Based on measurements of a ship at 17 GHz and on several simulated ships at 35GHz it is demonstrated how multipath
changes the range profiles that form a basis for the construction of ATR features for ship classification. The fluctuation
of range profiles leads to a corresponding fluctuation of feature values that make it difficult to define stable test feature
vectors, and reliable feature references in the training stage.
The most challenging problem of Automatic Target Recognition (ATR) is the extraction of robust and independent target
features which describe the target unambiguously. These features have to be robust and invariant in different senses: in
time, between aspect views (azimuth and elevation angle), between target motion (translation and rotation) and between
different target variants. Especially for ground moving targets in military applications an irregular target motion is
typical, so that a strong variation of the backscattered radar signal with azimuth and elevation angle makes the extraction
of stable and robust features most difficult. For ATR based on High Range Resolution (HRR) profiles and / or Inverse
Synthetic Aperture Radar (ISAR) images it is crucial that the reference dataset consists of stable and robust features,
which, among others, will depend on the target aspect and depression angle amongst others. Here it is important to find
an adequate data grid for an efficient data coverage in the reference dataset for ATR.
In this paper the variability of the backscattered radar signals of target scattering centers is analyzed for different HRR
profiles and ISAR images from measured turntable datasets of ground targets under controlled conditions. Especially the
dependency of the features on the elevation angle is analyzed regarding to the ATR of large strip SAR data with a large
range of depression angles by using available (I)SAR datasets as reference. In this work the robustness of these
scattering centers is analyzed by extracting their amplitude, phase and position. Therefore turntable measurements under
controlled conditions were performed targeting an artificial military reference object called STANDCAM. Measures
referring to variability, similarity, robustness and separability regarding the scattering centers are defined. The
dependency of the scattering behaviour with respect to azimuth and elevation variations is analyzed.
Additionally generic types of features (geometrical, statistical), which can be derived especially from (I)SAR images, are
applied to the ATR-task. Therefore subsequently the dependence of individual feature values as well as the feature
statistics on aspect (i.e. azimuth and elevation) are presented. The Kolmogorov-Smirnov distance will be used to show
how the feature statistics is influenced by varying elevation angles. Finally, confusion matrices are computed between
the STANDCAM target at all eleven elevation angles. This helps to assess the robustness of ATR performance under the
influence of aspect angle deviations between training set and test set.
A high resolution imaging millimetre wave SAR delivers three key parameters important for precision farming
applications, namely range, reflectivity and polarization state. The reflectivity gives information upon the type of crop
and its humidity. Especially in the millimeter wave region young growing green plants exhibit a considerably higher
reflectivity than older, dry leaves. Dependent on the transmit-receive polarization also indications are given upon the
humidity of the underlying soil. Polarimetry also allows to judge the ripeness of the grain as the geometry of the ear is
changing during the ripening process.
Fully polarimetric radars that use polarization diversity on transmit and receive and thus provide the full scattering
matrix, are subject to effects like cross-talk and channel imbalance. These distortions have to be eliminated by means of a
polarimetric calibration in order to warrant compatibility between training data and testing data that were measured at
different times or even by different radar sensors. It is shown for different types of classification features (geometric,
statistical, polarimetric, structural) how an insufficient PolCal may influence the ATR performance.
Three target types, namely T72, ZSU 23-4 and BMP-2 were measured in a tower/turntable configuration in several articulations each. A set of geometric, statistical, structural and polarimetric features is used to study the robustness of classification. Based on the Kolmogoroff-Smirnov distance between histograms a metric is defined that at the same time allows to quantify intra-class robustness and inter-class separability for an individual feature. For sets of several features, a simple classification approach in connection with a reference confusion matrix allows to assess the robustness of classification. It is demonstrated, that averaging the feature reference over all available target articulations improves the classification performance as compared to a reference that is based on one articulation only.
To have an appropriate data base for the development of non-cooperative target identification techniques, airborne measurements were conducted with the mmW-SAR-system MEMPHIS over agricultural terrain with a variety of different fields and canopies of trees. Four different depression angles were used, ranging between 15° and 38°, which allows to determine important clutter parameters as a function of depression angle. During the measurements evaluated here, the transmit polarization was switched from pulse to pulse between horizontal and vertical. Most important for a comparison between different passes is a careful polarimetric calibration. This was done using a statistical method. Absolute amplitude calibration was achieved by means of trihedral corner reflectors. The SAR processing provides the user with several degrees of freedom. Apart from different cross range (Doppler) resolution cell sizes it is possible to create either single look or multi-look images and study the influence of averaging on reflectivity statistics. The results are valuable not only for discrimination and ATR algorithms but also for the development of polarimetric target/clutter simulation models.
The paper describes the experimental set-up and discusses the evaluation methods. Typical results are presented and the implications on ATR methods are highlighted.
In the presentation it will be shown that the classification approach based on polarimetric features supplemented by geometrical information shows promising results using turntable measurements of one of the targets as reference data and airborne measurements of a set of targets as test data. Furtheron the results using the split measurement data sets (split into reference and test sets) will be discussed in detail. The two approaches are compared to each other and their respective advantages and disadvantages summarized.
Using synthetic aperture radars with appropriate signal processing algorithms is a recognized technique for remote sensing applications. A wide spectrum of radar frequencies is used and a high degree of sophistication implies polarimetric and further multichannel approaches. Each frequency band used, exhibits special sensitivities to features of the earth's surface or man-made targets. This is mostly due to the coupling of the electromagnetic waves to backscattering geometries which are related to the radarwavelength. A part of the spectrum which has been covered not very intensely is the millimeterwave region. This may be mostly due to the relatively high atmospheric absorption at millimeterwaves which obstructs the use of such sensors for long range applications. On the other hand for military applications IR-imaging sensors are widely used which suffer even more from adverse transmission properties of the atmosphere. Application of multichannel techniques as polarimetry, multifrequency techniques and interferometry are also done with more ease due to compactness of the hardware and simplicity of processing. As there exist no data which would allow to investigate the potential of multifrequency polarimetric and interferometric mmW-SAR the Millimeterwave Experimental Multifrequency Polarimetric High Resolution Interferometric Imaging System was installed into an aircraft C-160 `Transall' to gather respective data over different land scenarios. The off-line evaluation of the radar data starts with off-line track, calibration and reformatting procedures. Afterwards synthetic aperture processing is applied to these data to generate radar images for co- and cross-polarization at 35 GHz and 94 GHz. As already mentioned above, SAR-processing at millimeterwavelengths requires a considerable lower amount of sophistication in comparison with algorithms applied at lower radar-frequencies. This can mainly be attributed to the short aperture length at mm-wave frequencies. Taking this into account, the SAR-algorithm used here is relatively simple although fully automatic autofocussing is applied, using only radar-data without supply of external INS information. The interferometric evaluation uses phase unwrapping techniques tailored to the high resolution achieved at mm-waves. The paper describes experiments with the interferometric 35/94-GHz-SAR, describes the IFSAR and phase unwrapping algorithms as well as polarimetric segmentation approaches and shows respective results.
Different types of anti-tank surface mines were measured on a turntable in a n anechoic chamber, and modelled with the German SIMPRASS model. In addition, several flights over a realistic minefield were performed with a 35 GHz and a 94 GHz radar operated in parallel in a SAR configuration. It is found that typical RCS values of AT mines at mmW frequencies are in the region between -18dBsm and -30dBsm. Therefore, they are detectable by airborne mmW radar only for a resolution cell size comparable to the physical size of the mine itself depending on the background clutter type.