Radar automatic target recognition (RATR) based on high resolution range profiles (HRRPs) has received intensive attention in recent years. The data-driven HRRP recognition methods based on neural networks have achieved outstanding performance with a complete target-aspect template library. However, such methods can barely achieve satisfactory performance under the condition of incomplete target-aspects or low signal-to-noise ratio. In order to solve this problem, a prior-knowledge guided neural network (PriorK-NN) is proposed for HRRP target recognition, which incorporates prior-knowledge about scattering centers and target-aspects into neural networks. Firstly, by combining the physical generative mechanism of HRRP with neural networks, we proposed an interpretable scattering center layer (SC-layer). The SC-layer can effectively reduce the adverse effects of noise and extract the location and intensity of target dominant scattering centers. In addition, we proposed a loss function named targetaspect supervised contrastive loss based on the prior-knowledge about target-aspects. By using the proposed loss function, clusters of HRRPs belonging to the same class but with different target-aspects are pulled together, while simultaneously pushing apart clusters of HRRPs from different classes. Therefore, the proposed loss function can improve the recognition performance under the condition of incomplete target-aspects. Experiments on the aircraft electromagnetic simulation dataset and the measured dataset validated the effectiveness of our proposed method on noise-corrupted HRRP recognition and demonstrated superior performance compared with other HRRP recognition methods under the condition of incomplete target-aspects.

Current methods synthetic aperture radar–automatic target recognition (SAR-ATR) research methods still struggle with overfitting due to small amounts of training data, as well as black-box opacity and high computational requirements. Unmanned aerial vehicles (UAVs), as the mainstream means of acquiring SAR data, place higher requirements on ATR algorithms due to their flexible maneuvering characteristics. Our work starts by studying the electromagnetic (EM) backscattering mechanism and the physical properties of SAR. We construct a heterogeneous graph for the first time to fully exploit both the EM scattering (EMS) information of the target components and their interactions. Moreover, the multilevel multihead attention mechanism is introduced to the graph net to learn features from various topological structure levels. Additionally, we include a convolutional neural network (CNN)-based feature extraction net to replenish intuitive visual features. The above two nets form the lightweight dual-stream framework (LDSF). LDSF uses a feature fusion subnetwork (FFS) to adaptively fuse the dual-stream features to maximize the final classification performance. The experiments use two more rigorous evaluation protocols on MSTAR and OpenSARShip, namely, once-for-all (OFA) and less-for-more (LFM), which can rigorously assess the efficacy and generalization capability of the algorithms. The superiority of LDSF is verified.

Radar high resolution range profiles(HRRP) can represent abundant target structure signatures, which has found wide-spread applications. Recently, radar automatic target recognition(RATR) methods based on deep neural network have achieved promising results because of their strong generalization ability. However, these deep models usually require a large amount of labeled data to optimize the parameters, otherwise they would possibly encounter severe overfitting problem in few-shot condition. In order to solve the above problem, a novel few-shot HRRP target recognition method based on meta-learning framework is proposed, which introduces Long Short-Term Memory(LSTM) based neural network as learner for statistical HRRP data. The proposed method exploits multi-polarization HRRP data for RATR and successfuly improves recognition accuracy and generalization performance in few-shot condition. In the proposed method, a novel learner is designed, which is more suitable for processing one-dimensional statistical HRRP data. We evaluated our method based on an electromagnetic calculation dataset of airplanes and found that the proposed method could successfully fuse multi-polarization HRRP data to provide more effective information for RATR. The experimental results also showed that the proposed method produced improved performance compared with state-of-the-art few-shot learning methods.

The ambiguity function is a useful tool for assessing the response performance of radar waveforms. This paper addresses the problem of shaping the radar ambiguity function by designing the unimodular transmit waveform. We model the waveform design process as a non-convex quartic optimization problem aimed at minimizing the disturbance power of scatters in specific range-Doppler bins. Rather than using conventional methods like approximating and relaxing the objective function and constraints, we address the resultant optimization problem under the Riemannian manifold optimization framework and introduce a novel gradients-based algorithm called Quartic Riemannian Adaptive Regularization with Cubics. The developed algorithm applies the Riemannian gradient and Riemannian Hessian of the objective function to conduct iterative operations that gradually decrease the cost function value. The proposed algorithm has a lower iteration complexity bound in comparison to the classical Riemannian trust region method based on the second-order gradient. Numerical experiments demonstrate that our developed method can achieve a relatively higher signal-to-interference ratio with a faster convergence speed.

In practice, adaptive beamforming usually faces non-ideal situations where a limited number of snapshots are available, the training data are corrupted by desired target signals and the array mismatches exist. Traditional methods often degrade significantly under the above situation. In order to solve this problem, a new adaptive beamforming method based on atomic-norm optimization technique is proposed in this paper. In the proposed method, the interference subspace is estimated by minimizing the rank of interference data matrix while making the signals bounded within a ball of Frobenius norm around the observed data. This non-convex problem is solved using alternative optimization which decomposes it into two iterative steps. Each step can be formulated as semi-definite programming and solved efficiently. Unlike traditional methods, the proposed method can estimate the target signals, target directions and interference subspace simultaneously. This property guarantees that the proposed beamformer is free from the influence of target signals and able to adjust pointing direction adaptively. Then it is derived theoretically that the estimation of interference subspace in the proposed method is consistent and bounded. A fast implementation algorithm based on alternating direction method of multipliers is also derived. Compared with traditional methods, the proposed method not only performs better with target-contaminated training data and erroneous prior of target direction, but also requires much less snapshots to work. The effectiveness of the proposed method and its advantages over traditional methods are verified based on simulated and actual measured radar data.

The smeared spectrum (SMSP) jamming technique generates dense comb-shaped false targets at the receiver, making it difficult to detect the target of interest. Waveform design introduces greater flexibility at the transmitter, offering the potential for active anti-jamming strategies. This paper investigates countermeasures to SMSP jamming in multiple-input multiple-output (MIMO) radar systems by designing phasecoded frequency-modulated (PCFM) waveforms. Moreover, the complementary PCFM waveform design scheme is considered to further expand the design freedom, utilizing the coherence between multiple pulses to achieve improved performance. Specifically, we first formulate the complementary waveform design as a multi-objective optimization problem, by unifing the SMSP jamming levels, the waveform sidelobes, and the waveform orthogonality after the matched filter processing at the receiver into a set of fractional terms. Subsequently, the optimal complementary PCFM waveforms are obtained by employing the iterative algorithm based on the approximated Hessian matrix of the objective functions. Numerical simulations are conducted to demonstrate that the designed complementary PCFM waveforms significantly improve SMSP jamming suppression and enhance target detection performance