Radar is a core sensing tool in the fields of modern defense, aerospace, and civil monitoring, and its performance improvement has always been a research hotspot in the field of signal processing. However, the traditional radar signal processing system mainly relies on the assumption of linear time-invariant, and its performance is seriously degraded in complex scenes such as low observable target detection, strong mainlobe interference, and non-Gaussian clutter. Therefore, beyond linear processing (BLiP), a new applied research project at the algorithm level, has been paid more and more attention. BLiP focuses on improving the radar performance with the usage of signal processing techniques that go beyond current linear signal processing algorithms. This study addresses the potential for radar performance improvement of BLiP in areas such as waveform adaptive scheduling and design, clutter suppression, adaptive interference cancellation, direction of arrival (DOA) estimation, target detection, high-resolution imaging, and target tracking, and provides an outlook on future trends of BLiP in radar signal processing.
Agile imaging satellites play important roles in gathering Earth surface information due to their expansive field-of-view, high-resolution observations, and flexibility in observation timing. Genetic algorithm (GA) has been one of the most commonly utilized meta-heuristic algorithms for solving multiple agile satellites (MAS) scheduling problems due to its stable and good performance. Nonetheless, the iterative convergence speed of GA is slow owing to the blindness of the preliminary search. To address this issue, this paper proposes an efficient meta-heuristic MAS scheduling method integrated with a neural network (NN). In the proposed method, an NN is applied to learn the historical scheduling experience and generate effective initial solutions, thereby improving the convergence speed of GA for MAS scheduling. Experimental results demonstrate that the proposed NN-guided GA (NN-GA) accelerates the convergence of GA by approximately 60% and exhibits higher efficiency for MAS scheduling compared to existing state-of-the-art methods.
The dramatic rise in vehicle numbers and the development of autonomous driving technology have underscored the importance of accurate environment perception to prevent traffic accidents. Due to its distinctive advantages, millimeter-wave radar has emerged as the dominant type of vehicle-mounted sensor. However, frequency band for automotive radar is restricted to 76−81 GHz, resulting in insufficient spectrum resources because of the high-resolution requirement. The rapid increase of radars in the actual traffic environment will inevitably lead to mutual interference between radars, posing a potential threat to public transportation. To address this problem, this paper investigates the mutual interference of automotive frequency modulated continuous wave (FMCW) radars, and mitigates the mutual interference utilizing a low-rank tensor model. By stacking the short-time Fourier transform (STFT) matrix of all chirps in a single channel as tensors, a tensor robust principal component analysis (TRPCA) algorithm is employed to separate the target signal and the interference. Mutual interference of multiple input multiple output (MIMO) radar could be suppressed by applying TRPCA in all channels. Both simulations and numerical experiments demonstrate that the proposed method achieves higher signal to interference plus noise ratio (SINR) and lower root mean square error (RMSE) compared with other methods, thereby proving its superior interference suppression capability in different practical scenarios.
This paper proposes a two-dimensional (2D) angular super-resolution framework for sparse arrays under the single snapshot condition. The azimuth-elevation 2D angular super-resolution model is established, which shows the relationship between the 2D angular super-resolution image and the signal. Using this model, the 2D angular super-resolution problem is transformed into the beyond linear optimization problem. In order to efficiently and accurately address this optimization problem, we propose the sparse multi-layer iterative (SMLI) algorithm based on beyond linear signal processing (BLiSP) theory. During the layered iterative process, the solution region is continuously narrowed. The constructed nonlinear weighting matrix and sparse constraint coefficient enhance the ability to differentiate between the effect of signal and noise in solving the beyond linear optimization problem. Additionally, the nonlinear weighting matrix can be adaptively updated during the solving process, ensuring high-performance angular super-resolution results under different signal-to-noise ratios (SNRs). Experiments confirm the proposed method’s effectiveness and robustness.
Direction-of-arrival (DOA) estimation provides the angle information about interesting targets for array radar systems. However, conventional algorithms are designed for line-of-sight (LOS) scenarios and thus cannot be used to estimate non-LOS (NLOS) targets. Recently, intelligent reflecting surface (IRS) has been applied to DOA estimation for NLOS environments. This paper proposes a geometric model utilizing IRS for DOA estimation in NLOS scenarios. First, we establish an IRS-aided DOA estimation framework, where one-pair-IRSs is used to manipulate the propagation direction of the detection signals. Then, we formulate the estimation task using the elliptic positioning technique. Subsequently, we derive a closed-form solution for the resultant problem. Furthermore, we improve the architecture using multi-pair-IRSs and then design a binary weighting method to enhance the accuracy of DOA estimation. In comparison with the existing methods, the proposed algorithm is able to estimate two-dimensional (2D) DOA for NLOS targets. Numerical simulations are conducted to validate the effectiveness of IRS for DOA estimation in NLOS environments.
