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.
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.
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