KEYWORDS: Mining, General packet radio service, Soil science, Data modeling, Palladium, Performance modeling, Land mines, Metals, Sensors, Automatic target recognition
An empirical performance model for the Mine Hunter/Killer (MH/K) Ground Penetrating Radar (GPR) was developed and used to analyze the performance of this GPR as a function of soil type, soil moisture, mine casing and mine depth. The empirical modeling approach used can be modified to evaluate the performance of other GPRs if adequate data are collected. All of the data were reprocessed with the final MH/K automatic target recognition (ATR) algorithm so that performance variations due to environmental conditions could be characterized independently of ATR changes. The model estimates Probability of Detection (Pd) and False Alarm Rate (FAR) for buried mines as a function of ATR confidence, estimated soil moisture content (dry, moist or wet), mine casing (metal or plastic), burial depth (shallow or deep) and soil type (dirt or gravel). Time Domain Reflectometry (TDR) moisture probe measurements at one location augmented with qualitative observations of the soil conditions characterized the soil moisture content. The performance model was created from 52 alarm files collected at a temperate US Army test site over a total of 4 weeks during a 13-month period. The results show that for the MH/K GPR performance against plastic mines in dirt improves as soil moisture increases and performance in gravel is better overall than in dirt.
The thermal signatures of surface and buried land mines vary widely with time of day, weather, soil type, soil moisture content, and mine burial depth. There have been recent advances in modeling these effects, but until these models are fully developed and validated we will continue to rely on measured data. This paper witll present signatures in the medium-and long-wavelength infrared (MWIR and LWIR) spectral bands for surface and buried M19, M15 and VS1.6 anti-tank mines at two locations, a temperate site and an arid site. We will show that the apparent contrast of these landmines is substantial throughout the diurnal cycle except during thermal crossover periods after sunrise and sunset. Our results show that the mine signatures are well above sensor noise and that further improvements in sensitivity or resolution are not required. The paper will also present LWIR images of landmines buried in dirt and gravel road environments taken on a cold winter day and discuss the intriguing and unexpected differences observed between the images of landmines buried in dirt and gravel.
KEYWORDS: General packet radio service, Sensors, Land mines, Mining, Palladium, Forward looking infrared, Infrared sensors, Algorithm development, Metals, Imaging systems
Mine Hunter/Killer (MH/K) is an Advanced Technology Demonstration (ATD) program directed by the Army Night Vision Electronic Sensors Directorate (NVESD). The MH/K system consists of a vehicle-mounted system that detects and neutralizes surface and buried anti-tank (AT) mines. The detection element in this program consists of a Close-In Detection (CID) System that relies on a multi-sensor configuration. The CID System consists of three sensors: a ground penetrating radar (GPR), a metal detector (MD) and a forward-looking IR imaging system. TRW S and ITG has provided support for analysis, testing and algorithm development for Automatic Target Recognition and sensor fusion processing. This paper presents a multi-sensor fusion approach developeby TRW under this effort. In this approach, the incoming alarms from the three sensors are segregate into five classes, based on spatial coincidence of GPR and MD alarms, and on the presence of a surface null in the GPR depth profile. This GPR null, or 'notch', is indicative of shallowly buried objects or clutter, and helps in the discrimination against false alarm density, attempting to maintain a constant false alarm rate. This paper will describe this fusion methodology and the adaptive threshold method in detail, show the target and clutter probability density functions for each class, and show result form recent field test. Fused results will be compared with single sensor performance, and strengths and weaknesses of each sensor will be discussed.
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