It is practical and efficient to simplify targets to point scatterers in radar simulations. With low-resolution radars, the
radar cross section (RCS) is a sufficient feature to characterize the scattering properties of a target. However, the RCS
totals the target scattering properties to a scalar value for each aspect angle. Thus, a more detailed representation of the
target is required with high-resolution radar techniques, such as Inverse Synthetic-Aperture Radar (ISAR). In
straightforward simulation scenarios, high-resolution targets have been modeled placing identical point scatterers in the
shape of the target, or with a few dominant point scatterers. As extremely simple arrangements, these do not take the
self-shadowing into account and are not realistic enough for high demands.
Our radar response simulation studies required a target characterization akin to RCS, which would also function in highresolution
cases and take the self-shadowing and multiple reflections into account. Thus, we propose an approach to
converting a 3-dimensional (3D) surface into a set of scatterers with locations, orientations, and directional scattering
properties. The method is intended for far field operation, but could be adjusted for use in the near field. It is based on
ray tracing which provides the self-shadowing and reflections naturally. In this paper, we present ISAR simulation
results employing the proposed method. The constructed scatterer set is scalable for different wavelengths enabling the
fast production of realistic simulations including authentic RCS scattering center formation. This paper contributes to
enhancing the reality of the simulations, yet keeping them manageable and computationally reasonable.
For some time, applying the theory of pattern recognition and classification to radar signal processing has been
a topic of interest in the field of remote sensing. Efficient operation and target indication is often hindered by
the signal background, which can have similar properties with the interesting signal. Because noise and clutter
may constitute most part of the response of surveillance radar, aircraft and other interesting targets can be seen
as anomalies in the data. We propose an algorithm for detecting these anomalies on a heterogeneous clutter
background in each range-Doppler cell, the basic unit in the radar data defined by the resolution in range, angle
and Doppler. The analysis is based on the time history of the response in a cell and its correlation to the
spatial surroundings. If the newest time window of response in a resolution cell differs statistically from the
time history of the cell, the cell is determined anomalous. Normal cells are classified as noise or different type of
clutter based on their strength on each Doppler band. Anomalous cells are analyzed using a longer time window,
which emulates a longer coherent illumination. Based on the decorrelation behavior of the response in the long
time window, the anomalous cells are classified as clutter, an airplane or a helicopter. The algorithm is tested
with both experimental and simulated radar data. The experimental radar data has been recorded in a forested
landscape.
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.
In the recent years, radar land clutter modelling and processing have been aided with Geographic Information Systems
(GIS) and geodata in a few recognised researches such as in the Lincoln Laboratory. In our clutter research, one aspect
is to study the possibilities of using GIS in clutter classification in Finnish environment. Since the automation of this
process causes inaccurate results and a need to identify and label various types of land clutter sources through
geographic data (geodata) exists, we propose an approach based on the visual interpretation of clutter. We have created
a graphical visualisation tool for merging geodata with radar data interactively, including an option to select the shown
type(s) of geodata. The source identification is based on the visual observation of the output. The tool can also be
utilised when verifying simulated data.
In an example case, we have used the following geodata items: a base map, a terrain model, a database of tall structures,
and a digital elevation model, but other types of geodata can be used as well. Although the potential to enhance the
model is higher when more types of geodata are utilised, even with few carefully selected geodata items, clutter sources
can be recognised adequately. This paper presents an illustrative demonstration using an air surveillance radar
recording. This visual approach with the data merging tool has been useful, and the results have verified the
practicability. The contribution of this paper focuses on supporting clutter classification research and improving the
understanding of land clutter.
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 strength of radar response varies considerably. In this regard, the dynamic range of most receivers is not sufficient enough to operate optimally. Due to this fact, radar signal may represent only a fraction of the real backscattering phenomena. One way to solve the problem is to use automatic gain control (AGC). It helps to prevent the saturation of responses but inflicts performance degradation on subsequent radar signal processing. The same problem with dynamic range exists in other fields of sensing as well. For example, a solution in digital photography is to use various exposure times to determine the most appropriate one for the current conditions. In this paper, a corresponding approach is proposed for analyzing radar responses. The method requires measurements of a selected area to be performed with various gains, and the resulting dynamic ranges should overlap partially. The use of a linear receiver ensures that both the power and the coherent phase statistics can be extracted from the data. Using the proposed approach, a few distributions derived from extensive land clutter recordings from Finnish landscape are presented.
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: Cameras, Surveillance, 3D modeling, Geographic information systems, Systems modeling, Visibility, RGB color model, Photography, Sensors, Imaging systems
Surveillance camera automation and camera network development are growing areas of interest. This paper
proposes a competent approach to enhance the camera surveillance with Geographic Information Systems (GIS)
when the camera is located at the height of 10-1000 m. A digital elevation model (DEM), a terrain class
model, and a flight obstacle register comprise exploited auxiliary information. The approach takes into account
spherical shape of the Earth and realistic terrain slopes. Accordingly, considering also forests, it determines
visible and shadow regions. The efficiency arises out of reduced dimensionality in the visibility computation.
Image processing is aided by predicting certain advance features of visible terrain. The features include distance
from the camera and the terrain or object class such as coniferous forest, field, urban site, lake, or mast. The
performance of the approach is studied by comparing a photograph of Finnish forested landscape with the
prediction. The predicted background is well-fitting, and potential knowledge-aid for various purposes becomes
apparent.
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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