In this paper the phase response and reflection coefficient notch of a metal/anomaly detector design that operates in the high to very high frequency range is studied. This design uses a high-Q tuned loop antenna for metal/anomaly detection. By measuring the reflection coefficient or voltage standing wave ratio a frequency notch can be detected. Tuning to the optimal location for detector performance can be accomplished by monitoring both the depth of the notch and the phase response. It has been experimentally observed that there are three regions of interest relative to the notch and phase response of the detector. One is at the frequency where the phase response is on a near vertical line of substantial phase shift and the notch is near its deepest depth. The second and third are at slightly higher and lower frequencies, where the slope of the phase shift line is reduced and the notch is still deep, but slightly removed from the frequency of maximum depth. As would be expected, initial experimentation indicates that the region of maximum detection performance, in terms of relative change in phase response, occurs when the phase response is at the center of the near vertical phase shift response near the location of the deepest notch. However, there may be advantages to the other two regions, since the response is more stable and less prone to false alarms. Performance results for various combinations of phase response and notch depth will be shown.
Typical metal detectors work at very low to low frequencies. In this paper, a metal/anomaly detector design that operates in the high to very high frequency range is presented. This design uses a high-Q tuned loop antenna for metal/anomaly detection. By measuring the return loss or voltage standing wave ratio a frequency notch can be detected. Tuning to the optimal location of the notch can be accomplished by monitoring the phase response. This phase monitoring technique can be used to ground balance the detector. As a metal object is moved along the longitudinal axis of the loop antenna a substantial shift in the frequency of the notch is detected. For metal targets, the frequency shift is positive, and for ferrite and other targets, the frequency shift is negative. This frequency shift is created by the proximity of the target causing a change in the impedance of the antenna. Experiments with a prototype antenna show long-range detection with low power requirements. The detector requires only one loop with one winding which is used for both transmit and receive. This allows for a metal/anomaly detector with a very simple design. The design is lightweight and, depending on loop size, significantly increases detection depth performance. In the full paper, modeling and further experimental results will be presented. Performance results for various types of soil and for different types of targets are presented.
Currents on remote metallic objects such as landmines can be induced by projecting strong magnetic fields. These currents result in electromagnetic fields that can be subsequently detected. The magnetic field varies slowly as it passes from air into the ground and is sufficient to excite currents in buried metallic objects. Traditionally strong magnetic fields are produced using short-range transformer like inductive coupling, or as a component of powerful propagating electromagnetic fields. The strength of the magnetic component of the propagating electromagnetic field is restricted by regulatory limits on the total radiated radio frequency power. There is a need for a means to produce forward projected strong magnetic field at medium ranges with low-level propagation. This paper reports on a non-radiating loop antenna which maintains a constant amplitude and phase current around the loop and projects a strong magnetic field. The radiated field is small and results from the relativistic time-of-flight effect from one side of the loop to the other. The result is that a very strong magnetic field is produced in the near- to mid-field region, up to one wavelength away from the loop. Experiments with a prototype antenna and modeling show that the H-field is very high, radiated electromagnetic fields are negligible, and the drop off in field strength is inversely proportional to the distance squared. This agreement between experiments and modeling allows for a design based on computer simulations.
Hyperspectral imagery is often visualized as a three-dimensional image cube (two spatial dimensions and one spectral). When a hyperspectral sensor is set to stare at a fixed location a fourth dimension (time) is created as each new cube is sampled in time. In a ground-based stare-mode geometry each new cube has near perfect spatial registration with the previous data cubes. The problem with standard spectral-only hyperspectral detection algorithms is that they do not make effective use of temporal information. In this paper we combine temporal-differencing with temporal-spectral detection algorithms. The temporal-differencing allows for removal of most of the background prior to temporal-spectral detection. The temporal-spectral approaches combine temporal information with standard spectral-only statistical methods. By combining temporal-differencing with temporal-spectral information we are able to significantly improve detector performance and reduce the false alarm rate. We demonstrate the performance of these methods using data from the FIRST (Field-Portable Imaging Radiometric Spectrometer Technology). All the computer simulations and field data experiments show that temporal-differencing improves performance, inclusion of temporal-spectral information improves performance, and that the combination of temporal-differencing with temporal-spectral information greatly improves performance.
Most subpixel detection approaches require either full or partial prior target knowledge. In many practical applications, such prior knowledge is generally very difficult to obtain, if not impossible. One way to remedy this situation is to obtain target information directly from the image data in an unsupervised manner. In this paper, unsupervised target subpixel detection is considered. Three unsupervised learning algorithms are proposed, which are the unsupervised vector quantization (UVQ) algorithm, unsupervised target generation process (UTGP) and unsupervised NCLS (UNCLS) algorithm. These algorithms produce necessary target information from the image data with no prior information required. Such generated target information is referred to as a posteriori target information and can be used to perform target detection.
Target detection in remotely sensed images can be conducted spatially, spectrally or both. The difficulty of detecting targets in remotely sensed images with spatial image analysis arises from the fact that the ground sampling distance is generally larger than the size of the targets of interest in which case targets are embedded in a single pixel and cannot be detected spatially. Under this circumstance target detection must be carried out at subpixel level and spectral analysis offers a valuable alternative. This paper compares two constrained approaches for subpixel detection of targets in remote sensing images. One is a target abundance-constrained approach, referred to as the nonnegatively constrained least squares (NCLS) method. It is a constrained least squares linear spectral mixture analysis method which implements a nonnegatively constraint on the abundance fractions of targets of interest. A common drawback of linear spectral mixture analysis based methods is the requirement for prior knowledge of the endmembers present in an image scene. In order to mitigate this drawback, the NCLS method is extended to create an unsupervised approach, referred to as the unsupervised nonnegatively constrained least present in the image scene. The second approach is a target signature-constrained method, called the constrained energy minimization (CEM) method. It constrains the desired target signature with a specific gain while minimizing effects caused by other unknown signatures. Data from the HYperspectral Digital Imagery Collection Experiment (HYDICE) sensor are used to compare the performance of these methods.