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.
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.
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.
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