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Bistatic radar exhibits spatial isomerism and diverse configurations, leading to unique clutter characteristics distinct from those of monostatic radar. The clutter rank serves as a pivotal indicator of clutter characteristics, enabling the quantification of clutter severity. Space-time adaptive processing (STAP) is a critical technique to detect moving targets, and clutter rank determines the number of independent and identically distributed (IID) training samples and the degree of freedom (DOF) for effective suppression of clutter that STAP requires. Therefore, the accurate estimation of clutter rank for bistatic radar can provide a crucial indicator for designing and constructing STAP processors, thereby facilitating fast and efficient clutter suppression in bistatic radar systems. This study is based on the idea that clutter rank is the number of prolate spheroidal wave function (PSWF) orthogonal bases utilized for approximating the clutter signal. Firstly, the challenge of utilizing PSWF orthogonal bases for approximating the clutter signal in bistatic radar is elucidated. This pertains to the fact that, unlike monostatic radar clutter, bistatic radar clutter is not capable of being expressed as a single-frequency signal. The clutter rank estimation for bistatic radar is thus derived as the frequency bandwidth estimation. Secondly, to achieve this estimation, the frequency distribution of each individual scattering unit is investigated, thereby determining their extending frequency broadening (EFB) as compared to that of single-frequency. Subsequently, the integral average of EFB across the entire range bin is computed, ultimately enabling the acquisition of bistatic radar’s frequency bandwidth. Finally, the estimation method is extended to non-side-looking mode and limited observation areas with pattern modulation. Simulation experiments confirm that our proposed method provides accurate clutter rank estimations, surpassing 99% proportions of large eigenvalues across various bistatic configurations, observation modes, and areas.

A convenient formula for the estimation of clutter degrees of freedom is derived in wideband multiple-input and multiple-output (MIMO) radar with frequency diversity waveforms. The estimation of the clutter rank of MIMO radar is extended to the mode of the wideband with the help of the time-bandwidth product lemma; this could be valuable for designing an optimal technique in the application of parameter estimation, target recognition etc. The proposed rule is well verified by simulation results.

A computational-efficient estimated rule of clutter rank for multiple-input multiple-output (MIMO) radar with arbitrary transmitted waveform synthetic strategies is presented and proved. It is suitable for both conventional phased array radar and MIMO radar with a uniform sparse or non-uniform sparse transmitting subarray, and is also valuable for the design of optimal waveform synthetic strategies and space–time adaptive processing algorithms. The effectiveness of the proposed rule is verified by simulation results.

In this paper, the null constraint coherent integration (NCCI) technique is proposed and validated its performance under various clutter conditions. The usable Doppler space fraction (UDSF) and minimum detectable Doppler frequency (MDDF) is mainly used for the validation when received clutter has different Doppler spectrum with the one used for the designing the projection matrix for the NCCI. By numerical simulation, it is observed that UDSF and MDDF of the NCCI is not affected by the received stationary clutter with different clutter bandwidth in the design range and kept its superiority over the conventional MTI plus FFT. For the received clutter with different center frequency, performance of the NCCI is hardly affected up to 0.01 of the normalized center frequency. (5 pages)

The effective discrimination of targets from backgrounds in environments characterized by a low signal-to-clutter ratio (SCR) is paramount for the advancement of infrared small target detection (IRSTD). In this work, we propose a novel detection framework predicated on low-rank sparse decomposition (LRSD), incorporating an improved logarithmic norm and a mechanism for sparse structure refinement, herein referred to as the improved logarithmic norm and sparse structure refinement (ILN-SSR). The ILN-SSR framework more precisely characterizes the sparse properties of both the background and the target, enabling a more effective distinction between the target and its background. Initially, our approach entails the utilization of an improved logarithmic norm to precisely estimate the low-rank attributes of the infrared image background. This is followed by the employment of a linear sparse regularization term alongside a target-traits-based sparse regularization term aimed at meticulously identifying targets within sparse regions and refining the sparse structure. Subsequently, we combine these components into the ILN-SSR framework, which formulates IRSTD as an optimization problem. The resolution of this framework is achieved through the implementation of the alternating direction method of multipliers (ADMM). The efficacy of the proposed framework is corroborated through the analysis of six image sequences. Comprehensive experimental assessments affirmed the framework’s substantial robustness in navigating various complex backgrounds.

Cerebral visual impairment (CVI) is the leading cause of visual impairment in children in high income countries. Despite its prevalence, awareness of CVI among educators remains low, meaning that many affected children may not receive the support they need in school. While previous research has highlighted the challenges faced by children with CVI, few studies have systematically assessed teacher awareness and the effectiveness of targeted educational interventions in improving classroom practices. This study addresses this gap by evaluating: (1) teacher awareness of CVI, (2) existing classroom practices that may impact children with CVI, (3) the effectiveness of two CVI educational media formats (video and text) in increasing understanding, and (4) the changes teachers would be willing to implement following exposure to these resources. By comparing the impact of these two formats, this study provides insights into how best to deliver CVI training for teachers in a way that is both accessible and effective. A total of 111 teachers from primary, secondary, and special schools across the UK participated in a survey incorporating either a three-minute video simulation or a 1.5-minute text-based resource about CVI. Before exposure, 72% of participants had not heard of CVI, with awareness particularly low among mainstream teachers (98% of primary and 80% of secondary teachers were unaware ). Teachers also reported inconsistent use of CVI-supportive practices, such as reducing classroom clutter and simplifying smart screen content. Both media formats significantly increased teachers’ willingness to implement changes (p < 0.0001). The text format showed a slightly greater increase in average Likert scores, and the Wilcoxon signed-rank test revealed a larger statistical effect for text (z = -12.91) compared to video (z = -8.90). However, the video format was also highly effective, producing a similarly strong impact, with both formats achieving an identical median increase of 1.0. These results suggest that while text may have led to slightly larger shifts in rank-based scores, the video format remained a powerful and engaging tool for increasing teachers’ willingness to implement CVI-supportive strategies. The findings suggest that small, manageable adaptations, such as reducing visual distractions and maintaining consistency in classroom layouts, are practical for teachers and may have a meaningful impact on children with CVI. This study highlights the potential of bite-size learning resources in raising awareness and encouraging evidence-based teaching adaptations. By providing concise, accessible materials, teachers can be equipped with strategies to support children with CVI while minimising additional workload demands. Future efforts should focus on scaling these resources to reach a wider audience, including families and caregivers, to foster a more inclusive understanding and response to CVI.

Ground-penetrating radar (GPR) is an effective geophysical electromagnetic method for underground target detection. However, the target response is usually overwhelmed by strong clutter, thus damaging the detection performance. To account for the nonparallel case of the antennas and the ground surface, a novel GPR clutter-removal method based on weighted nuclear norm minimization (WNNM) is proposed, which decomposes the B-scan image into a low-rank clutter matrix and a sparse target matrix by using a non-convex weighted nuclear norm and assigning different weights to different singular values. The WNNM method’s performance is evaluated using both numerical simulations and experiments with real GPR systems. Comparative analysis with the commonly used state-of-the-art clutter removal methods is also conducted in terms of the peak signal-to-noise ratio (PSNR) and the improvement factor (IF). The visualization and quantitative results demonstrate that the proposed method outperforms the others in the nonparallel case. Moreover, it is about five times faster than the RPCA, which is beneficial for practical applications.

Vessel diseases are often accompanied by abnormalities related to vascular shape and size. Therefore, a clear visualization of vasculature is of high clinical significance. Ultrasound color flow imaging (CFI) is one of the prominent techniques for flow visualization. However, clutter signals originating from slow-moving tissue are one of the main obstacles to obtain a clear view of the vascular network. Enhancement of the vasculature by suppressing the clutters is a significant and irreplaceable step for many applications of ultrasound CFI. Currently, this task is often performed by singular value decomposition (SVD) of the data matrix. This approach exhibits two well-known limitations. First, the performance of SVD is sensitive to the proper manual selection of the ranks corresponding to clutter and blood subspaces. Second, SVD is prone to failure in the presence of large random noise in the dataset. A potential solution to these issues is using decomposition into low-rank and sparse matrices (DLSM) framework. SVD is one of the algorithms for solving the minimization problem under the DLSM framework. Many other algorithms under DLSM avoid full SVD and use approximated SVD or SVD-free ideas which may have better performance with higher robustness and less computing time. In practice, these models separate blood from clutter based on the assumption that steady clutter represents a low-rank structure and that the moving blood component is sparse. In this paper, we present a comprehensive review of ultrasound clutter suppression techniques and exploit the feasibility of low-rank and sparse decomposition schemes in ultrasound clutter suppression. We conduct this review study by adapting 106 DLSM algorithms and validating them against simulation, phantom, and in vivo rat datasets. Two conventional quality metrics, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), are used for performance evaluation. In addition, computation times required by different algorithms for generating clutter suppressed images are reported. Our extensive analysis shows that the DLSM framework can be successfully applied to ultrasound clutter suppression.

Ultra-wideband (UWB) impulse radar is widely used for through-wall human respiration detection due to its high range resolution and high penetration capability. UWB impulse radar emits very narrow time pulses, which can directly obtain the impulse response of the target. However, the time interval between successive pulses emitted is not ideally fixed because of timing jitter. This results in the impulse response position of the same target not being fixed, but it is related to slow-time. The clutter scattered by the stationary target becomes non-stationary clutter, which affects the accurate extraction of the human respiration signal. In this paper, we propose a method for reducing timing jitter’s impact in through-wall human detection by UWB impulse radar. After the received signal is processed by the Fast Fourier transform (FFT) in slow-time, we model the range-frequency matrix in the frequency domain as a superposition of the low-rank representation of jitter-induced clutter data and the sparse representation of human respiratory data. By only extracting the sparse component, the impact of timing jitter in human respiration detection can be reduced. Both numerical simulated data and experimental data demonstrate that our proposed method can effectively remove non-stationary clutter induced by timing jitter and improve the accuracy of the human target signal extraction.

Self-localization based on passive RFID-based has many potential applications. One of the main challenges it faces is the suppression of the reflected signals from unwanted objects (i.e., clutter). Typically, the clutter echoes are much stronger than the backscattered signals of the passive tag landmarks used in such scenarios. Therefore, successful tag detection can be very challenging. We consider two types of tags, namely low-Q and high-Q tags. The high-Q tag features a sparse frequency response, whereas the low-Q tag presents a broad frequency response. Further, the clutter usually showcases a short-lived response. In this work, we propose an iterative algorithm based on a low-rank plus sparse recovery approach (RPCA) to mitigate clutter and retrieve the landmark response. In addition to that, we compare the proposed approach with the well-known time-gating technique. It turns out that RPCA outperforms significantly time-gating for low-Q tags, achieving clutter suppression and tag identification when clutter encroaches on the time-gating window span, whereas it also increases the backscattered power at resonance by approximately 12 dB at 80 cm for high-Q tags. Altogether, RPCA seems a promising approach to improve the identification of passive indoor self-localization tag landmarks.