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
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.
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.