Fusion of Data From Forward-Looking Demining Sensors

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1 Fusion of Data From Forward-Looking Demining Sensors B. A. Baertlein, W.-J. Liao and D.-H. Chen The Ohio State University ElectroScience Laboratory 132 Kinnear Road, Columbus, OH Phone: (614) Fax: (614) Category: Session 38 ABSTRACT We present fusion algorithms and detection performance for two sensors of buried land mines capable of forwardlooking operation. The Þrst is a laser Doppler vibrometer (LDV) sensor of acoustically induced soil vibrations developed by the University of Mississippi. The second is a wideband, forward-looking, synthetic aperture groundpenetrating radar developed by SRI. We demonstrate the performance of the individual sensors and fusion algorithms over a data set acquired at Yuma Proving Grounds, AZ. During that collection the LDV sensor was operating in a down-looking mode, but its ability to collect forward-looking data has been conþrmed in other tests. The amount of fusible data available for these sensors is small, which limits the sophistication and performance of fusion algorithms used with these data. We observed that the GPR and LDV sensors are complementary to a degree. The GPR sensor is signiþcantly better at detecting metal mines, while the LDV sensor is somewhat better at detecting plastic mines. A classical decision-level OR fusion algorithm is shown to be effective for this sensor suite. 1. INTRODUCTION Physical limitations force many mine-detecting sensors to operate in close proximity to the soil, but for safety reasons greater sensor standoff is preferable. In response to this need, there is increased interest in forward-looking sensors, particularly for buried mine detection. Several technologies have shown an ability to detect buried mines at signiþcant standoff distances. It has long been recognized that passive infrared (IR) sensors will detect buried mines under certain conditions, but IR sensors have several weaknesses. Perhaps the most serious limitation of those sensors is that the signature of a buried mine is detectable only at certain times of the day and under favorable environmental conditions. This fact makes IR sensors unattractive in some roles. Ground penetrating radar (GPR) is a second sensor capable of detecting buried mines from a signiþcant standoff distance. When the radar is operated in a forward-looking geometry, the strong specular surface reßection that plagues down-looking GPRs is greatly reduced. A number of systems of this type have been demonstrated over the years. Recently, SRI has produced a forward-looking ground-penetrating radar (FLGPR) for demining. Another sensor that has recently shown promise for buried mine detection is remote sensing of acoustically stimulated ground motion. An implementation of this concept using a laser Doppler vibrometer (LDV) for detecting surface motion has recently been demonstrated by the University of Mississippi. The stressing performance requirements of demining motivate an investigation of multi-sensor data fusion. Fusion offers several potential beneþts including an increase in detection rate, a reduction in false alarm rate, and an ability to operate in more diverse environments. In this paper we describe the application of sensor fusion to the FLGPR and LDV sensors. We begin in Section 2 with a brief description of the sensors and the data they produce. In Section 3 we describe algorithms for processing data from these sensors. Recently, both sensors were used to acquire data at Yuma Proving Grounds (YPG), AZ. Single sensor performance, fusion algorithms, and fused sensor performance are described in Section 4. The resulting fusible data set is small, which has serious implications for fusion (described in Section 4.1). Moreover, sensor performance characteristics estimated from a small data set do not necessarily represent true sensor performance. 1

2 Nonetheless, we show that some relatively simple fusion techniques are effective. Section 5. Concluding remarks appear in 2. TEST SITE AND SENSOR SUITE 2.1. Test Site: Yuma Proving Grounds During March 2 the FLGPR and LDV sensors were operated at the YPG mine lanes. Those lanes comprise four 3 meter by 5 meter regions, the content of which is publicaly available. A fourth region of equal size is reserved for blind tests. The lanes contain solely AT mines of types TM62M, TM62P, and VS2.2. Lanes 1 and 2 each contain 15 TM62Ps, 15 TM62Ms, 15 VS2.2s, and 9 Þlled holes. Lane 3 contains 2 mines of each type and no Þlled holes. In total, the three lanes contain 5 of each mine type, of which 11 are buried at each of the depths zero (ßush), 5 cm, 1 cm and 15 cm. The remaining 9 mines of each type are placed on the surface SRI Forward Looking Ground-Penetrating Radar (FLGPR) The SRI FLGPR has been described previously. 1,2 Key features of the system as conþgured for the 2 YPG data collection are a 5 cm resolution in range and cross-range. This resolution is achieved in the range dimension through a 2.7 GHz bandwidth (-3. GHz). For the cross-range dimension, a pair of quad-ridged horns (one for transmit and another for receive) are mechanically scanned through a 4 meter synthetic aperture. SAR processing is used to image a rectangular region eight meters wide and 23 meters long (seven to 3 meters downrange from the radar). Polarimetric data (HH, VV, and HV) are acquired. The radar is mounted on a vehicle (a delivery van), which is normally moved down-range in increments of a few meters between data acquisitions. For each data collection the van location (i.e., its x, y, andz coordinates) and orientation (roll, pitch, and yaw) were recorded and used to derive ground coordinates from image coordinates. An empirically derived amplitude correction was used to boost the received signal with increasing range so as to generate a constant return amplitude with range. AsnotedinSection1,aforward-lookingradaroffers potential beneþts both in operator safety and in reduced surface clutter. The latter limits the ability of down-looking radars to detect near-surface targets. The forwardlooking geometry, however, has some drawbacks. In particular, there is a decrease in the amount of radar energy that impinges on a buried target. In addition, scattering from surface clutter is distributed in range for a forward looking sensor, which can cause a surface clutter return to be received at the same time as the return from a buried target. The FLGPR acquired 541 images of Lanes 1 through 4 at YPG, which include multiple views of all 15 known mines. In using those data we restricted our attention to images in which the mine of interest was viewed at a range of approximately 15 meters. Both the VV and HH returns were considered in our analysis. To date we have not examined the cross-polarized response U. Mississippi Acoustically Driven Laser Doppler Vibrometer The UM LDV system comprises a pair of acoustical speakers directed at the earth. 3 5 The speakers are driven by pseudo-random noise, producing a broadband acoustic excitation at the earth s surface. Coupling of this acoustic energy into seismic vibrations is detected by a raster-scanned LDV. The data produced by this sensor comprise a square array 16 pixels on a side acquired on a uniform linear spatial grid with nominal overall dimensions of 1 meter square. At each spatial position a Doppler frequency response is measured, producing a spatial estimate of the surface velocity as a function of frequency. The measured frequency band ranges from a few tens of Hz to a few hundred Hz. The UM collection yielded 223 LDV images, which comprise samples of 96 known mines, 29 unique clutter sites (of which eight were Þlled holes at known locations) and 29 sites in the blind test area (Lane 4). During the Yuma collection the LDV was operated in a down-looking geometry, but its ability to collect data in a forward-looking geometry has been demonstrated previously in tests at other sites. 3. SINGLE-SENSOR ALGORITHMS Before it is possible to fuse sensor data having different formats, the data must be reduced to a common form. In our work the sensor data are reduced to a small number of features. In this section we describe that processing for each sensor. 2

3 3.1. FLGPR In this preliminary evaluation, we used as features the VV and HH signal-to-clutter ratios estimated by SRI. These values are determined as the ratio of (1) the brightest pixel within a speciþed distance from the expected target location and (2) the average of 36 clutter pixels taken from a region offset in cross-range from the true mine position. Since the resulting data are a ratio of a peak to an average, they are necessarily greater than or equal to unity. The FLGPR collected multiple images of each mine, and in this work we have winnowed those images using the following approach: Preference was given to data from a range of 15 meters, since the FLGPR signal-to-clutter ratio is range dependent, and the most effective detection typically occurs at that range. When multiple S/C values were available for a given mine at a given range, the average value was used. When data from a 15 meter range were not available, data from 2 meters or 1 meters were used instead LDV In processing the LDV data, it is Þrst necessary to estimate and remove background trends observed in the images. Such trends are thought to arise from the acoustic wave sweeping over the region being interrogated. A low-order polynomial model is Þtted to the data from each frequency band, and the unknown coefficients are determined via least squares. The procedure is described and illustrated in a companion paper, 6 and it will not be repeated here. The residual image is then processed using a CFAR algorithm 7 : a spatial shape for the mine signature is assumed, and a spatially matched Þlter is applied at each point in the image. The unknown spectral amplitude of the mine signature is computed using a maximum likelihood procedure which, for the assumption of Gaussian clutter, is equivalent to the solution of a set of linear equations. The residual error variance in the Þlter output is compared to a local estimate of the image clutter variance. An estimate of the log-likelihood ratio is obtained, which is used for detection. In addition to the log-likelihood ratio, we also found that the image variance was a good discriminator of mines. High variance was found to correlate well with the presence of a mine. 4. SINGLE-SENSOR AND FUSED PERFORMANCE The sensor data described above were used to generate ROC curves for single-sensor and fused detections. In this section we review the performance of the individual sensors, describe the fusion algorithm, and demonstrate the performance beneþts of fusion Data Set Limitations Although a signiþcant amount of data was collected with both sensors, the data suitable for fusion are surprisingly limited. Only data acquired at the same location can be combined through fusion. We refer to those data as fusible. The coordinates of the FLGPR data are known from the position and orientation of the vehicle. The radar positions can also be checked by examining the positions of known Þducials in the imagery. During the LDV collection, the mine positions were known and imagery collected over known mines can be combined with the relevant FLGPR data for fusion. The fusible target set comprises 33 TM62M mines, 33 TM62P mines, and 29 VS2.2 mines. The locations of the LDV clutter data, however, were not recorded. As a result, the fusible clutter data comprise the responses of eight Þlled holes for which the locations were known. This small set of non-targets greatly limits our ability to train and test classiþers and fusion algorithms, but it is sufficient to make some qualitative tests of fusion and detection concepts. It is also important to note that because the size of the clutter set is small, performance estimates shown here are not necessarily representative of the sensor s true performance. The size of the data set also limits the techniques available for classiþer design and fusion. With two sensors having two features each, it is not feasible to train feature-level fusion algorithms for the resulting four-dimensional space. We adopt a soft decision-level fusion approach, in which the outputs of the LDV and GPR detectors (prior to thresholding) are input to the fusion algorithm. 3

4 4.2. FLGPR Performance The performance of the FLGPR for all fusible mines in the YPG test site is given in Figure 1. We present separate ROC curves for the VV and HH signal-to-clutter ratios (SCRs). A study of these data reveals that the VV and HH SCRs are correlated, and fusion of them provides little beneþt. In what follows, we use the HH output as the FLGPR detector. It is common in theoretical analyses of detection and classiþcation to assume that data from a given sensor comprise identically distributed random variables. That assumption is violated for the FLGPR. To see this, consider the ROC curves shown in Figures 2 and 3 for metal (TM62M) and plastic (TM62P and VS2.2) mines respectively. For this small fusible data set the FLGPR has limited success in detecting mines in general (cf. Figure 1) and very poor performance for plastic mines (cf. Figure 3), but its performance on metal mines is excellent (Figure 2). The distribution of S/C values for metal mines differs signiþcantly from that of plastic mines, and this fact can be used to advantage in what follows GPR S/C (VV) GPR S/C (HH) Figure 1. ROC curve for the FLGPR on metal and plastic mines LDV Performance The performance of the LDV on all samples at YPG is shown in Figure 4. The Þgure shows ROC curves derived from the individual features (CFAR output and image variance) and also a classiþer output that uses both features. The classiþer comprises a weighted sum of nonlinearly remapped features. SpeciÞcally, both the CFAR value (feature f 1 ) and the image variance (feature f 2 ) are scaled to the range [,1]. The fused detector feature (f F )isgivenby f F = w 1 f 1 + w 2 f p 2 (1) where weights w 1 and w 2 and power law p are selected to maximize performance. The weights w 1 and w 2 account for the different performance of these two features, while the power-law remapping (on the normalized interval [,1]) permitsustodeþne simple nonlinear boundaries in the (f 1,f 2 ) feature space. Alternatively, we can think of the power-law mapping as a method for rescaling the features to comparable threshold levels. The responses of the LDV sensor for metal and plastic mines are shown in Figures 5 and 6. It can be seen that the response of this sensor is somewhat better for plastic mines. The variance features is signiþcantly more effective for plastic mines and small false alarm rates. 4

5 GPR S/C (VV) GPR S/C (HH) Figure 2. ROC curve for the FLGPR on metal mines GPR S/C (VV) GPR S/C (HH) Figure 3. ROC curve for the FLGPR on plastic mines Fusion Performance The foregoing results show that the FLGPR and LDV sensors are complementary to a degree. The FLGPR works markedly better for metallic mines, which the LDV works somewhat better for plastic mines. Fusion allows us to exploit this behavior by using simple OR fusion of hard (binary) decision-level outputs. The procedure used is as follows: 5

6 LDV CFAR LDV Var LDV Classifier Figure 4. ROC curves for LDV features and the LDV classiþer on metal and plastic mines LDV CFAR LDV Var LDV Classifier Figure 5. ROC curve for the LDV on metal mines. The soft-decision outputs from each sensor are scaled to the interval [,1]. A single threshold is applied to those outputs, resulting in two binary decision values. The decision values are logically OR ed together, producing a single fused decision. An ROC curve computed from this fusion algorithm is shown in Figure 7. The responses of the two sensors individually to the fusible data sets are also shown. We observe that fusion has produce a gain in P d throughout 6

7 LDV CFAR LDV Var LDV Classifier Figure 6. ROC curve for the LDV on plastic mines. most of the range of P fa as a result of the metal mines being detected by the GPR. The gain, however, is not large since the metal mines are only one third of the total targets and the LDV already has some capability to detect those targets. Nonetheless, it is notable that although the GPR performs poorly for the complete fusible data set, its excellent performance for metal mines can provide a beneþt via fusion. 5. CONCLUDING REMARKS Forward-looking demining sensors are attractive for the added operator safety they provide, and fusion of multisensor data is an effective method of improving the performance of a forward-looking system. In this paper we have demonstrated this concept. During March 2, data were acquired at Yuma Proving Grounds using both a forward-looking GPR sensor and a forward-looking laser Doppler vibrometer that sensed acoustically driven seismic soil motion. In fusing these data we found that the GPR is a very effective sensor of metal mines, but it is less effective on plastic mines. Conversely, the LDV sensor is somewhat more effective on plastic mines than on metal mines. The amount of fusible data is small (in particular, the amount of fusible clutter data is very limited), which limits the number and sophistication of fusion techniques that are suitable to this problem. We found that a simple OR fusion approach can exploit the complementarity of these sensors and provides an improvement in performance. 6. ACKNOWLEDGMENTS The authors gratefully acknowledge the assistance of SRI in interpreting the FLGPR data and in providing the S/C values used for this study. Similarly, the assistance of the University of Mississippi in providing and interpreting the LDV data is gratefully acknowledged. This project was supported by funds from Duke University under an award from the ARO (the OSD MURI program). The Þndings, opinions and recommendations expressed therein are those of the author and are not necessarily those of Duke University or the ARO. 7

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