In this letter, a new method is proposed for reducing the radar cross section (RCS) of an antenna array. The RCS is reduced by optimizing the spatial arrangement of the antenna array, which is treated as a classification problem. To achieve this, an RCS excitation matrix is generated using radar signal direction of arrival (DOA) and array information. This matrix is then input into a carefully designed light kernel residual network (LK-ResNet) to produce a switch matrix. Light kernels can increase the number of channels and nonlinearity with lower time complexity. The numerical and simulated results show that the proposed RCS excitation matrix provides much higher classification accuracy than the initial data. The LK-ResNet offers superior stealth performance when compared to other networks. Furthermore, the proposed method is also validated with measurement.

Electrically small, Hilbert fractal, near-field resonant parasitic (NFRP) antennas are reported and investigated in this paper. This type of electrically small antennas (ESAs) consists of Hilbert fractal curves as the NFRP resonators and small vertical monopoles as the driven elements. By offsetting the driven monopole off the centre position, good impedance matching can be easily realized. The current distributions on the fractal NFRP elements and the driven monopoles manifest that strong electric coupling could excite loop-mode currents along the Hilbert fractal NFRP structures, and thereby horizontal magnetic monopole patterns with broadside radiation property are realized. The results show that the 2nd-step evolution Hilbert fractal NFRP antenna witnesses an electrically size reduction of 25% compared to ka = 0.9622 of the square capacitively loaded loop (CLL) NFRP antenna, while maintaining its original broadside radiation. It is also demonstrated that there is a performance trade-off, i.e. higher fractal iteration orders leading to smaller resonant frequencies, narrower bandwidth and lower radiation efficiencies.

The tripole vector antenna comprises three orthogonal dipole antennas, so it could completely capture all the electric field of the incident electromagnetic (EM) wave. Then, the electric field information could be used to estimate the direction of arrival (DOA) of the EM wave if two conditions are satisfied. One is that there exists only one single EM wave in space. The other is that the EM wave is elliptically or circularly polarized. The new estimation method obtains two snapshot vectors through the output of a tripole antenna and computes their cross-product vector. Furthermore, the direction of the cross-product vector is used to estimate the DOA of the EM wave directly. We analyze the statistical characteristics of the DOA estimation error to prove that the new scheme is an asymptotic unbiased estimation. Furthermore, unlike the existing Multiple Signal Classification (MUSIC)-based algorithms, the proposed approach only need one tripole vector antenna instead of an antenna array. Meanwhile, the new method also outperforms existing MUSIC-based algorithms in the term of computational complexity. Finally, the performance and advantages of the proposed method are verified by numerical simulations.

This article presents a novel optimization algorithm for large array thinning. The algorithm is based on Discrete Particle Swarm Optimization (DPSO) integrated with some different search strategies. It utilizes a global learning strategy to improve the diversity of populations at the early stage of optimization. A dispersive solution set and the gravitational search algorithm are used during particle velocity updating. Then, a local search strategy is enabled in the later stage of optimization. The particle position is adaptively adjusted by the mutation probability, and its motion state is monitored by two observation parameters. The peak side-lobe level (PSLL) performance, effectiveness and robustness of the improved PSO algorithm are verified by several representative examples.

The existing deep learning methods for radar cross section (RCS) reduction are unsuitable for large-scale sparse arrays because their large scales result in large numbers of classes and high network complexity levels. This article proposes a new deep learning method to solve these problems. First, a hybrid-interval particle swarm optimization (HIPSO) algorithm is presented. The number of classes is reduced using the presented HIPSO algorithm, which adaptively adjusts the sampling interval. Then, a fully convolutional shortcut network based on an attention mechanism (AM-FCSN) is designed. The optimal spatial arrangement is selected by the designed AM-FCSN. Finally, simulations show that the proposed HIPSO algorithm reduces the number of classes from O (10 ⁴ ) to O (10 ² ). Moreover, compared with different neural networks, the proposed AM-FCSN achieves computational complexity and parameter complexity reductions of 38.46% and 80.2%, respectively, while attaining higher accuracy. The proposed method can effectively reduce the large-scale sparse array RCS in real time.

In a broadband adaptive array antenna with a space-time filter, a delay filter is required before digital beamforming when the Frost algorithm is used to obtain the weight vector. In this paper, we propose a Farrow structure instead of a direct form FIR structure to implement the time delay filter since it can satisfy the demand for real-time update and is very suited for the FPGA platform. Furthermore, a new off-line algorithm to calculate the Farrow filter coefficient is presented if the filter coefficient is symmetric. Finally, simulations are presented to illustrate that the design methodology for the Farrow filter is correct. The simulations also prove that the Frost broadband adaptive antenna effectively mitigates the interferences with a Farrow filter.