The motion sensing capability of millimeter-wave radar is significantly contingent upon its operating frequency. However, conventional techniques do not allow frequency modification once the radar hardware is completed. In this article, a novel frequency-reconfigurable technique is proposed to enable the operating frequency reconfiguration without any hardware modifications, enhancing the radar’s capability to perceive displacement motions across various scales. This technique splits frequency-modulated continuous-wave (FMCW) beat signals into multiple sub-FMCW signals, yielding pairs of quadrature signals that are similar to those in continuous-wave radars. These quadrature signals are subsequently synthesized to quadrature signals at a new operating frequency. For weak motions of deep subwavelength scale, the proposed technique improves the detection sensitivity by reconstructing equivalent quadrature signals at a higher operating frequency, such as from 60 up to 480 GHz. For large-scale motions, this technique surpasses the radar’s velocity limit by reconstructing that at a lower frequency, e.g., 60–1 GHz. Simulation results and theoretical analysis demonstrate the superiority of this technique for motion sensing. Experiments were also carried out to validate this technique in detecting the weak deep subwavelength, large-scale mechanical vibrations, and action gestures. Experimental results indicate that this technique exhibits approximately a three times accuracy improvement in detecting weak motions and about ten times accuracy improvement in detecting large-scale motions compared to the conventional technique.

Rapid and accurate rendezvous and proximity operations for spacecraft are crucial to the success of most space missions. In this paper, a sequential convex programming method, combined with the first-order and second-order Birkhoff pseudospectral methods, is proposed for the autonomous rendezvous and proximity operations of spacecraft. The original nonlinear and nonconvex close-range rendezvous problem with thrust constraints and no-fly zone constraints is converted into its convex version by using the sequential convexification techniques; then, the Birkhoff pseudospectral method is used to transcribe the dynamic constraints into a series of linear algebraic equality constraints, in other words, a convex second-order conic programming problem with a relatively small condition number. Thus, the resulting problem can be accurately and efficiently solved by a convex solver. The simulation results indicate that the proposed methods, especially the second-order Birkhoff pseudospectral method, have obvious advantages over other methods in computational efficiency and sensitivity.

This paper innovatively presents a compact co-linearly polarized full-duplex patch antenna that exhibits high isolation and broadside symmetrical radiation pattern at the ISM band for integrated sensing and communication (ISAC) in Internet of Things (IoT). The design features an extremely closely-spaced Tx-Rx antenna pair with an edge-to-edge distance of 0.5 mm (0.009 μ 0), aiming at mitigating radiation pattern misalignment. Additionally, a novel decoupling structure is proposed, incorporating a center-connecting-strip with a grounding-through-via positioned at the antenna’s midpoint, effectively canceling both port coupling and radiation pattern interference. A thorough analysis is conducted from current distribution, equivalent circuit modeling, and characteristic mode theory to provide deeper physical insights and elucidate the decoupling mechanism. Both simulations and measurements of the prototype are performed to validate the design approach. Experimental results prove that the proposed full-duplex antenna demonstrates possesses the merits of high Tx-Rx isolation (a maximum value of 61.6 dB), compact size (0.07 μ03), straightforward decoupling and feeding scheme, broadside and symmetrical radiation patterns (a maximum 3 dB beamwidth of 103∘). Moreover, extensive full-duplex system validations for both wireless communication and radar sensing are conducted by integrating the proposed antenna into a custom-designed radar system. These validations confirm the merits of the antenna’s high isolation and wide beamwidth in practical system applications, thereby not only validating the effectiveness of the decoupling strategy but also showcasing the potential of the antenna for ISAC applications in IoT era.

Multiple-input-multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar systems often encounter performance degradation in short-range applications. In particular, short-range leakages and stationary clutters can introduce interferences that obscure the detection of targets of interest, while traditional systems are limited by inadequate spatial resolution, which constrains their ability to acquire accurate point cloud data. To address these challenges, a K-band short-range end-to-end 2T4R sparse MIMO FMCW system was proposed and designed, achieving a threefold enhancement in point cloud acquisition capability. Based on this system, a 3-dB beamwidth of 8.97° and a measured angular resolution of 13.8° are achieved, corresponding to improvements of 29.8% and 32.8%, respectively, compared to the conventional uniform array, while a sidelobe level of −10 dB and a wide field of view of 100° are also demonstrated. Furthermore, a novel frequency-domain interferences (leakages and stationary clutters) cancellation technique (FDICT) has been introduced to eliminate interferences, significantly enhancing the system’s short-range detection performance. By exploiting the frequency-domain characteristics of the intermediate frequency (IF) signals, this technique effectively mitigates interferences, offering robust and versatile cancellation of multipath leakages and stationary clutters for both synchronous and asynchronous systems, thus enabling accurate point cloud acquisition. Simulations and experiments were conducted to validate the system. The results demonstrate that the radar achieved the expected performance, and the proposed technique effectively eliminates leakages and stationary clutters, leading to an average improvement of 15 dB in signal-to-interference ratio (SIR) across the entire spectral range. Compared to traditional systems, the proposed system demonstrates superior short-range sensing performance, making it particularly advantageous for short-range applications.

Accurate contactless microwave cardiogram (MCG) sensing, which is essential for providing health information, remains a significant challenge in multitarget scenarios. In particular, existing multiple-input–multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radars struggle to effectively suppress respiratory interference, which impacts the detection of subtle heart motions. Moreover, traditional systems are constrained by spatial resolution, thereby limiting the ability to resolve multitarget, and further degrading MCG extraction performance. To address these challenges, a novel 4-D digital beamforming (DBF) technique is proposed, in which 3-D chest positions of multiple targets, including range, azimuth, and elevation, are localized by the radar with precise beam steering, after which the corresponding micro-Doppler motions are extracted. A newly introduced motion-frequency-domain beamforming (MFDDBF) technique is then employed to achieve 1-D MCG sensing and respiratory interference suppression, enabling linear, efficient, and distortion-free time-domain waveforms extraction of both the fundamental and harmonic components of the respiratory. By removing the respiratory from the chest micromotions, clean MCG signals are obtained. Furthermore, a $K$ -band short-range end-to-end 4T8R sparse MIMO FMCW radar system is designed and implemented, achieving a spatial resolution of 6° in azimuth and 8° in elevation, while providing a 90° field of view (FOV) in both dimensions and maintaining a −6-dB sidelobe level (SLL), which enables effective multitarget separation and significantly enhances the accuracy of MCG sensing. Experiments were conducted to validate the system. The results demonstrate that the system achieves an accurate multitarget MCG sensing and a root-mean-squared error (RMSE) of 10 ms for RR interval, highlighting its potential for radar-based vital sign monitoring applications.

Target detection in sea clutter is a significant problem in marine surveillance. The difficulty of radar target detection lies in the low signal-to-clutter ratio (SCR) and insufficient feature extraction brought by handcrafted features. In recent years, with the great success of artificial intelligence in the field of computer vision, there is a trend that researchers start to apply convolutional neural networks to radar target detection. However, most methods based on convolutional neural networks need to convert one-dimensional signals into two-dimensional images for preprocessing, which not only increases the computational burden, but also reduces the accuracy of detection. Therefore, in this paper, we propose a Transformer-based sea clutter target detection method, which takes the collected one-dimensional signal as input directly after FFT preprocessing. And comparing with two-dimensional images, which can extremely reduces the computational cost. Most importantly, Transformer-based network has self-attention mechanism to strengthen target and suppress noise, thus improving the accuracy of target detection. The proposed algorithm is verified on the IPIX datasets, and the results show that the proposed algorithm attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train and inference.