The outboard corner reflectors are widely used as a countermeasure to radar tracking and detection. Therefore, it is essential to determine the composite time-domain scattering characteristics of the corner reflector and ship against the sea background and the jamming effectiveness of the corner reflector array. In this paper, the composite electromagnetic scattering characteristics of ship target, corner reflector, and the sea surface are studied based on the time-domain bouncing ray method (TDSBR). The radar echo and broadband radar cross section (RCS) results for the time domain pulse incident are also obtained and the high-resolution range profile (HRRP) is calculated. We then devise an evaluation system based on centroid jamming intensity and range profile similarity to determine the jamming characteristics of passive jamming. We further propose an array composing of three sizes of corner reflectors. Finally, the centroid jamming effectiveness is analyzed by comparing the RCS of the corner reflector array and the ship, and the deception jamming effectiveness is measured by calculating the range profile similarity based on the cosine similarity method and the range profile peak envelope overlap. Simulation results show that the reflector array can achieve a good performance both for centroid and range profile deception. The simulation results given and the evaluation method proposed in this paper provides significant practical insights into the design and analysis of the passive jamming systems.

Chaff cloud is widely used in target covering. In this paper, a jamming performance estimation strategy is presented for the elliptical and cubic chaff clouds. The range profiles of the composite models of the plane with the chaff cloud are experimentally accessed in an anechoic chamber using a stepped-frequency waveform measurement system. Then, dimensions, characteristic length, and distributing entropy of the equivalent scattering centers of the plane and the jammer are extracted from the measured range profiles, respectively. Based on these features, support vector machines, naïve Bayesian classification, and decision tree classification methods are employed to determine the recognition rate of the target under the chaff jamming, and then determine the jamming efficiency of the two kinds of chaff clouds. Results show that two types of chaff clouds are able to reduce the target recognition rate. Besides, when the radar uses the Euclidean distance and decision tree classification methods to complete the recognition task, a good jamming efficiency can be achieved by releasing the elliptical or the cubic chaff cloud. Meanwhile, a low level of jamming performance outcomes using the support vector machines classification method. It is also found that the chaff cloud released from the side of the plane achieves a better jamming performance compared with the other releasing locations. Finally, the influence of environmental noise on the recognition rate of the plane is studied. Estimating results show that the recognition rates decrease with the increasing of the power level of the noise. When the transmitting power level of the noise reaches 1.2 times the reflected power level of the target, it is quite hard to estimate the jamming performance of two kinds of chaff clouds.

The electromagnetic (EM) scattering properties of the chaff cloud in airflow are rarely addressed in previous works. To deal with this issue, two types of the chaff clouds in airflow are first established based on aerodynamics theories. Then, the K-means algorithm is utilized to divide the chaff cloud into several parts according to the elements’ number density. Meanwhile, the EM transmission property of each part is extracted with the help of the method-of-moments (MoM). Based on this, the vector radiative transfer (VRT) method is introduced to estimate the bistatic scattering coefficients of the established chaff clouds with 40 million elements, which takes into account the radiation pattern of the transmitting antenna and the EM coupling between chaff elements. To speed up the VRT program, parallel computing technologies are adopted. Besides, the verifications of the VRT method are proposed by performing simulations with a numerical method and conducting experiments. Comparative investigations indicate that the results predicted by the proposed VRT method have a good agreement with experimental ones with an error of less than 10%.

This paper investigates the covert communication in a D2D underlaying cellular network consisting of a cellular transmitter, a base station, a cellular receiver, multiple colluding wardens, and an underlaid D2D pair with a transmitter and a receiver. For multiple colluding wardens, they fuse their observations to a fusion center (FC) with the maximum ratio combining (MRC) scheme and the equal gain combining (EGC) scheme. To protect the covert communication between the cellular transmitter and the base station from being detected by multiple colluding wardens, the underlaid D2D transmitter reuses the spectrum of the cellular user to transmit its signal to the D2D receiver with a random power to deliberately confuse multiple colluding wardens. We first provide the basic theoretical results for the detection performance of FC under MRC and EGC, respectively, i.e., the optimal detection threshold, the minimum detection error probability and its average value. We then explore the modeling of the average covert rate as well as the optimal power control for average covert rate maximization to enhance the covert performance under MRC and EGC. Finally, extensive numerical and simulation results are presented to validate theoretical analysis and also to illustrate the effects of some important system parameters on the average minimum detection error probability, the average covert rate, and the maximum average covert rate under MRC and EGC, respectively. We can find that the detection performance of multiple colluding wardens under the MRC scheme is better than that under the EGC scheme.

A fast adaptive surrogate modeling technique for analyzing the target's radar cross section (RCS) response versus frequency is proposed based on Gaussian process regression (GPR). Specifically, an iterative process of modeling and sampling, which seeks the representative points (such as extreme points and inflection points) of the RCS curve, is presented to adaptively determine the required samples and progressively improve the modeling fidelity of GPR. Validation experiments based on two exemplary targets are performed. Compared with the traditional GPR-based surrogate modeling technique employing a one-shot sampling strategy, the proposed adaptive GPR-based surrogate modeling technique further reduces the computational workload (more than 30%) while maintaining high accuracy.

Calibration isan important step in measuring the electromagnetic scattering from a target. Metal spheres are usually selected as calibrators in the calibration process. However, real spherical calibrators are not perfect spheres, which cause calibration errors due to their geometric irregularity and thus affect the radar cross section (RCS) prediction accuracy for the target. A method of estimation and compensation based on high-resolution range profiles (HRRPs) of calibrators is proposed. First, the scattering of a real-world spherical calibrator is measured using the sweep-angle and the sweep-frequency strategies. Then, the error curves are obtained by comparing the HRRPs of the real-world calibrator and its theoretical solutions at each incident angle, which are also used to compensate the RCS error of the real-world spherical calibrator. Finally, the corrected RCS of the calibrator is adopted to access the accurate RCS of the target being tested. The results of simulation and testing show that the proposed method reduces the root mean square error of the RCS of the target by 40%, which demonstrates the effectiveness of the presentedmethod.