Search Results (317)

Spaceborne interrupted frequency-modulated continuous-wave synthetic aperture radar (iFMCW SAR) employs a single antenna on a single spacecraft operating in a time-division transmit/receive mode, effectively avoiding mutual interference between transmitted and received signals and thereby overturning the design paradigm of spaceborne FMCW SAR systems. However, the periodic switching of the antenna between transmit and receive states results in periodic data gaps along the azimuth direction in the echo signal, leading to spurious artifacts in the reconstructed images and severely degrading image quality. Sparse signal recovery techniques based on compressive sensing models have been shown to effectively suppress such spurious targets. Nevertheless, the generalized orthogonal matching pursuit (GOMP) algorithm requires prior knowledge of the signal sparsity, a condition that is often impractical in real-world scenarios. To address this limitation, this paper investigates the variation pattern of the residual norm with respect to sparsity in the GOMP algorithm and proposes a modified GOMP algorithm based on binary search. This approach enables rapid and accurate determination of the true sparsity level without prior knowledge, thereby achieving sparsity-adaptive reconstruction with GOMP and significantly enhancing the imaging quality of iFMCW SAR. Simulation experiments involving both point and scene targets are provided to demonstrate the effectiveness and potential of the proposed algorithms for practical applications. Full article

Sit-to-stand (STS) transitions are clinically informative indicators of functional independence and are sensitive to compensatory strategies associated with physical decline and pain. This study presents a non-contact, non-visual framework for quantitative STS assessment using a 60 GHz frequency-modulated continuous-wave (FMCW) radar in a residential setting. We developed a signal-processing pipeline that converts intermediate-frequency radar data into range–time intensity (RTI) maps, tracks dominant torso motion, and extracts temporal, kinematic, and spectral features. Experiments were conducted across two sensing orientations (subject-facing and side-facing), five mounting heights (45–153 cm), and three execution speeds, with approximately 30 repeated cycles per condition. For normal non-compensated STS transitions, radar-derived metrics reflected expected biomechanical scaling: mean full-cycle duration decreased from $23.90$ s (slow) to $13.95$ s (medium) and $7.98$ s (fast), while peak ascent velocity increased from $0.311$ m/s to $0.358$ m/s and dominant cadence increased from 0.0416 Hz to 0.125 Hz. Simulated abnormal transitions produced distinct and quantifiable deviations. Preparatory rocking introduced an additional oscillatory phase (mean rocking duration 2.36 s), prolonging the standing transition to 4.80 s and altering trajectory regularity. Across configurations, subject-facing mid-torso mounting provided the most continuous and separable STS signatures, whereas side-facing placement and extreme heights reduced effective radial motion or introduced clutter artifacts. These findings establish practical deployment guidelines and demonstrate that radar-derived STS metrics can serve as candidate digital biomarkers for unobtrusive, privacy-preserving detection of mobility decline, compensatory pain behaviors, and functional impairment in real-world home environments. Full article

Small drone-borne W-band synthetic aperture radar (SAR) systems are highly susceptible to motion errors that conventional navigation sensors and phase-based autofocus algorithms cannot effectively resolve due to phase wrapping. This paper presents a sensor-independent imaging framework to robustly suppress these errors. First, joint time-frequency analysis is employed to identify and discard motion-corrupted pulses. Subsequently, a compressed sensing-based backprojection algorithm reconstructs high-resolution images from the remaining sparse dataset. To alleviate the substantial memory burden of matrix-based compressed sensing, the reconstruction is reformulated iteratively. Experimental results confirm that the proposed method maintains structural integrity even when up to 60% of the received pulses are corrupted and demonstrates robust focusing down to an SNR of −25 dB. This approach provides a practical, memory-efficient, and cost-effective solution for SAR platforms. Full article

In this paper, an improved state-space method (SSM) is proposed for gait feature extraction. By introducing zero-phase component analysis Whitening (ZCA Whitening) and an algorithm to search estimated echo as the preprocessing method, pedestrian echoes are divided into three groups according to the frequency probability density: torso, feet, and other segments. Two channels of echoes are selected as inputs to the SSM, which is employed to identify the corresponding micro-Doppler trajectory. On this basis, five gait features of torso amplitude, stride length, walking cycle, torso maximum speed, and feet maximum speed are extracted. Simulation based on the Boulic model, compared with the traditional SSM, demonstrated that there is no need to estimate the model order and that a more accurate torso micro-Doppler trajectory and effective micro-motion features of the feet can be obtained by the proposed method. Finally, 77 GHz FMCW radar was used to collect the echoes of four pedestrians. The classifier was designed based on a support vector machine (SVM), and the classification experiment verified the effectiveness of the extracted gait features. Full article

With ongoing advancements toward 6G networks, the terahertz (THz) band is expected to serve as an essential platform for realizing integrated sensing and communication (ISAC). In particular, maintaining high-data-rate communication while ensuring highly responsive, real-time radar operation in dynamic environments is a critical requirement. This study presents a THz-band ISAC architecture that utilizes a high-speed wavelength-tunable laser for photomixing, enabling simultaneous generation of a fast frequency-swept frequency-modulated continuous-wave (FMCW) radar signal and amplitude-shift keying (ASK) communication. The wavelength-tunable laser enables sub-microsecond frequency sweeps and supports high repetition rates suitable for real-time operation. To address the limitations in waveform design efficiency in conventional time-division ISAC, we experimentally investigate two transmission strategies for simultaneous operation. The first is a frequency-division scheme that reduces mutual interference between radar and communication signals, and the second is a joint-waveform scheme in which both functions share the same THz carrier. Using a single THz transmitter, the proposed system achieves sub-centimeter ranging accuracy together with 15-Gbit/s data transmission. These findings demonstrate that the presented ISAC approach enables efficient integration of radar and communication functions while lowering overall system complexity and implementation cost, offering substantial potential for deployment in future 6G infrastructures. Full article

Lightweight multi-rotor unmanned aerial vehicles (UAVs) have shown great potential in flexible Earth observation, but they impose strict restrictions on payload, volume, and power consumption. Traditional pulse-Doppler synthetic aperture radar (SAR) systems offer high imaging performance but suffer from high peak power and large volume, making them unsuitable for lightweight UAV platforms. To meet the low-power demand, most existing lightweight UAV SAR systems adopt frequency-modulated continuous-wave (FMCW) schemes, which are compact and low cost yet limited by a low range resolution, poor anti-interference ability, and single imaging modes. Therefore, it is urgent to develop an SAR system that combines the high performance of pulse radar with the lightweight advantage of FMCW radar. To this end, this paper proposes a compact, low-power Ka-band pulse-Doppler SAR system for multi-rotor UAVs. With 1.2 GHz bandwidth and highly integrated RF and antenna design, the system achieves miniaturization and low power consumption while maintaining high-resolution imaging capability. Furthermore, two-step waveform error correction and a signal predistortion method are presented to compensate amplitude and phase errors and improve the purity of the transmitted signal. Experimental results show that the proposed system can obtain clear SAR images with a resolution better than 0.3 m, providing a practical high-performance pulse-SAR solution for lightweight UAV platforms. Full article

Accurate absolute pressure measurement is of great importance in industrial control, environmental monitoring, and aerospace. Traditional fiber-optic Fabry–Perot (F-P) pressure sensors usually involve complex microfabrication and high-cost demodulation systems, while conventional diaphragm capsule sensors are limited in sensitivity and resolution. This work presents a low-cost, high-resolution fiber-optic F-P absolute pressure sensor. The sensor uses a vacuum capsule as one reflective surface and a partially reflective fiber collimator as the other, forming a low-finesse F-P interferometer. The cavity length is linearly modulated by the elastic deformation of the capsule under pressure, and high-precision demodulation is realized using frequency modulated continuous wave (FMCW) interferometry instead of conventional spectral methods. Static experiments from 10 to 110 kPa show that the sensor exhibits a high sensitivity of 15,105 nm/kPa and a resolution of 3.3 Pa. Furthermore, the sensor operates normally within the range of −20 °C to 70 °C, exhibiting a pressure–temperature cross-sensitivity of 0.081 kPa/°C and a cavity length drift of 496 nm/h. With the advantages of high performance, simple structure, low cost, and good scalability by selecting different capsules, the proposed sensor has promising potential for practical applications in pressure measurement fields. Full article

In frequency-modulated continuous-wave (FMCW) radar systems, the detection of Doppler-extended targets (DETs) is a critical challenge. The micro-Doppler effects induced by the motion of extended targets such as pedestrians cause the echo energy to spread along the Doppler dimension. As a result, a single range-Doppler cell is unlikely to form a pronounced amplitude peak above the background noise level. Consequently, existing constant false alarm rate (CFAR) methods that rely on single-cell amplitude decisions tend to suffer from performance degradation in DET scenarios and exhibit limited adaptability under varying clutter conditions. To solve these issues, we propose a nonlinear transform–based variability index CFAR detector for DET (DET-NTVI-CFAR), with the aim of improving detection probability and maintaining stable false alarm control in complex clutter backgrounds. This work constructs a detection statistic by applying a nonlinear transform to the accumulated power cells and derives the threshold from the corresponding probability distribution model. A variability index CFAR (VI-CFAR) decision strategy is introduced to select the appropriate detection branch under different operating conditions. In the threshold design stage, the false alarm probability expressions of three sub-detection methods are derived to guide the selection of threshold parameters. Simulation results demonstrate that the proposed method achieves stable false alarm control and improves detection probability in various environments. Field test results also confirm the applicability of the DET-NTVI-CFAR detector. Full article

Gait is a sensitive marker of mobility decline and fall risk, motivating unobtrusive sensing methods that can extract spatiotemporal parameters outside specialized gait laboratories. This paper presents a physics-based comparison of two millimeter-wave frequency-modulated continuous-wave (FMCW) radar deployment paradigms using a low-cost, system-on-chip (SoC) 60 GHz Infineon BGT60TR13C radar sensor: (i) a fixed (tripod-mounted) corridor observer and (ii) a shoe-mounted body-centric configuration attached to the medial side of the left shoe. Four healthy adult author-participants performed repeated 30 s corridor trials under five gait styles (regular, slow, fast, simulated festination, and simulated freezing-of-gait), including brief pauses during turns; an empty-corridor recording was acquired to characterize static clutter. Step events were detected using peak-picking on foot-related velocity envelopes with adaptive thresholds, and step count, cadence, step time, and step-time variability were derived. Performance of the fixed and shoe-mounted configurations was quantitatively compared to video ground truth using mean absolute percentage error (MAPE) for step count estimation. Across all gait styles, the shoe-mounted FMCW radar consistently reduced step-count error relative to the fixed corridor-mounted configuration, with the largest gains under irregular patterns (e.g., festination: 37.1% fixed vs. 9.6% shoe-mounted). These findings highlight the advantages of body-centric millimeter-wave radar sensing and support low-cost SoC radar as a pathway toward wearable, privacy-preserving gait monitoring in real-world environments. Full article

Frequency-modulated continuous-wave (FMCW) radars are widely used for range and velocity estimation. However, conventional velocity measurement techniques based on 2D-FFT processing require a large number of chirps and suffer from a maximum unambiguous velocity limitation, which restricts their applicability to high-speed targets. This study addresses these challenges by proposing a hybrid FMCW chirp waveform that employs bandwidth variation between consecutive chirps while maintaining a constant chirp duration. The proposed method enables separation of range- and Doppler-dependent frequency components using only two chirps; thus, it improves the maximum velocity constraint by keeping intermediate-frequency bandwidth and sampling requirements low. In addition, spatial angle estimation is performed using an amplitude-comparison monopulse antenna configuration, allowing single-snapshot angle measurement with low computational complexity. To enhance measurement robustness, extended and unscented Kalman filters are integrated for target tracking. Simulation results demonstrate that the proposed waveform achieves accurate velocity estimation for very high-speed targets and that the unscented Kalman filter consistently outperforms the extended Kalman filter in terms of convergence speed and robustness, particularly under poor initialization and strong nonlinearities. The results confirm that the proposed framework provides an efficient solution for tracking a single, fast-moving, isolated target in a homogeneous environment using FMCW radar systems at short and medium ranges. Full article

Frequency-Modulated Continuous-Wave (FMCW) millimeter-wave (mmWave) radars, originally developed for automotive applications, can be also explored for environmental sensing due to their compact size, low cost, and robustness under adverse environmental conditions. However, measurements obtained from commercial automotive radars are often affected by environmental noise and intrinsic self-interference caused by coupling between transmitting and receiving patch antennas, which can degrade the reliability of relative power-based range profiles. In this paper, the performance of the AWR1843BOOST FMCW mmWave radar from Texas Instruments is investigated, with particular emphasis on noise due to antenna coupling. A sub-optimal post-processing technique based on Noise Environmental Measurement (NEM) removal is proposed to remove both deterministic noise, associated with antenna coupling, and stochastic noise, related to environmental contributions. The proposed approach is validated through controlled laboratory experiments involving different targets characterized by distinct dielectric properties, including a metallic object, an absorbing object, and a target with varying degrees of wetness. The experimental results demonstrate that the NEM removal technique significantly enhances the clarity of the backscattered target’s relative power, preserving differences between target values, and improves the radar’s sensitivity to material properties and water content. Measurements accomplished at the electromagnetic and remote sensing laboratory of the Università degli Studi di Napoli Parthenope confirmed the soundness of the proposed NEM removal technique and the sensitivity of the AWR radar to the dielectric properties of targets. Full article

Sleep apnea–hypopnea syndrome (SAHS) is a common sleep-related breathing disorder associated with substantial cardiovascular and neurocognitive risks. Although polysomnography (PSG) remains the clinical gold standard for diagnosis, its cost, operational burden, and limited accessibility hinder scalable and longitudinal home monitoring. Frequency-modulated continuous-wave (FMCW) radar provides unobtrusive, non-contact respiration sensing, yet radar-based event detection is often constrained by scarce annotations and pronounced domain shifts relative to PSG signals. In this work, we propose a deep learning framework for apnea–hypopnea event detection from FMCW radar that combines a 1D U-Net segmentation backbone with multi-head self-attention (MHSA) and cross-modality transfer learning. The model was first pre-trained on a large public PSG dataset to learn transferable respiratory-event representations and then fine-tuned on a smaller clinically annotated radar respiration dataset using synchronized PSG labels. It produced per-sample event probabilities, which were further refined via temporal post-processing to generate event-level detections and apnea–hypopnea index (AHI) estimates. Experimental results demonstrate strong performance in the radar domain, achieving precision of $0.8137\pm 0.0332$, recall of $0.8369\pm 0.0470$, and an F1-score of $0.8167\pm 0.0052$. Overall, these results indicate that PSG-to-radar transfer learning enables accurate, low-cost, and non-contact sleep apnea screening, supporting scalable longitudinal monitoring in home-like settings. Full article

Ceramic Matrix Composites (CMC) are widely used in critical applications such as leading edges of aircraft wings and thermal insulation layers of thermal protection systems due to their advantages of being lightweight, high-temperature resistant, and impact-resistant. However, influenced by manufacturing processes and service environments, internal defects such as pores and delamination are prone to occur, significantly compromising the mechanical properties and service reliability of the material. This paper primarily evaluates the feasibility and applicability of using Terahertz Frequency Modulated Continuous Wave (FMCW) technology for the non-contact detection of CMC. First, the measurement principle of FMCW is introduced, and the structure of the detection system, including a two-dimensional mechanical scanning platform, optical lenses, a control platform, and a data acquisition unit, is outlined. Subsequently, scanning imaging was performed on CMC specimens and their bonded thermal protection structure (TPS) specimens, demonstrating the feasibility of Terahertz FMCW technology as an advanced non-destructive testing tool for CMC inspection. The issues of diffraction and the Rayleigh limit inherent in real-aperture terahertz imaging were analyzed and discussed. A multi-scale fusion defect detection method incorporating background estimation is proposed to enable precise delineation of defect regions. Experimental results show that, after processing with the proposed algorithm, the minimum detectable pore diameter at the focal plane is 1 mm, with a regional error of approximately 3%. The detection error for pores and debonding areas in CMC is maintained within 6.44%. Analysis indicates that combining terahertz imaging technology with image processing algorithms enables the quantitative analysis of internal defects in composite materials, offering a new technical approach for defect detection in composite materials. Full article

Frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) based on coherent ranging is a technology capable of high-resolution distance measurement while remaining robust against ambient light interference. However, extending the measurable range remains challenging due to (i) the coherence length limitation of the laser and (ii) distance ambiguity caused by frequency ambiguity in coherent detection. To overcome these limitations, we propose an unambiguous range-extended FMCW LiDAR enabled by arrayed waveguide grating (AWG)-based spectral multiplexing. By spectrally demultiplexing the reference arm into four wavelength channels with sequentially designed optical path delays, multiple independent interference signals are obtained simultaneously without increasing the number of photodetectors or optical couplers. A channel-pair-based distance decoding algorithm is further introduced to resolve distance ambiguity by classifying detection outcomes across adjacent channels and selectively applying predefined operations. The proposed FMCW LiDAR system effectively extends the measurable range to approximately five times that of a conventional FMCW LiDAR. Experimental results demonstrate high measurement accuracy and successful reconstruction of three-dimensional distance maps, validating the system’s potential for extended-range FMCW LiDAR applications. Full article

To address the vulnerability of traditional linear frequency-modulated continuous wave (FMCW) fuze to jamming due to fixed modulation parameters, this paper proposes a novel fuze waveform design scheme using chaotic code-based frequency and phase composite modulation along with a Normalized Rate-Invariant Ranging algorithm (NRIR). Leveraging the ergodicity and initial value sensitivity of the Logistic chaotic map, a dual-dimensional composite modulation system is constructed. In the frequency domain, the frequency modulation slope undergoes periodic binary variation according to chaotic states to break the signal periodicity. In the phase domain, phase encoding is implemented based on chaotic binary sequences to further improve waveform entropy and complexity, effectively destabilizing the parameter stability required for coherent jamming. To resolve the distance–Doppler coupling challenges and spectral dispersion issues caused by variable-slope modulation, the NRIR algorithm is developed. By introducing a resampling transformation operator, the non-stationary rate-varying beat frequency signal is mapped to a normalized “constant-slope” space, enabling coherent accumulation and ranging of targets. Using the ambiguity function as an analytical tool, theoretical analyses, simulation experiments, and test results demonstrate that this design scheme exhibits excellent performance in suppressing DRFM jamming and sweep-frequency jamming, providing theoretical support and technical approaches for fuze anti-jamming design. Full article