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For airborne radar, detecting a low–slow–small (LSS) target is a hot and challenging topic, which results from the rapidly increasing number of non-cooperative flying LSS targets becoming of widespread concern, and the low signal-to-clutter ratio (SCR) of LSS targets results in the targets being particularly easily overwhelmed by the clutter. In this paper, a novel light gradient boosting machine (LightGBM)-based LSS target detection algorithm for airborne radar is proposed. The proposed method, based on the current real-time clutter environment of the range cell to be detected, firstly designs a specific real-time space-time LSS target signal repository with special dimensions and structures. Then, the proposed method creatively designs a new fast-built real-time training feature dataset specifically for the LSS target and the current clutter, together with a series of unique data transformations, sample selection, data restructuring, feature extraction, and feature processing. Finally, the proposed method develops a unique machine learning-based LSS target detection classifier model for the designed training dataset, by fully excavating and utilizing the advantages of the ensemble decision trees-based LightGBM. Consequently, the pre-processed data in the range cell of interest are classified using the proposed algorithm, which achieves LSS target detection by evaluating the output results of the designed classifier. Compared with the traditional classical target detection methods, the proposed algorithm is capable of providing markedly superior performance for LSS target detection. With an appropriate computational time, the proposed algorithm attains the highest probability of detecting LSS targets under the low SCR. The simulation outcomes and detection results with the experimental data are employed to validate the effectiveness and merits of the proposed algorithm.

A Frequency Diverse Array (FDA), developed innovatively based on phased array radar, can obtain an angle-range-time-dependent multidimensional transmit beampattern by modulating frequencies across different transmit antenna elements, which considerably increases the beam control ability and signal processing dimension. After joint transmit-receive processing, an FDA can be applied to various areas, such as multidimensional parameter joint estimation, mainlobe deceptive jammer suppression, ambiguous clutter suppression, and high-resolution and wide-swath imaging. This study investigates the waveform design and signal processing method of a multifunctional integrated system based on an FDA from the system level, with emphasis on new signal processing methods for integrated detection and estimation, integrated ambiguity resolution and jammer suppression, as well as integrated Synthetic Aperture Radar (SAR) imaging and moving target detection. Moreover, the application prospects of FDA multifunctional integrated systems are provided.

ABSTRACT Aim Design Method Results Conclusion Implication for the Profession and/or Patient Care Reporting Method Patient or Public Contribution To explore frail older adults' preferences and needs regarding mobile health (mHealth) exercise interventions in China. Additionally, it sought to identify the nudge strategies necessary for initiating and sustaining exercise behaviours among frail older adults.A qualitative study.The semi‐structured interviews were conducted between April and May 2024 from two communities in Changsha, China. The data were analysed using a deductive framework analysis aligned to nudge theory, and an inductive thematic analysis to gather relevant needs and preferences.This study involved 14 participants with pre‐frailty or frailty, aged 60–82 years (median age of 64 years). While participants were generally receptive to new technologies, lower levels of health literacy and competing priorities often hindered their participation. Three primary functionality requirements were as follows. (1) Profession engagement: tailored exercise prescription, professional and timely feedback and guidance; (2) personalised knowledge encompassing pain management, successful cases and inspiration; (3) beneficial, tailored, dynamic, fragmented, challenging exercise courses. Participants showed positive attitudes towards simplification nudges, gamification nudges, social nudges, trustworthy nudges, reminder nudges, economic nudges, feedback nudges and pre‐commitment nudges. Addressing privacy concerns was essential to build trust and acceptance among older adults.These findings emphasised the importance of designing mHealth interventions that address frail older adults' specific needs and preferences while incorporating effective nudge strategies to promote engagement and adherence. Future researchers should explore wearables, ChatGPT language models, virtual coaching assistants, exercise snack to further optimise the experience and analyse the effects of nudges in mHealth exercise interventions among older adults.Exercise systems or app development for frail older adults should meet three basic functionality and essential nudge strategies.The consolidated criteria for reporting qualitative research (COREQ) guidelines were used for reporting.Older adults' engagement and interview data contribute a lot. [ABSTRACT FROM AUTHOR]

Abstract DOA estimation based on sparse representation in a non‐uniform noise environment is proposed using a tail minimisation technique. The noise‐free covariance matrix is modelled as diagonal and off‐diagonal components relating to incoming signals. In order to combat coherent signals, the auto‐ and cross‐correlation entries of the incident signal are considered separately by using the least square (LS) criterion. Subsequently, from the perspective of difference co‐array a vectorised covariance matrix is obtained to emulate the received signal similar to the output of a single snapshot. The effect of non‐uniform noise is significantly mitigated via a removal operation. A tail minimisation scheme that is capable of deriving sharper spectra peaks is then applied to detect the DOAs. The proposed approach has the advantages of a larger virtual array aperture beneficial to DOA estimations of uncorrelated and/or coherent signals. The technique can suppress false spectral peaks to a certain extent and is also computationally effective. Numerical simulation verifies the effectiveness of the proposed method.

Aimingat non-side-looking airborne radar, we propose a novel unsupervised affinity propagation (AP) clustering radar detection algorithm to suppress clutter and detect targets. The proposed method first uses selected power points as well as space-time adaptive processing (STAP) weight vector, and designs matrix-transformation-based weighted input data, with which the first unsupervised weighted AP clustering is proposed by means of their similarity matrix, responsibility values and availability values. Then, new reconstructed weighted power inputs are designed, and the second weighted AP clustering is proposed. Finally, with their cluster results, a detection-discriminant criterion is designed for the judgment of target detection, and simultaneously, the clutter is suppressed. Compared with the conventional and important STAP, ADC and JDL algorithms, and several SO-based, GO-based and OS-based CFAR algorithms, the proposed unsupervised algorithm achieves much higher probability of detection and provides distinctly superior target-detection performance. With reasonable computation time, it can better conquer the range dependence in characteristic of clutter and better process non-independent identically distributed (non-IID) samples of non-side-looking radar. Sufficient simulations are performed, and they demonstrate that the proposed unsupervised algorithm is preferable and advantageous.

Load balancing is of great significance to extend the longevity of wireless sensor networks, due to the inherent imbalanced energy overhead in such networks. However, existing solutions cannot balance the load distribution in partially disconnected wireless sensor networks. For example, if a network is partitioned into several segments with different area sizes, some areas have much more traffic load than other areas. In this article, we propose a load-balanced routing scheme, which aims to balance energy consumption within each segment and among different segments. First, we adopt unequal transmission distances to build initial routing for intrasegment load balancing. Second, we adopt the genetic algorithm to build extra routing between different segments for intersegment load balancing. The unique character of our work is twofold. On one hand, we investigate partitioned wireless sensor networks where there are several isolated segments. On the other hand, we pursue load balancing from a global perspective rather than from a local one. Some simulations verify the effectiveness and the advantages of our scheme in terms of extra deployment cost, system longevity, and load balancing degree.

Shadows are widely used in the tracking of moving targets by video synthetic aperture radar (video SAR). However, they always appear in groups in video SAR images. In such cases, track effects produced by existing single-target tracking methods are no longer satisfactory. To this end, an effective way to obtain the capability of multiple target tracking (MTT) is in urgent demand. Note that tracking by detection (TBD) for MTT in optical images has achieved great success. However, TBD cannot be utilized in video SAR MTT directly. The reasons for this is that shadows of moving target are quite different from in video SAR image than optical images as they are time-varying and their pixel sizes are small. The aforementioned characteristics make shadows in video SAR images hard to detect in the process of TBD and lead to numerous matching errors in the data association process, which greatly affects the final tracking performance. Aiming at the above two problems, in this paper, we propose a multiple target tracking method based on TBD and the Siamese network. Specifically, to improve the detection accuracy, the multi-scale Faster-RCNN is first proposed to detect the shadows of moving targets. Meanwhile, dimension clusters are used to accelerate the convergence speed of the model in the training process as well as to obtain better network weights. Then, SiamNet is proposed for data association to reduce matching errors. Finally, we apply a Kalman filter to update the tracking results. The experimental results on two real video SAR datasets demonstrate that the proposed method outperforms other state-of-art methods, and the ablation experiment verifies the effectiveness of multi-scale Faster-RCNN and SimaNet.

Clutter suppression is a key problem for airborne radar, and space-time adaptive processing (STAP) is a core technology for clutter suppression and moving target detection. However, in practical applications, the non-uniform time-varying environments including clutter range dependence for non-side-looking radar lead to the training samples being unable to satisfy the sample requirements of STAP that they should be independent identical distributed (IID) and that their number should be greater than twice the system’s degree of freedom (DOF). The lack of sufficient IID training samples causes difficulty in the convergence of STAP and further results in a serious degeneration of performance. To overcome this problem, this paper proposes a novel autoencoder neural network for clutter suppression with a unique matrix designed to be decoded and encoded. The main challenges are improving the accuracy of the estimation of the clutter-plus-noise covariance matrix (CNCM) for STAP convergence, designing the form of the data input to the network, and making the network successfully explored to the improvement of CNCM. For these challenges, the main proposed solutions include designing a unique matrix with a certain dimension and a series of covariance data selections and matrix transformations. Consequently, the proposed method compresses and retains the characteristics of the covariances, and abandons the deviations caused by the non-uniformity and the deficiency of training samples. Specifically, the proposed method firstly develops a unique matrix whose dimension is less than half of the DOF, meanwhile, it is based on a processing of the selected clutter-plus-noise covariances. Then, an autoencoder neural network with l2 regularization and the sparsity regularization is proposed for the unique matrix to be decoded and encoded. The training of the proposed autoencoder can be achieved by reducing the total loss function with the gradient descent iterations. Finally, an inverted processing for the autoencoder output is designed for the reconstruct ion of the clutter-plus-noise covariances. Simulation results are used to verify the effectiveness and advantages of the proposed method. It performs obviously superior clutter suppression for both side-looking and non-side-looking radars with strong clutter, and can deal with the insufficient and the non-uniform training samples. For these conditions, the proposed method provides the relatively narrowest and deepest IF notch. Furthermore, on average it improves the improvement factor (IF) by 10 dB more than the ADC, DW, JDL, and original STAP methods.