In ultra-wideband through-the-wall radar (TWR) imaging, conventional back projection (BP) algorithms offer clear physics and accurate wall delay compensation. However, their reliance on global delay traversal leads to high computational cost. The increasing compactness of modern TWR systems and the latency constraints of time-critical missions render existing BP methods inadequate for practical requirements. To reduce the redundant computations of BP in sparse TWR scenes, this paper proposes an energy selective forward projection (ESFP) method as an acceleration method built on the BP imaging model. After preprocessing enhances target sparsity, a closed-form mapping between the equivalent electrical length and the range-azimuth grid is constructed for each transmit-receive pair, which enables direct forward projection of selected echoes. On this basis, an adaptive high-energy range bin selection strategy is developed. It identifies a compact set of informative bins by examining peak structure characteristics and consistency across channels, without requiring an explicit SNR estimate as an input parameter, target number priors, or iterative optimization. Tests on a TWR platform show that ESFP reduces the imaging time by over an order of magnitude compared with representative BP algorithms, while maintaining comparable focusing performance in the sparse TWR scenarios.

Accurate imputation of missing data is crucial in the Industrial Internet-of-Things (IIoT), where operations are often compromised by noisy samples from harsh environments. Traditional imputation methods struggle with such noise due to their black-box nature or lack of adaptability. To address this issue, we recast data imputation as a distribution alignment challenge, utilizing the flexibility of optimal transport (OT) to handle noisy samples. Specifically, we first introduce the Proximal Optimal Transport (POT) problem, where the transportation cost is obtained by the network simplex approach with a selective matching mechanism, which renders it capable of matching distributions with noisy samples. Subsequently, we propose the POT-I framework, where the objective is to minimize the transport cost of POT. The produced gradient is used to refine the imputation value, which achieves missing data imputation (MDI) while getting robustness to noisy samples. Experiments on real-world IIoT datasets demonstrate the superiority of POT-I over state-of-the-art imputation methods.

To address the shortcomings of existing vital signs detection radar systems, such as complex hardware design and high cost, and considering the need for versatility and flexibility, this paper designs and implements a continuous wave (CW) vital signs detection radar RF front-end based on AD9361. The AD9361 RF transceiver features high integration and strong flexibility. Key parameters including local oscillator frequency, signal bandwidth, and sampling rate can be adjusted through software programming, which effectively simplifies the hardware design of vital signs detection radar. First, this paper elaborates on the principle of vital signs detection. Second, it details the overall design scheme, including the system’s overall architecture and main software modules. Finally, the effectiveness and reliability of the system are verified through local oscillator testing experiments, loop-back experiments, and actual vital sign measurement analysis. The design scheme proposed in this paper can provide technical references for the development of CW vital signs detection radar towards flexibility, miniaturization, and low cost, and has certain engineering application value.

SAR high maneuverability platform is one of the future development needs. However, the high maneuverability of the platform can lead to nonlinear flight trajectories, which can result in severe 2-D spatial variability of the signal, imposing the traditional full aperture frequency domain algorithms to fail. Spurred by these limitations, this article introduces a hybrid parametric and nonparametric full-aperture imaging algorithm. The proposed algorithm uses the two-step and range migration algorithm (RMA) for range cell migration correction. RMA is followed by a hybrid parameterized nonlinear chirp scaling algorithm and a nonparameterized phase gradient autofocus algorithm to correct second-order and third-order azimuth spatial variant characteristics. Finally, a resampling algorithm corrects the remaining fourth-order azimuth spatial variant characteristic. Extensive simulation imaging results effectively validate the effectiveness of the algorithm.

In passive localization of frequency-hopping (FH) emitters, the unknown carrier frequencies destroy the inter-pulse phase coherence required for synthetic aperture processing. This degrades the accuracy of range parameter estimation and emitter localization. Meanwhile, the carrier frequency and the reference slant range are highly coupled in the azimuth phase history. As a result, a deviation in the carrier frequency is equivalent to an offset in the slant range estimate. The two parameters are difficult to estimate independently, which further limits the ultimate localization accuracy. To address these issues, a bistatic synthetic aperture passive localization method for FH emitters based on full-pulse azimuth accumulation entropy is proposed in this paper. By alternately optimizing the FH carrier frequency sequence and the reference slant range, their coupling in the azimuth phase history is decoupled. The estimation accuracy of the FH carrier frequencies is improved, and high-quality two-dimensional focusing results are obtained. The position and velocity of the emitter are then solved from the bistatic range-history coefficients. The effectiveness of the proposed method is verified through simulation results.

Ultra-wideband MIMO through-wall radar has been widely used in disaster rescue, anti-terrorism investigation, and intelligent security through multi-antenna cooperative transmission and reception, which not only significantly improves the radial and azimuth resolution, but also has strong medium penetration ability. However, in practical applications, existing systems and mainstream detection algorithms rely too much on the prior modeling of walls and noise parameters, which can easily produce false alarms and missed detections in unknown walls or dynamic scenes. In order to solve above challenges, this paper proposes a joint algorithm based on local contrast detection and adaptive region-growing feature discrimination, which first enhances the local contrast of the PCF-weighted BP radar image, and then introduces the region-growing algorithm to segment target area, and eliminates false alarms according to the geometric features of region. Simulation and field experiments show that the proposed algorithm has better comprehensive performance.

In through-the-wall detection scenarios with low signal-to-noise ratio (SNR) and strong clutter, existing target detection methods generally suffer from inaccuracies, poor real-time performance, and the limitation of detecting only moving or stationary targets. To address these challenges, this paper proposes a through-the-wall radar (TWR) target detection method based on cross-correlation adaptive robust principal component analysis (CCARPCA) capable of simultaneously detecting multiple moving and stationary targets. First, pulse compression is applied to original echo signals using the inverse fast Fourier transform, resulting in high-resolution one-dimensional range profiles. Second, the principal component analysis algorithm suppresses strong clutter interferences, thereby improving the SNR. Next, the back projection algorithm is employed for multi-channel coherent imaging, enabling the extraction of 2-dimensional information and enhancing the sparsity of cross-correlation data. Lastly, considering the drawbacks of the robust principal component analysis (RPCA), such as long detection time and poor robustness, this paper introduces the cross-correlation coefficient and proposes the CCARPCA algorithm, which completely separates the target from the background noise. The experimental results based on a series of simulated and measured data demonstrate the effectiveness of the proposed method in detecting both moving and stationary targets behind walls. Compared to generalized likelihood ratio test, constant false alarm rate, and RPCA, our method achieves a substantial improvement of over 16.4% in detection accuracy based on measured data while maintaining real-time detection capability. Additionally, its detection performance is less sensitive to changes in initial parameters, indicating its superior robustness.

As a novel configuration of along-track multistatic synthetic aperture radar (Multi-SAR), the high frame rate along-track swarm SAR (ATS-SAR) has garnered significant attention in recent years due to its exceptional efficiency in reducing data acquisition time. Motivated by its potential for high-resolution imaging of moving targets, this article investigates the application of ATS-SAR in moving target imaging. However, high frame rate ATS-SAR-based moving target imaging confronts two critical challenges: time-space coupling and partial data loss in moving target echoes. To address these challenges, we first conduct a comprehensive analysis and theoretical derivation of the moving target echo model under the high frame rate ATS-SAR configuration. Subsequently, we propose an innovative motion parameter estimation algorithm that exploits unique echo characteristics to achieve high-performance imaging. Furthermore, we introduce the high-resolution, high frame rate ATS-SAR subaperture collaborative imaging algorithm for moving targets (MT-SACIm-ATS). Extensive simulations and a real measured experiment validate the effectiveness of the MT-SACIm-ATS algorithm, demonstrating imaging performance that closely approximates reference imaging results. Comparative analysis with several state-of-the-art algorithms further highlights the superiority of the proposed approach in terms of resolution and robustness.

Detecting multiple human targets in indoor scenarios using ultra-wideband (UWB) radar usually involves false detection results caused by the secondary reflections, which might reduce the target detection accuracy and cause a more severe deterioration when the number of targets increases. This article proposed a two-step accuracy improvement method for multitarget detection in environments with multiple human targets of more than three and strong secondary reflections by the surroundings, especially the walls. Based on the rough detection results acquired by the modified CA-CFAR (MCA-CFAR) processing, the first step achieves the primary false alarm suppression using a short-window accumulation in the time domain. Then, the second step applies the decision confidence on the detection results from the first step to assess the reliability of results for improved accuracy. The two-step accuracy improvement could thus have a higher accuracy through cascading false alarm suppression. The effectiveness and accuracy of the proposed algorithm are verified based on the experimental results.

The echo characteristics of moving ship targets are complex and difficult to focus using traditional imaging algorithms. This article proposes an modified nonlinear chirp scaling algorithm (NCSA) algorithm, which first analyzes the echo model of a moving ship, then uses the RMA algorithm to implement Range Cell Migration Correction ( RCMC) , and finally uses the improved NCS algorithm to correct the azimuth space-variance. The simulation results show that the proposed algorithm has good focusing effect.

With a rather strong environment clutter and multi-target interference, while indoor monitoring using FWD radar in complex scenarios, the current CA-CFAR algorithm would inevitably introduce the variance peaks leading to the false detection results in multi-target detection. This paper proposed a Modified CA-CFAR based on the Sliding Window DC Removal and Adaptive Threshold method, using Sliding Window DC Removal to eliminate the static environment clutter to avoid the previous variance peaks involved by replacing the MTI and achieving the Adaptive Threshold through echo statistic estimation to reduce the false detection results. The effectiveness of the proposed algorithm has been verified through comparative analysis based on radar-measured data.

The knowledge of heart and respiratory rates (HRs and RRs) is essential in assessing human body static. This has been associated with many applications, such as survivor rescue in ruins, lie detection, and human emotion detection. Thus, the vital signal extraction from radar echoes after pre-treatments, which have been applied using various methods by many researchers, has exceedingly become a necessary part of its further usage. In this review, we describe the variety of techniques used for vital signal extraction and verify their accuracy and efficiency. Emerging approaches such as wavelet analysis and mode decomposition offer great opportunities to measure vital signals. These developments would promote advancements in industries such as medical and social security by replacing the current electrocardiograms (ECGs), emotion detection for survivor status assessment, polygraphs, etc.

An event-triggered control strategy based on extended state observer (ESO) is proposed for the attitude tracking problem of small plug-and-play spacecraft with uncertain inertia parameters, external disturbances, and actuator faults. A simplified controller is developed based on the angular velocity and the general disturbances estimated by the provided ESO using the information of the system inputs and the angular velocities. In the designed event-triggered sampling mechanism, a state-dependent event-triggered strategy determines the triggering instant of the controller to reduce the frequency of information transmission between the controller and the actuator. In comparison with the previous literature, this paper considers uncertain inertia parameters, external disturbances, and actuator faults as general disturbances estimated by ESO, especially for the actuator faults. The inputs of ESO are the error of the angular velocities, which can simplify the controller design. Moreover, the designed ESO can effectively attenuate the influence of measured noises generated by the gyroscopes. The proposed event-triggered policy balances the performance of event-triggering and the control stability performance, which reduces the final state convergence regions without increasing more triggering times compared to existing studies. Furthermore, the investigated policy achieves Zeno-free triggering. Numerical simulations verify theoretical results. (c) 2022 COSPAR. Published by Elsevier B.V. All rights reserved.

The nonlinear characteristics of the motion trajectory of the synthetic aperture radar (SAR) flight platform can lead to severe two-dimensional space-variance characteristics of the signal, greatly affecting the imaging quality, and are currently considered as one of the difficulties in the field of SAR imaging. This paper first discusses the nonlinear trajectory SAR model and its space-variance characteristics and then discusses algorithms such as scaling-based algorithms, interpolation-based algorithms, time-domain algorithms, and hybrid algorithms. The relative merits and applicability of each algorithm are analyzed. Finally, computer simulation and actual data validation are conducted.

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Human vital signs such as respiratory and heart rates (RR and HR) are reflections of human body status, which are widely applied to rescuing or clinical estimation. In this study, an improved algorithm combining the Variational Mode Decomposition (VMD) and empirical wavelet transform (EWT) is proposed for human vital sign signal monitoring using Impulse Radio Ultra-Wide Band (IR-UWB) radar. The VMD processes the initial data and decomposes it into sub-signals to obtain the RR component. The HR-related ones with intermodulation components, determined by the power of the signal in the corresponding frequency band of HR, then become the input of the EWT. The processing results of the combined algorithm using both measured and simulated data reveal high accuracy.

In this study, an adaptive dynamic programming (ADP) control strategy based on the strain measurement of a fiber Bragg grating (FGB) sensor array is proposed for the vibration suppression of a complicated flexible-sloshing coupled system, which usually exists in aerospace engineering, such as launch vehicles with a large amount of liquid propellant as well as a flexible beam structure. To simplify the flexible-sloshing coupled dynamics model, the equivalent spring-mass-damper (SMD) model of liquid sloshing is employed, and a finite-element method (FEM) dynamic model for the beam structure coupled with the liquid sloshing is mathematically established. Then, a strain-based vibration dynamic model is derived by employing a transformation matrix based on the relationship between displacement and strain of the beam structure. To facilitate the design of a strain-based control, a tracking differentiator is designed to provide the strains’ derivative signals as partial states’ estimations. Feeding the system with the strain measurements and their derivatives’ estimations, an ADP controller with an action-dependent heuristic dynamic programming structure is proposed to suppress the vibration of the flexible-sloshing coupled system, and the corresponding Lyapunov stability of the closed-loop system is theoretically guaranteed. Numerical results show the proposed method can effectively suppress coupled vibration depending on limited strain measurements irrespective of external disturbances.

Aiming at the problem of target detection for multiple source information fusion, in this article, a decision-level fusion algorithm for visible and SAR images is proposed. First, using the Faster-RCNN network detects visible and SAR images to retain the detection results, respectively. Second, the semantic segmentation of visible images based on U-Net is realized. Finally, based on the detection results of single source and semantic segmentation results of land and sea, a fusion strategy based on decision level is proposed to achieve accurate target detection under multisource information. Through experimental verification, the detection performance of the proposed algorithm is an advantage over that of single-source image detection. The detection accuracy is 2.87% and 4.73% higher, and the recall rate is 3.02% and 0.19% higher than that of visible and SAR images separately. Compared with other target detection algorithms based on traditional image fusion, the proposed method has fewer false detections and missed detections.