As synthetic aperture radar (SAR) imaging technology continues to evolve, the growing repository of SAR images depicting diverse types of observed targets has sparked rising interest in SAR target incremental recognition techniques. However, most existing SAR target incremental recognition algorithms typically require an ample amount of training data. In urgent scenarios such as emergency response and disaster relief, there may be a necessity to identify targets for which a substantial amount of data has not been previously accumulated. Algorithms designed for general scenarios often fail to achieve satisfactory performance in such situations. To tackle the aforementioned issues, this paper presents a few-shot incremental recognition algorithm for SAR targets based on orthogonal distributed features. Specifically, an orthogonal distribution optimization method for features is designed, which not only mitigates the feature confusion in few-shot incremental learning, but also reserves space for features of potential unseen classes. A random augmentation method for high-dimensional features is proposed to improve the overfitting problem while assisting in strengthening the boundaries between features of different classes. Furthermore, a joint decision criterion based on Euclidean distance and cosine distance is introduced, enabling the classifier to possess sufficient generalization ability and robustness in handling dynamic data. Experimental results on the MSTAR dataset show that the algorithm outlined in this paper outperforms existing methods in SAR target few-shot incremental recognition tasks, demonstrating its superior effectiveness.

Currently, a set of scaled squat circular cylinders have been widely used for co-polarimetric radar cross section (RCS) calibration in various test ranges where low RCS metal pylons are used. The major drawback of a circular cylinder is its high sensitivity to elevation angle, as well as the frequency-dispersive theoretical RCS. In this work, we propose a new RCS calibrator, the metal ellipsoidal cylinder, whose RCS characteristics are similar to a metal sphere, while it can be mounted on a metal pylon with little electromagnetic (EM) interactions as a circular cylinder does. A high-frequency RCS expression is derived using geometrical optics (GO) for the new calibrator. The scattering mechanisms are analyzed to establish the accurate parametric representation of the theoretical RCS. Based on surface current analysis and numerical calculation, the calibrator-pylon interaction (CPI), the angular sensitivity, and the bistatic RCS characteristics are discussed, enabling the calibration uncertainty evaluation for applying the proposed calibrator in ground-plane range (GPR). Numerical and experimental results of a manufactured squat ellipsoidal cylinder (SEC) are presented to demonstrate the excellent performance of the proposed ellipsoidal cylinder calibrator for co-polarimetric RCS calibration.

Physically based multispectral image simulation consists of sensor system modeling, bottom-of-atmosphere (BOA) image generation, and top-of-atmosphere (TOA) image calculation. TOA radiance images are usually generated using a lookup table (LUT) for computational efficiency, which is calculated by means of atmospheric radiative transfer codes with different combination of input variables, including viewing zenith, solar zenith, and relative azimuth angles; visibility; columnar water vapor; and ground elevation. In this paper, a new strategy is proposed for TOA radiance image simulation, where transmitted surface radiance and atmospheric radiance at the TOA are calculated, respectively, to improve accuracy as well as efficiency. The transmitted surface radiance image is obtained from pixel-by-pixel calculation of BOA radiance and path transmittance. In calculating the atmospheric radiance of TOA, two LUTs are built for the emitted and the scattered radiance from each atmospheric layer, respectively. The effects of visibility and columnar water vapor on the atmospheric radiance are characterized by means of an equivalent path transmittance, which is related to the scene geometry as well as the thickness of atmospheric layer. In this way, when a new scene is simulated, except for three variables, i.e., viewing and solar zenith angles and atmospheric layer number, other parameters are set as constants in building the LUTs, enabling more combinations of input variables without adding excessive computational burden. Multispectral images in different bands with moderate spatial resolution are simulated and compared with the moderate-resolution imaging spectroradiometer (MODIS) images to demonstrate the accuracy and the usefulness of the proposed strategy.

In this article, a polarimetric multiple input multiple output (MIMO) radar with a linear antenna array including eight transmitters and 12 receivers is developed and calibrated for near-field imaging measurement in frequency band spanning from 26.5 to 38 GHz. In the system hardware configuration, the single-polarized antennas are connected to a vector network analyzer (VNA) through a radio frequency (RF) switch matrix as a compromise of performance and expense. A two-step system calibration procedure is proposed in the compensation of signal distortion, where the measured polarimetric data of specified calibrators is compared with the simulation results at each channel to solve the compensation coefficients for frequency response. The measured 2-D radar images are generated by means of back projection (BP), in which the weight coefficient of each channel is calculated to minimize the difference in the simulation counterparts. The full polarimetric data of vehicles is acquired and processed to demonstrate the usefulness of the developed system along with its calibration procedure. The 2-D images generated in different frequency bands show that the dynamic range is about 40 dBsm for horizontal-horizontal (HH) and vertical-vertical (VV) polarizations, while it is around 30 dBsm for vertical-horizontal (VH) and horizontal-vertical (HV) polarizations. Meanwhile, the cross-polarization isolation of the developed system is better than 31 dBsm at the current state.

Fully polarimetric multiple input multiple output (MIMO) radars are getting more and more applications for near-field polarimetric scattering matrix (PSM) imagery. A polarimetric MIMO radar consists of a large number of transmitting and receiving channels. The crosstalk and imbalance among the channels severely distort the measured PSM. In this article, a fully polarimetric calibration technique for a near-field MIMO imaging radar is proposed using a single passive calibrator named quarter concave cylinder linked dihedral (QCCLD) reflector which was presented in our previous work. The bistatic PSM of the QCCLD is derived based on physical optics (PO) approximation. For a QCCLD rotating around the line of sight (LOS) between the centers of the MIMO array and the calibrator, higher order scattering components are excited under bistatic transceiver antennas. Besides, a polarimetric calibration procedure for near-field MIMO imaging radar is developed. Utilizing Fourier analysis, three main scattering components (MSCs) of the QCCLD are extracted, while the undesirable scattering components which degrade the calibration accuracy are suppressed. Outdoor experiments are conducted using a polarimetric near-field MIMO imaging radar over 12–18 GHz frequency band. It is shown that after fully polarimetric calibration using the QCCLD, an improvement of more than 12 dB in polarization isolation is obtained, validating the proposed technique. Further analysis using polarimetric decomposition of the calibrated PSM images for both the Sandia laboratories implementation of cylinders (SLICY) benchmark target and a Volvo sport utility vehicle (SUV) demonstrate the usefulness in identifying scattering mechanisms.