction-based method to be viable, a proper balance between the coil height and geometry, notch dimensions, SFRC volume fraction, and surface conditions must be attained such that a detectable eddy current is created in the SFRC target at normal vehicle speeds. The preliminary results presented demonstrate a combination of variables can produce detectable signals that could enable an AV to locate its lateral position in the travelling lane. Peak signals were observed when sensor coil was positioned within 5 cm of the center of notch material indicating the use of sensor array in AV can effectively identify vehicle lateral location with respect to center of the lane.

As the signal level moved from high to low, coil height, notch dimension, and steel fiber content had more influence on the signal attenuation (40% to 75% signal reduction for low signal level)

compared to adverse weather conditions like standing water or ice (0% to 25% signal reduction for low level) as seen from figure 7. With further testing, optimum coil height can obtained and fixed at that height in AV. Factors like notch dimension and steel fiber content can be engineered during new pavement construction or future pavement rehabilitation to provide detectable signal strength.

Volume fraction of conductive material would need to be increase or the size of the notch dimension to maintain signal strength especially for larger coil heights above the road surface. For instance, the notch dimension of 3.8 cm (1.5 inch) mostly had longitudinally-aligned fibers in the prism because the fiber length was only 5.1 cm (2 inch). Larger notch sizes could contain longitudinal as well as transverse distributed fibers and increase the signal strength. Proper distribution of fiber is important for uniform signal detection as the improper distribution or balling of fibers leads to a stronger signal at some locations and very weak or no signal in others. Both the size of the electrically-conductive concrete material and fiber size and content can be addressed by proper design and construction techniques to ensure detection by the eddy current coil attached to the AV.

An additional advantage of this technique over computer vision or other optical or positional methods is ice doesn’t affect the signal results and surface water ponding still produces a detectable signal. Once the design and construction factors like height of coil, notch dimension, steel fiber dosage and proper distribution of the fibers are optimized to provide reliable signal strength, this passive sensing method can be a robust, redundant alternative to detect AV lateral position in adverse weather condition.

7. CONCLUSIONS

Connected autonomous vehicles are evolving rapidly and will slowly integrate into the normal transportation operations. One of the main challenges of AV is safe guidance in adverse weather conditions, where existing AV sensors may be intermittent or unable to perform at a prescribed reliability or accuracy level. To provide an additional layer of safety through passive vehicle to infrastructure communication, an electrically-conductive material signature using steel fiber reinforced concrete (SFRC) is embedded in the concrete pavement surface to enable the determination of the vehicle position in the lane. This electrically-distinct material is detected using an induction-based eddy current technique.

Laboratory testing was completed on notched slab specimens which were infilled with SFRC prisms in order to determine the most sensitive variables to the eddy current method. The variables expected to impact the signal strength such as volume of steel fibers, notch dimension, height of the sensor coil from target, and adverse conditions (ice and water) were tested systematically. The SFRC material was successfully detected by the sensor coil at varying signal strength depending on the available conductive material (fiber content and SFRC dimension), coil height, and presence of standing water. For this setup and coil circuit and geometry, lower steel fiber content (0.5%), notch dimension (3.8 cm), and height of the sensor coil (17.5cm)

reduced the signal strength between 40% to 75% as compared to the high signal levels (steel fiber content of 1.0%, notch dimension of 8.9 cm, and height of the sensor coil of 12.5cm). The presence of standing water of 5.1 cm only reduced the signal 25% while 5.1 cm of ice on the surface of the specimen had no attenuating effect. This approach of modifying a small longitudinal slice of pavement using an electrically-conductive material like SFRC is a promising means to detect the lateral position of AV especially in adverse weather conditions.

8. ACKNOWLEDGEMENTS

Funding for this research was provided by the Center for Connected and Automated Transportation of University of Michigan Ann Arbor with grant number 69A3551747105 through the U.S. Department of Transportation’s University Transportation Centers (UTC) program.

9. REFERENCES[1] J. Anderson, K. Nidhi, K. Stanley, P. Sorensen, C. Samaras, and O. Oluwatola,

Autonomous vehicle technology: A guide for policymakers. Rand Corporation, 2014.

[2] T. Folsom, “Energy and Autonomous Urban Land Vehicles,” IEEE Technol. Soc. Mag., vol. 31, no. 2, pp. 28–38, 2012.

[3] J. B. Greenblatt and S. Shaheen, “Automated Vehicles, On-Demand Mobility, and Environmental Impacts,” Curr. Sustain. Energy Reports, vol. 2, no. 3, pp. 74–81, Sep. 2015.

[4] M. Taiebat, A. L. Brown, H. R. Safford, S. Qu, and M. Xu, “A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles,” Environ. Sci. Technol., Sep. 2018.

[5] L. Burns and C. Shulgan, Autonomy: The Quest to Build the Driverless Car—And How It Will Reshape Our World. HarperCollins, 2018.

[6] C. Ilas, “Electronic sensing technologies for autonomous ground vehicles: A review,” in 2013 8TH INTERNATIONAL SYMPOSIUM ON ADVANCED TOPICS IN ELECTRICAL ENGINEERING (ATEE), 2013, pp. 1–6.

[7] N. Kalra and S. M. Paddock, “Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?,” Transp. Res. Part A Policy Pract., vol. 94, pp. 182–193, Dec. 2016.

[8] Z. Tao, P. Bonnifait, V. Fremont, and J. Ibanez-Guzman, “Lane marking aided vehicle

localization,” in 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 2013, pp. 1509–1515.

[9] Z. Tao, P. Bonnifait, V. Fremont, and J. Ibanez-Guzman, “Mapping and localization using GPS, lane markings and proprioceptive sensors,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, pp. 406–412.

[10] M. Schreiber, C. Knoppel, and U. Franke, “LaneLoc: Lane marking based localization using highly accurate maps,” in 2013 IEEE Intelligent Vehicles Symposium (IV), 2013, pp. 449–454.

[11] P. H. Dana, “Global Positioning System (GPS) Time Dissemination for Real-Time Applications,” Real-Time Syst., vol. 12, no. 1, pp. 9–40, 1997.

[12] J. R. Rodríguez-Pérez, M. F. Álvarez, and E. Sanz-Ablanedo, “Assessment of Low-Cost GPS Receiver Accuracy and Precision in Forest Environments,” J. Surv. Eng., vol. 133, no. 4, pp. 159–167, Nov. 2007.

[13] Youjing Cui and Shuzhi Sam Ge, “Autonomous vehicle positioning with gps in urban canyon environments,” IEEE Trans. Robot. Autom., vol. 19, no. 1, pp. 15–25, Feb. 2003.

[14] V. Milanés, J. E. Naranjo, C. González, J. Alonso, and T. de Pedro, “Autonomous vehicle based in cooperative GPS and inertial systems,” Robotica, vol. 26, no. 5, pp. 627–633, Sep. 2008.

[15] I. Skog and P. Handel, “In-Car Positioning and Navigation Technologies—A Survey,” IEEE Trans. Intell. Transp. Syst., vol. 10, no. 1, pp. 4–21, Mar. 2009.

[16] B. Kim, S. Lee, K. Kim, J. W. Yoon, and S.-H. Han, “Pulse-induction metal detector with time-domain bucking circuit for landmine detection,” Electron. Lett., vol. 51, no. 2, pp. 159–161, Jan. 2015.

[17] R. Medek, J. Nicolics, and D. Schrottmayer, “High sensitive pulse inductive eddy current measurement for mine detection systems,” in 24th International Spring Seminar on Electronics Technology. Concurrent Engineering in Electronic Packaging. ISSE 2001. Conference Proceedings (Cat. No.01EX492), 2001, pp. 207–211.

[18] G. Overton and C. Moreland, Inside the Metal Detector. Geotech Press, 2012.

[19] A. Arage, W. M. Steffens, G. Kuehnle, and R. Jakoby, “Effects of Water and Ice Layer on Automotive Radar,” in Proc. of the German Microwave Conf., 2006.

[20] B. Hubbard and N. Glasser, Field techniques in glaciology and glacial geomorphology. John Wiley & Sons, 2005.

[21] L. A. Plewes and B. Hubbard, “A review of the use of radio-echo sounding in glaciology,” Prog. Phys. Geogr., vol. 25, no. 2, pp. 203–236, Jun. 2001.