Microelectromechanical systems resonant accelerometers (MRAs), with their high scale factor and semi-digital output, are well-suited for precision inertial sensing but face challenges in simultaneously optimizing bandwidth and noise performance. To understand these limitations, a detailed noise model of the closed-loop digital phase-locked loop (DPLL)-based MRAs is developed to characterize the dominant noise sources and their impact on phase-domain behavior. This paper presents a Kalman filter (KF)-based fusion method to mitigate the trade-offs identified in the noise model, combining a wide-bandwidth configuration and a low-noise configuration by first applying a high-pass (HP) filter to the wide-bandwidth output. Experimental validation under both static and 70 Hz dynamic excitation conditions demonstrates up to a two-order-of-magnitude reduction in acceleration noise spectral density, reaching 85.14 ng/root, along with a bias instability of Hz 20.31 ng with an integral time of 35 s. Furthermore, the KF fusion algorithm enables the detection of ultra-low-frequency environmental vibrations at 0.01 Hz, improving the Signal-to-Noise Ratio (SNR) to approximately 16.02 dB compared to the original MRA's performance. This proposed approach offers an effective solution for enhancing noise performance and long-term stability without compromising frequency bandwidth, enabling the use of MRAs in broadband, low-noise inertial measurement systems.

Integrated sensing and communications (ISAC) has been envisioned as a promising solution to support emerging services in low-altitude wireless networks (LAWNs), where upgrading 5G ground base stations (GBS) toward new active sensing systems with wide coverage, low cost, high accuracy, and favorable spectrum compatibility, is strongly desired. However, such an evolution faces several critical challenges, particularly in the detection and tracking of weak and slow unmanned aerial vehicles (UAVs). These challenges include ISAC waveform design, clutter cancellation resilient to high clutter-to-noise ratios (CNRs), and efficient Doppler separation between UAVs and clutter. To that end, we summarize potential solutions and raise a comprehensive framework on implementing the 5Gadvanced (5G-A) GBS. Outfield experiments demonstrate that the developed 5G-A GBS can effectively track weak and slow targets at distances exceeding 1 kilometer, while incurring only a 1.2

With the increasing demand for three-dimensional positioning in Synthetic Aperture Radar (SAR) systems, multi-view SAR technology is rapidly evolving. Airborne-distributed SAR systems, benefiting from multi-platform collaborative observation, flexible baseline configuration, and synchronous imaging, have become an ideal solution for realizing this technology. However, the flight paths of these platforms are not optimal, and the airborne navigation equipment also suffers from measurement errors, which severely deteriorates the multi-view SAR target positioning accuracy of the airborne-distributed platforms. Currently, research on this issue remains scarce. This paper is based on the multi-view normalized Range Doppler positioning model, introducing platform position errors to derive the Cram & eacute;r-Rao Lower Bound (CRLB). A detailed positioning accuracy analysis is conducted for different flight paths and various sources of errors, demonstrating that platform position errors are a primary factor affecting target positioning accuracy. To address this, a target positioning method based on inter-platform ranging information is proposed, which imposes constraints on the position of the airborne-distributed platform using inter-platform ranging data, thereby reducing the dependence of target positioning accuracy on platform position errors and enhancing the robustness of three-dimensional positioning for multi-view SAR targets. The effectiveness of the proposed method is verified using measured data, which reduces the 3D positioning error of the target by nearly 60%.

Small unmanned aerial vehicle (UAV)-borne distributed tomographic synthetic aperture radar (TomoSAR) systems offer flexible baseline configurations and low deployment cost, making them attractive for rapid and high-resolution three-dimensional (3D) reconstruction. However, the distance between adjacent channels placed on different UAVs is relatively large due to the flight safety spacing considerations. This leads to high sidelobes in the elevation point spread function (PSF) within the reconstruction range. Meanwhile, atmospheric turbulence may cause UAVs to deviate from their predefined trajectories, making it difficult to suppress sidelobes through baseline optimization. Large baselines may also introduce spatial decorrelation between channels, which gives rise to random phase noise in the interferometric phase and further aggravates elevation ambiguity by increasing the sidelobe level of the PSF. To address this problem, this paper proposes an elevation ambiguity resolution method based on neighborhood-adaptive elevation priors. In the proposed method, a window function is constructed from reconstruction results of neighboring pixels and incorporated into the reconstruction process to suppress the interference caused by high sidelobes. In this way, the probability of correct target reconstruction is improved. The effectiveness and robustness of the proposed method are validated using both simulations and real measured data. Experimental results obtained with a C-band small UAV-borne distributed TomoSAR system show that the proposed method effectively suppresses ambiguity and enables ambiguity-free reconstruction of target buildings. Statistical analysis further demonstrates that the number of ambiguous points produced by the proposed algorithm is only one-fifth of that produced by the conventional OMP method.

Small Unmanned Aerial Vehicle (UAV)-borne distributed Tomographic Synthetic Aperture Radar (TomoSAR) systems exhibit remarkable residual time-varying baseline errors due to the limited precision of the position and orientation system on small UAV platforms. These errors critically degrade the performance of three-Dimensional (3D) target reconstruction. Compared with airborne repeat-pass 3D Synthetic Aperture Radar (SAR), distributed TomoSAR mounted on small UAVs imposes stricter compensation accuracy requirements for time-varying baseline errors because of the altitude constraints of the carrying platform. Under the conditions of low signal-to-noise ratio and substantial time-varying baseline errors, existing estimation methods often fail to provide stable and reliable results. In this paper, a two-step time-varying baseline error estimation method based on image azimuth displacement is proposed. The method sequentially estimates the low-frequency component through the co-registration of the master and slave images and the high-frequency component using a multisquint algorithm. Iterative refinement is applied to enhance estimation accuracy. The experimental results obtained from real C-band small UAV-borne distributed TomoSAR data demonstrate that, compared with the enhanced multisquint processing method, the proposed method considerably reduces the root mean square of differential interferometric phases across most channels, thereby effectively improving interchannel coherence. In addition, the elevation-direction standard deviation of the reconstructed point cloud is reduced from 5.16 to 1.33 m, and the height reconstruction error of building targets is less than 0.5 m, validating the effectiveness and superiority of the proposed method.

Synthetic aperture radar (SAR) object detection is an important part of remote sensing interpretation. However, because of variations in frequency band, resolution, background clutter, and target scattering responses, the performance of existing detectors often degrades when training and testing data are acquired from different SAR domains. Although domain adaptation methods offer a promising paradigm for solving this problem, most of them mainly pursue domain-invariant feature alignment and suppress sensor-dependent scattering characteristics that are useful for object detection. This problem becomes more challenging in few-shot scenarios, where only a few fully annotated target-domain SAR images are available. To address this issue, we propose a scattering-aware shared-specific feature decomposition framework for few-shot SAR domain adaptation object detection. We decompose detection features into a shared path and several soft-gated scattering-specific expert paths. The shared path learns transferable object structural information and is used for asymmetric domain alignment, while the scattering-specific experts adaptively compensate heterogeneous SAR responses. In addition, routing-domain auxiliary loss is introduced to encourage specific experts to capture sensor-dependent routing preferences, and an expert balancing loss is used to prevent routing collapse. Extensive experiments on four bidirectional heterogeneous SAR detection tasks between FARAD-X/FARAD-Ka and MiniSAR under different few-shot settings have been conducted and experimental results demonstrate that the proposed method achieves superior performance in both forward and reverse adaptation directions.

Lightweight Unmanned Aerial Vehicles (UAVs) have limited space, low payload capacity, and constrained power supply capabilities. Therefore, their payloads are constrained by size, weight, and power (SWaP). Thus, designing edge-side signal processing architectures for the payloads of UAVs faces severe challenges. Traditional ASIC design based on manual optimization struggles to meet the demands of low latency and low resource occupancy in edge-side applications. To address this challenge, this paper proposes a signal processing hardware accelerator architecture design framework with algorithm-hardware co-design. The framework employs a cross-level dataflow graph representation to formally capture task characteristics. Reconfigurable dataflow templates and reusable operator IP components are systematically constructed based on this representation. Through multi-objective design space exploration, the framework achieves Pareto-optimal mapping from algorithmic specifications to hardware implementations. Finally, automatic generation of top-level hardware descriptions enables rapid FPGA-based prototyping and functional validation. Taking synthetic aperture radar (SAR) imaging as a study example, compared with non-reconfigurable architectures, this scheme reduces the equivalent gate count by 51.4% without increasing processing latency. Compared with a conventional reconfigurable dataflow architecture, the design improves energy efficiency from 12.8 MS/J to 16.0 MS/J, representing a 25.4% enhancement, while also scaling the supported data processing size by a factor of 4×. It provides a high-performance and scalable hardware acceleration solution for lightweight edge-side computing platforms.

Aiming at the problem that it is difficult to extract the micro-Doppler feature of UAV under low signal-to-clutter ratio (SCR), which leads to recognition failure, a recognition algorithm based on enhanced synchronous extraction transform (SET) and harmonic aware sparse Bayesian learning (HA-SBL) is proposed in this paper. Firstly, adaptive dual denoising algorithm is introduced to enhance SET to achieve high concentration of time-frequency energy and background denoising. Secondly, after extracting the cadence frequency spectrum (CFS), the feature extraction is transformed into a sparse representation problem, and a parametric multi-order harmonic prior dictionary is constructed. SBL is used to solve the problem due to its excellent performance in sparse representation problem solving. Addressing the issue of misjudgment in SBL, this paper injects local physical energy support into Bayesian iteration, and proposes HA-SBL algorithm to accurately suppress false activation without physical support. Finally, the threshold is constructed based on the optimal sparse confidence weight reconstruction to complete the target recognition. Experiments show that the algorithm can still maintain high recognition accuracy in low SCR environment, showing excellent performance and robustness.

This paper presents a novel intra-device fusion method for a differential micromechanical resonant accelerometer (MRA) by leveraging multi-resonance modes to enhance performance. By combining the differential outputs from multiple flexural modes within a single device, the proposed approach effectively mitigates the trade-off between frequency bandwidth and noise. Experimental results show the acceleration noise power spectral density (PSD) improving from $533.63 \text{ng}/\sqrt{} \text{Hz}$ to $95.54 \text{ng}/\sqrt{} \text{Hz}$. The Allan deviation decreasing from 669.18 ng at 1.3 seconds to 25.13 ng at 20 seconds, while maintaining a 100 Hz frequency bandwidth.

Synthetic aperture radar (SAR) systems, as wideband radar systems, are inherently susceptible to interference signals within their operational frequency band, which significantly affects SAR signal processing and image interpretation. Recent studies have demonstrated that semiparametric methods (e.g., the RPCA method) exhibit excellent performance in suppressing these interference signals. However, these methods predominantly focus on processing SAR’s raw echo data, which does not satisfy the sparsity requirements and entails extremely high computational complexity, complicating integration with imaging algorithms. This paper introduces an effective method for suppressing interference signals by leveraging the sparsity of the SAR image domain. It utilizes the sparsity of the interference signal in the two-dimensional frequency domain, following focusing processing, rather than relying on low-rank properties. This approach significantly reduces the computational complexity. Ultimately, the effectiveness and efficiency of the proposed algorithm are validated through experiments conducted with simulated and real SAR data.

With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground-moving target indication (GMTI) radar systems has received great interest. However, because of size, weight, and power (SWaP) limitations, the UAV may not be able to equip a highly accurate inertial navigation system (INS), which leads to reduced accuracy in the moving target relocation. To solve this issue, we propose using an image registration algorithm, which matches a Doppler beam sharpening (DBS) image of detected moving targets to a synthetic aperture radar (SAR) image containing coordinate information. However, when using conventional SAR image registration algorithms such as the SAR scale-invariant feature transform (SIFT) algorithm, additional difficulties arise. To overcome these difficulties, we developed a new image-matching algorithm, which first estimates the errors of the UAV platform to compensate for geometric distortions in the DBS image. In addition, to showcase the relocation improvement achieved with the new algorithm, we compared it with the affine transformation and second-order polynomial algorithms. The findings of simulated and real-world experiments demonstrate that our proposed image transformation method offers better moving target relocation results under low-accuracy INS conditions.

Unmanned Aerial Vehicle (UAV)-based distributed Synthetic Aperture Radar (SAR) is a current research focus. Phase synchronization is crucial for eliminating the non-coherence of distributed systems. However, as the number of UAVs increases, fast time-varying multipath effects caused by rotors can lead to multipath fading. This degrades the signal-to-noise ratio (SNR) of the synchronization link and distorts the synchronization waveform. It further breaks the reciprocity of the dual one-way synchronization link, ultimately degrading phase synchronization accuracy. We propose a robust method for spike detection and error propagation to improve phase synchronization precision. Using the Hampel filter, we detect pulse peak position jitter and remove observations from anomalous links. We then use data fusion based on minimum variance to recover synchronization errors in these links, leveraging the redundancy in synchronization phase matrices. The effectiveness of the proposed method is confirmed through flight test data from a four-UAV distributed TomoSAR experiment. Compared to the maximum-peak detection method, the phase accuracy is improved from 12.84 deg to 0.61 deg. This method supports the application of distributed SAR.

With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground moving target indication (GMTI) radar systems has received great interest. In GMTI, moving target relocation is an essential requirement, because the positions of the moving targets are usually displaced. For a multichannel radar system, the position of moving targets can be accurately obtained by estimating their interferometric phase. However, the high position accuracy requirements of antennas and the computational resource requirements of algorithms limit the applications of relocation algorithms in UAV-borne GMTI radar systems. In addition, the clutter’s interferometric phase can be severely affected by the undesired phase error in the site. To overcome these issues, we propose an improved knowledge-based (KB) algorithm. In the algorithm, moving targets can be relocated by comparing their interferometric phase with the clutter’s phase. As for the undesired phase error, the algorithm first employs a random sample consensus (RANSAC) algorithm to iteratively filter the outliers. Compared with other classic relocation algorithms, the proposed algorithm shows better relocation accuracy and can be applied in real-time applications. The performance of the proposed improved KB algorithm was evaluated using both simulated and real experimental data.

Impulse ultrawideband (UWB) synthetic aperture radar (SAR) combines high-azimuth-range resolution with robust penetration capabilities, making it ideal for applications such as through-wall detection and subsurface imaging. In such systems, the performance of UWB antennas is critical for transmitting high-power, large-bandwidth impulse signals. However, two primary factors degrade radar imaging quality: (1) inherent limitations in antenna radiation efficiency, which lead to low-frequency signal loss and subsequent time-domain ringing artifacts; (2) impedance mismatch at the antenna terminals, causing standing wave reflections that exacerbate the ringing phenomenon. This study systematically analyzes the mechanisms of ringing generation, including its physical origins and mathematical modeling in SAR systems. Building on this analysis, we propose a Bayesian ringing suppression algorithm based on sparse optimization. The method effectively enhances imaging quality while balancing the trade-off between ringing suppression and image fidelity. Validation through numerical simulations and experimental measurements demonstrates significant suppression of time-domain ringing and improved target clarity. The proposed approach holds critical importance for advancing impulse UWB SAR systems, particularly in scenarios requiring high-resolution imaging.

Multiple-Input Multiple-Output (MIMO) radar enjoys the advantages of a high degree of freedom and relatively large virtual aperture, so it has various forms of applications in several aspects such as remote sensing, autonomous driving and radar imaging. Among all multiplexing schemes, Time-Division Multiplexing (TDM)-MIMO radar gains a wide range of interests, as it has a simple and low-cost hardware system which is easy to implement. However, the time-division nature of TDM-MIMO leads to the dilemma between the lower Pulse Repetition Interval (PRI) and more transmitters, as the PRI of a TDM-MIMO system is proportional to the number of transmitters while the number of transmitters significantly affects the resolution of MIMO radar. Moreover, a high PRI is often needed to obtain unambiguous imaging results for MIMO-SAR 3D imaging applications on a fast-moving platform such as a car or an aircraft. Therefore, it is of vital importance to develop an algorithm which can achieve unambiguous TDM-MIMO-SAR 3D imaging even when the PRI is low. Inspired by the motion compensation problem associated with TDM-MIMO radar imaging, this paper proposes a novel imaging algorithm which can utilize the phase shift induced by the time-division nature of TDM-MIMO radar to achieve unambiguous MIMO-SAR 3D imaging. A 2D-Compressed Sensing (CS)-based method is employed and the proposed method, which is called HPC-2D-FISTA, is verified by simulation data. Finally, a real-world experiment is conducted to show the unambiguous imaging ability of the proposed method compared with the ordinary matched-filter-based method. The effect of velocity error is also analyzed with simulation results.

High-precision, robust, and rapid three-dimensional (3D) passive positioning of the radiation source is critical for modern reconnaissance systems. While synthetic aperture technology has advanced 2D passive positioning performance, existing methods fail to achieve full 3D positioning with sufficient accuracy and computational efficiency. This is because of the inherent limitations of the single-station platform in resolving elevation-angle ambiguity. To address this gap, we propose a Cross-Track Interferometric Synthetic Aperture (CISA) 3D passive positioning algorithm. The algorithm innovatively realizes robust elevation-angle measurement by recursively deriving the long baseline unambiguous phase difference step-by-step from a virtual short baseline. The 3D positioning is achieved by combining passive synthetic aperture and interferometric angle measurement. Furthermore, we establish the incoherence model of synthetic aperture passive positioning for the first time and propose a compensation method based on static acquisition data to improve the practicability of CISA. Simulation and experimental results demonstrate that the proposed CISA algorithm achieves a positioning accuracy of 4.73‰R, improves computational efficiency by 1–2 orders of magnitude compared to conventional methods, and exhibits superior robustness to noise. The research can provide a reference for the method research and engineering realization of synthetic aperture 3D passive positioning.

Non-contact, rapid and accurate measurement of respiratory rates (RR) and heart rates (HR) in neonates has significant clinical importance. Existing methods predominantly focus on thoracic respiratory signal measurement. This thoracic-focused approach, when applied to neonates who exhibit predominantly abdominal breathing, leads to a low signal-to-interference ratio (SIR) that compromises the accuracy of RR measurement compared with using abdominal signal. Moreover, neonatal and staff motion in clinical environments pose challenges for the robustness of monitoring systems. In this paper, a method for the separation of thoracic and abdominal measurements based on MIMO radar is proposed to make use of the RR information contained within the abdominal signal, while extracting HR information from thoracic signal, in which case RR can be extracted more precisely from abdominal signal with a high SIR. In order to ensure proper separation of radar beams under the interference from neonatal and staff motion, this paper presents an integrated measurement system that combines a monocular camera and MIMO radar to achieve precise and real-time guidance for the radar beams. Experimental results demonstrate significant improvements in RR measurement accuracy and system robustness. We report maximum root mean square errors of 2.16 Beats Per Minute(BPM) for RR measurements and 3.54 BPM for HR measurements.

In recent years, joint wireless communication and Synthetic Aperture Radar (SAR) imaging has received extensive attention. The realization of this concept remains a significant challenge. For communication and SAR, they have conflicting requirements on waveforms. The state-of-the-art solutions are based on the Orthogonal Frequency Division Multiplexing (OFDM) techniques. However, OFDM waveforms are sensitive to the Doppler frequency. Even worse, their unavoidable Cyclic Prefix (CP) would introduce false peaks into SAR images. To be insensitive to frequency bias and free of CPs, Filter Bank Multicarrier (FBMC) waveforms with stepped frequency chirp and Offset Quadrature Amplitude Modulations (OQAM) are proposed in this paper. Particularly, stepped frequency chirps, which have been optimally weighted for SAR imaging, are interleaved into FBMC subcarriers as pilots. Meanwhile, the channel changes can be obtained from these pilots for communication equalization. Moreover, concerning the intrinsic interference of imaginary parts between pilots and OQAM symbols, compensation algorithms are introduced. With the proposed waveform schemes, the identical frequency band can be utilized for data transmission while performing high-resolution SAR imaging. Theoretical analysis and simulation results demonstrate that the proposed waveform scheme is feasible.

Time-division multiplexing (TDM) MIMO radar has attracted significant attention due to its hardware simplicity. However, its applications are significantly limited by two main challenges: motion-induced phase migration and the low-PRF characteristic. Accurate velocity estimation, which is required to compensate for motion-induced phase migration, cannot be achieved using the conventional range-Doppler process due to the low PRF. To address this issue, we propose a novel method based on the angle-phase-compensated angle-Doppler spectrum (APAD). We demonstrate that the APAD spectrum is essentially the ideal angle-Doppler spectrum blurred by a point spread function (PSF), which is determined by the system parameters of the TDM MIMO radar. By applying L1 regularization and the alternating direction method of multipliers (ADMMs), the deblurring process can restore the angle-Doppler spectrum, enabling unambiguous velocity estimation and undistorted direction-of-arrival (DOA) detection. Furthermore, we analyze the CramerRao bound (CRB) of the proposed method and compare it with a single-cycle method. Through extensive simulations, we demonstrate that the proposed method outperforms the existing methods in terms of radar imaging and velocity estimation. Finally, we validate the effectiveness of the proposed method in complex scenarios through real-world experiments.