Radars are used for various purposes, and we need flexible methods to explain radar response phenomena. In general,
modeling radar response and backscatterers can help in data analysis by providing possible explanations for
measured echoes. However, extracting exact physical parameters of a real world scene from radar measurements
is an ill-posed problem.
Our study aims to enhance radar signal interpretation and further to develop data classification methods. In
this paper, we introduce an approach for finding physically sensible explanations for response phenomena during
a long illumination. The proposed procedure uses our comprehensive response model to decompose measured
radar echoes. The model incorporates both a radar model and a backscatterer model. The procedure adapts
the backscatterer model parameters to catch and reproduce a measured Doppler spectrum and its dynamics at a
particular range and angle. A filter bank and a set of features are used to characterize these response properties.
The procedure defines a number of point-scatterers for each frequency band of the measured Doppler spectrum.
Using the same features calculated from simulated response, it then matches the parameters-the number of
individual backscatterers, their radar cross sections and velocities-to joint Doppler and amplitude behavior of the
measurement. Hence we decompose the response toward its origin. The procedure is scalable and can be applied
to adapt the model to various other features as well, even those of more complex backscatterers. Performance
of the procedure is demonstrated with radar measurements on controlled arrangement of backscatterers with a
variety of motion states.
During the last decade, the safety regulations of the airports have been set to a new level. As the number of
passengers is constantly increasing, yet effective but quick security control at checkpoints sets great requirements
to the 21st century security systems. In this paper, we shall introduce a novel metal detector concept that
enables not only to detect but also to classify hidden items, though their orientation and accurate location
are unknown. Our new prototype walk-through metal detector generates mutually orthogonal homogeneous
magnetic fields so that the measured dipole moments allow classification of even the smallest of the items with
high degree of accuracy in real-time. Invariant to different rotations of an object, the classification is based
on eigenvalues of the polarizability tensor that incorporate information about the item (size, shape, orientation
etc.); as a further novelty, we treat the eigenvalues as time series. In our laboratory settings, no assumptions
concerning the typical place, where an item is likely situated, are made. In that case, 90 % of the dangerous and
harmless items, including knives, guns, gun parts, belts etc. according to a security organisation, are correctly
classified. Made misclassifications are explained by too similar electromagnetic properties of the items in question.
The theoretical treatment and simulations are verified via empirical tests conducted using a robotic arm and our
prototype system. In the future, the state-of-the-art system is likely to speed-up the security controls significantly
with improved safety.
Geographical information systems (GIS) have been the base for radar ground echo simulations for many years.
Along with digital elevation model (DEM), present GIS contain characteristics of terrain. This paper proposes
a computationally sensible simulation procedure to produce realistic radar terrain signatures in a form of raw
data of airborne pulse Doppler radar. For backscattering simulation, the model of the ground is based on DEM
and built with point-form backscattering objects. In addition to the usual DEM utilization for xyz coordinates
and shadowed region calculation, we assume that each data point in GIS describes several scatterers in reality.
Approaching the ground truth, we distribute individual scatterers with adjustable attributes to produce authentic
response of areas such as sea, fields, forests, and built-up areas. This paper illustrates the approach through an
airborne side-looking synthetic aperture radar (SAR) simulation. The results prove the enhanced fidelity with
realistic SAR image features.
The detection and identification of hazardous chemical agents are important problems in the fields of security
and defense. Although the diverse environmental conditions and varying concentrations of the chemical agents
make the problem challenging, the identification system should be able to give early warnings, identify the gas
reliably, and operate with low false alarm rate. We have researched detection and identification of chemical
agents with a swept-field aspiration condenser type ion mobility spectrometry prototype. This paper introduces
an identification system, which consists of a cumulative sum algorithm (CUSUM) -based change detector and
a neural network classifier. As a novelty, the use of CUSUM algorithm allows the gas identification task to
be accomplished using carefully selected measurements. For the identification of hazardous agents we, as a
further novelty, utilize the principal component analysis to transform the swept-field ion mobility spectra into
a more compact and appropriate form. Neural networks have been found to be a reliable method for spectra
categorization in the context of swept-field technology. However, the proposed spectra reduction raises the
accuracy of the neural network classifier and decreases the number of neurons. Finally, we present comparison
to the earlier neural network solution and demonstrate that the percentage of correctly classified sweeps can be
considerably raised by using the CUSUM-based change detector.
This paper presents a method for generating volumetric clutter for air surveillance radar simulation. Complex
valued radar signal consists of magnitude and phase. In the presented simulation, radar clutter signal is created
from magnitude and phase distribution and then filtered imitating the radar signal formation. Radar geometry
can be integrated to the simulation by manipulating magnitude, phase, and phase difference distributions. Magnitude
is affected by range bin size and distance from radar. Also weather condition and polarization effect on
the signal. These can be controlled with adjustments to the distribution that the matrix is created from. This
solution offers a simple way to create background to realistic radar simulation. Different distributions are used
for signal magnitude and phase of various clutter sources. Typically, volumetric clutter source consists of many
evenly sized scatterers. Preliminary phase, originating from randomly distributed particles, can be considered
evenly distributed. Phase difference in long time, on the other hand, shows the radial movement of particles.
Therefore, phase difference can be modeled, for example, with Gaussian distribution and magnitude with Weibull
distribution, of course, depending on true environment. As an example, chaff is simulated with differing radial
wind.
KEYWORDS: Radar, Signal to noise ratio, Doppler effect, Signal detection, Target detection, Phase shift keying, Signal processing, Antennas, Fourier transforms, Detection and tracking algorithms
A method assuming linear phase drift is presented to improve radar detection performance. Its use is based on the assumption that the target illumination time comprises multiple coherent pulses or coherent processing intervals (CPI). For example in a conventional scanning radar, this often inaccurate information can be used for statistical data mapping to point out possible target presence. If coherent integration is desired in a beam-agile system, the method should allow sequential detection. Discussion involves a pragmatic example on the echo phase progress utilization in the constant false alarm rate (CFAR) processing of a moving target indication (MTI) system. The detection performance is evaluated with scanning radar simulations. The method has been tested using real-world recordings and some observations are briefly outlined.
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