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

Inaccurate measurements of analog sensors caused by electromagnetic interference (EMI) signals threaten the security and reliability of sensor-based systems. This article proposes a self-check method for recognizing and correcting disturbed measurements of analog sensors under EMI signals. The core basis of this method involves the use of a dummy sensing element to make the sensor sensitive only to EMI signals, rather than the measurand. This configuration enables the sensor to switch between normal mode and self-check mode through switching devices. Then the sensor response to EMI signals in normal mode is characterized by that in self-check mode, which is computable. A detailed implementation framework is derived to ensure that the sensor responses to EMI signals in both modes are highly similar, involving the properties of the dummy sensing element, the input voltage of the operational amplifier (opamp), and the power supply rails. Owing to its low demand for additional components and computing resources, the proposed self-check method is cost-effective and convenient to implement. Its effectiveness and robustness are validated on a temperature sensor, an ambient light sensor, a linear variable differential transformer (LVDT) and a wireless temperature sensor node with EMI experiments using different signals parameters, temperatures and light intensities. The experimental results show that this method can enhance the measurement accuracy of analog sensors to help maintain the functioning of sensor-based systems under EMI signals.

Analog sensors that operate in weak signal systems are susceptible to external complex electromagnetic environment, which is typically reflected in their output offsets. Electromagnetic susceptibility (EMS) modeling of sensors can evaluate their operational status and predict their output offsets with monitored electromagnetic interference (EMI) signal parameters. Forward EMS modeling for a specific sensor entails multiple factors and exhibits limited generalizability. To mitigate this problem, a data-driven EMS modeling method for analog sensors based on limited EMS test data is proposed in this article, which extracts correlation characteristics between the sensor output offset and EMI signal parameters through Gaussian process regression (GPR). Two EMI signal parameters, namely, interfering frequency and interfering level, are utilized as training inputs, and the corresponding sensor output offset obtained from EMS tests are employed as training targets. The proposed method assumes that the acquired test data inherently contain noise, aligning with the inherent uncertainty of EMS tests. This serves to enhance its generalization capability. Two types of EMS tests, conduction susceptibility and radiation susceptibility tests, are carried out on two types of sensors to demonstrate the effectiveness and superiority of this method. Experimental results indicate that this method has the superior prediction accuracy than three other commonly used regression methods. Additionally, it has the capability to estimate the confidence of the predicted results and reduce the required testing workload through the Grubbs’ test. On this basis, the operational status of sensors can be evaluated and the sensing output can be modified appropriately.

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.

Jamming attacks can severely disrupt the communications in wireless sensor networks (WSNs). Accurate jammer localization is crucial for the implementation of active anti-jamming strategies. Existing localization methods primarily target the proactive jammer equipped with an omnidirectional antenna within the WSN area, which limits their effectiveness in more complex scenarios. To bridge this capability gap, an accurate localization method based on signal strength map (ALSSM) is proposed to locate the jammer, regardless of whether it is proactive or reactive, and irrespective of its antenna type or location. The ALSSM method constructs a jamming signal strength (JSS) map using JSSs collected by sensor nodes via a deception strategy. This map is then utilized to formulate an evaluation metric to quantify localization estimation errors. Therefore, the localization task is transformed into an optimization problem. This method is convenient to implement, as it requires no additional dedicated hardware, and the collected JSS data can be integrated into the WSN’s normal data transmission process. Simulation and experimental results demonstrate that the ALSSM method can accurately locate the jammer, regardless of whether it is directional or omnidirectional, and irrespective of its deployment inside or outside the WSN area.