Frequency-modulated continuous-wave (FMCW) radar plays a pivotal role in the field of remote sensing. In low-altitude environments, where UAV-borne FMCW radars are increasingly used, mutual interference poses significant challenges to radar performance. In this paper, a novel FMCW radar interference mitigation (RIM) method, termed as RIMformer, is proposed by using an end-to-end Transformer-based structure. In the RIMformer, a dual multi-head self-attention mechanism is proposed to capture the correlations among the distinct distance elements of intermediate frequency (IF) signals. Additionally, an improved convolutional block is integrated to harness the power of convolution for extracting local features. The architecture is designed to process time-domain IF signals in an end-to-end manner, thereby avoiding the need for additional manual data processing steps. The improved decoder structure ensures the parallelization of the network to increase its computational efficiency. Simulation and measurement experiments are carried out to validate the accuracy and effectiveness of the proposed method. Extensive simulations and empirical measurements demonstrate that RIMformer significantly improves interference mitigation and signal recovery, which advances the reliability and effectiveness of UAV-borne FMCW radars in complex environments.

Automotive radars are widely deployed in advanced driver assistance systems (ADASs) and autonomous driving. However, the increased vehicles equipped with radars make mutual interferences in automotive radars become an nonnegligible issue. The incoherent interferences between automotive frequency modulated continuous wave (FMCW) radars can significantly increase the noise level, leading to the degeneration of target detection. To address the problem, this article proposes an interference suppression approach based on the Fractional Fourier Transform (FRFT). It is noted that the interference component in the intermediate frequency (IF) signal exhibits chirp-like characteristics, which shows the energy concentration in a FRFT domain. Therefore, the interference component can be easily filtered out in the FRFT domain. Additionally, to decrease the computational cost in the presence of multiple interferences, a two-dimensional (2-D) constant false alarm rate (CFAR) detector is utilized to detect interference components. The proposed approach is also effective in scenarios with a large proportion of interference samples. Both simulations and field experiments demonstrate the performance of the proposed approach

This paper presents an neural network based approach to identify in real time faulty components found on industrial brushless exciters. A brushless exciter or ldquorotating rectifierrdquo is a key component of a synchronous motor. Improper operation of this component can prove costly for the motor's owner. A method is based on Fourier analysis combined with the use of neural networks is presented to detect some common failures involving a three phase rotating rectifier. A laboratory setup was constructed to create fault condition data sets. These data sets were used to determine a preprocessing technique in conjunction with an appropriate neural net structure and training algorithm. Robustness of the system was tested using various levels of measurement noise to good result.

Radio frequency power amplifier (PA) is an important part of the transmitter system, which can drive numerous output devices. However, the nonlinear characteristics of PA will cause serious harmonic interference, which leads to electromagnetic interference (EMI) problems. In this article, the nonlinear characteristics and the memory effect of PA are analyzed. The strong nonlinearity region and the weak nonlinearity region are divided according to the strength of the nonlinearity. For the strong nonlinearity, an encoder–decoder-based (E-D-based) artificial neural network model is proposed to predict the harmonic interference of PA. To promote the prediction of high-order harmonics when the input signal is small, a multilayer perceptron model is used for the weak nonlinearity region. The models can effectively predict the first five harmonics of PA, in which the mean absolute error of the fundamental wave is about 0.1 dB, the one of the second-order and the third-order harmonics is about 0.5 dB. Since transfer learning (TL) can simplify the training of the model based on the similarity of different tasks, TL based on model transfer is used to predict the harmonic interference of other PAs according to the existing models. The amount of data required for the modeling of PA can be greatly reduced and the accuracy of prediction can be guaranteed by applying TL. Ultimately, the proposed method can predict the harmonic interference rapidly and accurately according to the known excitation signal so that corresponding measures can be taken to avoid the influence of radiated spurious emission on the use of the sensitive receiving devices in the same electromagnetic environment.

Frequency-modulated continuous-wave (FMCW) millimeter-wave (mmWave) radar systems are increasingly utilized in environmental sensing due to their high range resolution and robust sensing ability in severe weather environments. However, mutual interference among radar systems significantly degrades the target detection capability. Recent advancements in interference mitigation utilizing deep learning (DL) approaches have demonstrated promising results. DL-based approaches typically have high computational costs, which makes them unsuitable for real-time applications with strict latency requirements and limited computing resources. In this paper, we propose an efficient solution for real-time radar interference mitigation. A lightweight transformer, which is smaller and faster than the baseline transformer, is designed to reduce interference. The integration of linear attention mechanisms with depthwise separable convolutions significantly reduces the network’s computational complexity while maintaining a comparable performance. In addition, a two-stage knowledge distillation (KD) process is deployed to compress the network and enhance its efficiency. The staged distillation approach alleviates the training difficulties associated with substantial differences between the teacher and student networks. Both simulated and real-world experiments demonstrate that the proposed method outperforms the state-of-the-art methods while achieving high processing speeds.

Frequency-modulated continuous-wave (FMCW) radar plays a pivotal role in the field of remote sensing. The increasing degree of FMCW radar deployment has increased the mutual interference, which weakens the detection capabilities of radars and threatens reliability and safety of systems. In this paper, a novel FMCW radar interference mitigation (RIM) method, termed as RIMformer, is proposed by using an end-to-end Transformer-based structure. In the RIMformer, a dual multi-head self-attention mechanism is proposed to capture the correlations among the distinct distance elements of intermediate frequency (IF) signals. Additionally, an improved convolutional block is integrated to harness the power of convolution for extracting local features. The architecture is designed to process time-domain IF signals in an end-to-end manner, thereby avoiding the need for additional manual data processing steps. The improved decoder structure ensures the parallelization of the network to increase its computational efficiency. Simulation and measurement experiments are carried out to validate the accuracy and effectiveness of the proposed method. The results show that the proposed RIMformer can effectively mitigate interference and restore the target signals.