IET Radar, Sonar and Navigation

Space-time adaptive processing (STAP) methods can provide good clutter suppression potential in airborne radar systems. However, the performance of these methods is limited by the training samples' support in practical applications. To address this issue, a deep learning framework for STAP is developed. First, the clutter space-time data and their exact clutter covariance matrices (CCMs) are simultaneously modelled via simulation, in which various non-ideal factors such as aircraft crabbing, array errors, and internal clutter motion with all possible levels in practice are all considered. Then, a multi-layer two-dimensional convolutional neural network (CNN) is developed. In this CNN, low-resolution angle-Doppler profiles estimated by a few training samples are used for the input and the high-resolution counterpart obtained by the exact CCMs are utilized for the labels. Once trained, the CNN can be used to predict the high-resolution angle-Doppler profile using a few measured data in near real time. The high-resolution clutter spectrum can be further calculated using the space-time steering dictionary and the above obtained profile. Finally, the CCM of the measured data can be constructed and the space-time weight vector can also be achieved. Compared with recently developed sparsity-based STAP methods, the performance of the proposed method is better and the computational load of it is far fewer, and therefore more suitable for real-world implementation. The simulation results have demonstrated the superiority of the proposed method in both clutter suppression performance and computation efficiency.

Keqing Duan

Hui Chen

Wenchong Xie

Yongliang Wang

IET Radar Sonar & Navigation

Deep learning for high‐resolution estimation of clutter angle‐Doppler spectrum in STAP

Abstract To enhance the capability of identifying unknown emitters in open spaces, an open‐multiscale attention kernel (MSAK)‐convolutional neural network‐long short‐term memory (CNN‐LSTM) structure is proposed. To this end, first, a MSAK module and CNN‐LSTM structure are introduced, and then, the depth and complexity of the feature extraction network are improved to enhance its representation capability. To classify unknown emitters accurately, the MSAK‐CNN‐LSTM model is improved to obtain an open‐MSAK‐CNN‐LSTM model with open‐set recognition capability. Additionally, the two preprocessing procedures are summarised, and their strengths and weaknesses are compared. Experimental results show that the proposed open‐MSAK‐CNN‐LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space. In addition, it has significant advantages in low signal‐to‐noise ratio (SNR) scenarios.

Yuanhao Zheng

Jiantao Wang

Jie Huang

Open space radar specific emitter identification using MSAK‐CNN‐LSTM network

In the past few years, the commercial use of drones has exploded, since they are a safe and cost-effective solution for many kinds of problems. However, this fact also opens the door for malicious use. This work presents a novel system able to detect and recognise drones from other targets, allowing the police and security agencies to deal with this new aerial thread. The proposed system only uses a persistent range–Doppler radar, avoiding the restrictions of the optical sensors, usually required for the recognition part. The processing is based on constant false alarm rate detection stage, followed by a convolutional neural network that performs the recognition. This network takes as input raw range–Doppler radar data and predicts their class (car, person, or drone). For this purpose, an extensive controlled trial test campaign has been performed, resulting in a novel dataset with more than 17,000 samples of drones, cars, and people, acquired in real outdoor scenarios. As far as authors’ knowledge, this is the first range–Doppler radar database for the recognition of drones and other targets. The high-accuracy results (99.48%) suggest that this system could be successfully used in security and defence applications to discriminate between drones and other entities.

Ignacio Roldan

Carlos R. del‐Blanco

Álvaro Duque de Quevedo

Fernando Ibañez Urzaiz

Javier Gismero Menoyo

Alberto Asensio López

Daniel Berjón

Fernando Jaureguizar

Narciso García

IET Radar, Sonar &amp; Navigation

DopplerNet: a convolutional neural network for recognising targets in real scenarios using a persistent range–Doppler radar

By partial interception and multiple forwarding of a radar transmitting signal, digital radio frequency memory-based interrupted sampling repeater jamming can yield a partial processing gain and form multiple false target groups in the range direction, achieving jamming effects of both suppression and deception. Various improved jamming strategies have been proposed, while jamming suppression problems have not been fully addressed. In this study, a jamming suppression method based on the idea of ‘reconstruction and cancellation’ is proposed by analysing the jamming principle. The method firstly analyses the pulse compression results with time-frequency analysis to obtain the intercepted slice number and forwarding times; then, the slice width is estimated by deconvolution processing; finally, iterative cancellation is used to suppress the jamming. Performance of the method was verified by Monte Carlo simulation. The results show that the normalised error of the slice width estimation is <5% when the jamming-to-noise ratio reaches 15 dB (after pulse compress); the cancellation method can effectively restrain the jamming by bringing an improvement of signal-to-jamming ratio >16 dB.

Chao Zhou

Quanhua Liu

Xinliang Chen

Parameter estimation and suppression for DRFM‐based interrupted sampling repeater jammer

基于DRFM的中断采样转发干扰参数估计与抑制

In the military and civilian surveillance domain, it is of great significance to mine regular behaviours of targets for situation awareness and command decision support. Most of the existing trajectory clustering algorithms only consider the similarity of spatial position of the trajectory, without sufficient multi-dimensional information such as time, course and velocity. Some approaches based on information fusion take these multi-dimensional information into account, but the features with different dimensions fused by weight coefficients are not robust and universal for different scenarios. In this paper, a regular behaviour mining method based on spatiotemporal trajectory multi-dimensional features and density clustering is proposed. Firstly, multi-dimensional Hausdorff similarity is defined to measure spatiotemporal trajectory from different feature dimensionalities. Different from methods based on information fusion, the proposed method defines trajectory density in feature similarity of different dimensions and adaptively determines parameters according to feature distribution in different dimensions. Experimental results in simulated and radar measured trajectory data show that the proposed method can be accurate and robust in clustering evaluation indexes such as Purity, Precision, Recall and Rand Index from different scenarios, which has a good application prospect in intelligent surveillance tasks.

Qiaowen Jiang

Yu Liu

Bencai Wang

Qifang He

Caisheng Zhang

Ziran Ding

Tao Jian

Mining regular behaviors based on spatiotemporal trajectory multi‐dimensional features

基于时空轨迹多维特征的规律行为挖掘

Abstract In order to further improve the accuracy of the non‐linear positioning model in the research of ultra wide band (UWB) indoor positioning, a Gaussian unscented Kalman filter (GUKF) algorithm is proposed in this paper. This localisation algorithm first uses a Gaussian function to design a Gaussian smoothing filter template to process the smoothing of experimental data in the GUKF algorithm, and then the filtering algorithm is used to obtain higher positioning accuracy. This paper utilises simulations and actual experiments to verify and analyse the GUKF algorithm, and the actual experiment environment was divided into line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS) experimental environments. The measured experimental results indicate that in the static test of location tags in LOS and NLOS experimental environments, the root mean square error (RMSE) of the GUKF algorithm is reduced by 15.88% and 14.10%, respectively; in the dynamic test, the RMSE of the GUKF algorithm is reduced by 16.67% and 17.89%, respectively, compared with the unscented Kalman filter algorithm. In addition, the positioning performance evaluation method of the mean error and cumulative distribution function curve also verifies that the GUKF algorithm has a higher positioning accuracy than the UKF, Least Squares, and Time of Arrival algorithms.

DaLong Sun

Mingsheng Wei

Yiyang Lyu

Di Wang

Shidang Li

Wenshuai Li

Lei He

Shihu Zhu

A Gaussian Unscented Kalman Filter algorithm for indoor positioning system using Ultra Wide Band measurement

使用超宽带测量的室内定位系统的高斯无迹卡尔曼滤波算法

Abstract In future battlefield scenarios, the high density of platforms often leads to multiple responses from existing secondary surveillance radar (SSR) systems, causing collision interference of response signals in the time domain. The frame slotted ALOHA (FSA) algorithm originally used by the system cannot ensure a high identification probability and has a long identification time. In response to these problems, this paper explores the problems of current SSR systems under the urgent need for precise multi‐target identification in dense environments. It investigates how to integrate code division multiple access (CDMA) technology with the ALOHA algorithm to enhance the system's target identification probability. The authors propose an improved workflow and signal transceiver principles for the SSR system and validate the excellent performance of the SSR system in multi‐target identification within dense environments through simulation experiments based on the proposed composite anti‐collision algorithm.

Haonan Chen

Rui Guo

Zengping Chen

Enhanced performance of secondary surveillance radar system in dense UAV environments using CDMA techniques

使用CDMA技术在密集无人机环境中提升二次监视雷达系统性能

Abstract The classification methods for hyperspectral images (HSI) based on decision trees and convolutional neural networks have shown increasing advantages, but these methods often require a large number of labelled samples for learning, which is difficult for HSI, and the interpretability of the network is not high. Therefore, this paper proposes classification methods based on improved attention interpretable table learning (TabNet) named 3D TabNet HSI (3D‐TabNetHS) and unsupervised 3D TabNet HSI (U3D‐TabNetHS). These methods use sequential attention to select appropriate HSI spatial‐spectral features and add a space spectral information extraction (SSE) module composed of a 3D convolutional neural network (3D‐CNN) and fully connected layers to the Attention Transformer module in the original TabNet network to extract spatial‐spectral soft features. At the same time, unsupervised learning can be used to retrain the 3D‐TabNetHS network, and the classification accuracy of the resulting U3D‐TabNetHS network can be further improved. Compared with other HSI classification methods based on decision trees, the HSI classification accuracy of 3D‐TabNetHS is higher. On three typical HSI datasets, the accuracy metric overall accuracy of 3D‐TabNetHS reached as high as 98.71%, 94.73%, and 97.23%, respectively. Simultaneously, the consistency evaluation metric Kappa also reached 98.56%, 93.98%, and 96.31% respectively. The experimental results indicate the feasibility and reliability of the proposed method in HSI classification.

Ning Li

Daozhi Wei

Shucai Huang

Yong Zhang

3D‐TabNetHS: A hyperspectral image classification method based on improved interpretable 3D attentive TabNet

3D‐TabNetHS：一种基于改进的可解释3D注意力TabNet的高光谱图像分类方法

Abstract This paper presents a single‐antenna receiver passive radar system in the context of moving target detection such as trains, car, planes and UAVs, leveraging the long‐term evolution (LTE) network as an illumination source. The proposed system uses signal reconstruction enabled by the telecom structure of the opportune signal in order to forego the use of a reference antenna. This presents the advantage of not relying on a physical signal for reference and its possible defect, potentially yielding better performances. The techniques introduced are validated through simulation and experiments. Moreover, a simplified passive radar system emphasises one of the key advantage of passive radar over other competing technologies for moving target detection: stealthiness and cost‐effectiveness.

Lionel de Guenin

Patrick Rosson

Nicolas Petrochilos

Eric Moreau

Single receiver Long‐Term Evolution passive radar system using signal reconstruction for moving target detection

使用信号重构的单接收机长期演进被动雷达系统用于移动目标检测

Abstract Graph‐based spectrum sensing in noisy environments has major implications for civilian and military signal processing applications. However, existing algorithms suffer from high computational complexity and performance deterioration at low signal‐to‐noise ratios (SNRs). Therefore, a spectrum‐sensing algorithm based on graph feature fusion using a quadratic form derived from self‐loop weights and the graph Laplacian matrix is proposed in this study. The sum of the first and second block maxima of the power spectrum of the observed signal is selected as the input to the graph converter. Self‐loop weights are combined with the Laplacian matrix to construct the graph quadratic form, which serves as the test statistic for decision‐making. By applying majorisation and the extreme value theory, it is demonstrated that the proposed algorithm outperforms existing methods. The simulation results confirm the robust spectrum‐sensing performance across various signal modulation types and pulse shapes. Thus, compared to existing algorithms, except block range‐ and energy‐detection‐based methods, the proposed algorithm demonstrates the best spectrum‐sensing performance under low SNRs and channel‐fading conditions, while achieving the lowest computational complexity. The proposed approach enables more efficient and accurate spectrum sensing, fostering advancements in communication technologies and defence applications.

Abbreviation	IET Radar Sonar & Navi
Journal Impact	1.49
Quartiles(Global)	TELECOMMUNICATIONS(Q4)

ISSN	1751-8784, 1751-8792
h-index	94

Publication Information	Publisher: John Wiley & Sons Inc.，Publishing cycle: Monthly，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2007，Proportion of original research papers: 98.65%，Self Citation Rate:14.30%， Gold OA Rate: 79.50%
Journal Fees	$2470Details
Average review cycle	网友分享经验：平均6.0个月
Average recruitment ratio	网友分享经验：约50%

IET Radar, Sonar and Navigation

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