Iet Radar Sonar and Navigation最新文献

During the procedure of three-dimensional (3D) moving target localisation in multistatic passive radar (MPR) system, conventional closed-form algorithms and their enhanced versions necessitate at least four transmitters to obtain unambiguous localisation, and they are prone to poor noise resistance. In this paper, based on multiple sets of bistatic range difference (BRD) and bistatic range rate difference (BRRD) measurements, an innovative closed-form algorithm is proposed which, combines an improved two-step weighted least squares (ITSWLS) using the Newton method (NM) to minimise the number of transmitters required for localization. In a 3D environment, this algorithm can precisely localise targets with merely three transmitters. Compared with the existing closed-form algorithms, this algorithm saves one transmitter resource, breaking through the constraints of traditional approaches. After theoretical analysis and simulation verification, in the presence of just three transmitters, the estimation accuracy of the algorithm for both near-field and far-field target parameters can reach the Cramér–Rao lower bound (CRLB) when the measurement noise is low. If an additional transmitter is incorporated, this algorithm has higher localization accuracy and better noise resistance compared to the elliptic localization (EL), TSWLS, ITSWLS, and Taylor algorithms.

Ground moving target indication (GMTI) plays a critical role in both civilian applications and military applications. The accuracy of complex images coregistration is a major factor for multichannel synthetic aperture radar (SAR) GMTI. Moreover, polarimetric interferometric SAR (PolInSAR) represents a growing strength in multi-function radar systems. In response to this, we propose a new SAR-GMTI approach tailored for the PolInSAR system. To address the deterioration in clutter suppression performance caused by the coregistration errors, we construct a joint scattering vector (JSV) for polarimetric SAR (PolSAR) by incorporating the neighbouring vectors of the central pixel. Subsequently, the covariance matrix of JSV is estimated and subjected to eigen-decomposed. Clutter suppression of the PolInSAR system for GMTI is performed using eigen-subspace projection, where the JSV is projected onto noise subspace. This method is robust against coregistration errors since all ground moving target information is encapsulated within the JSV. Furthermore, the improvement factors with different clutter backgrounds (by changing the signal-to-clutter-plus-noise-ratio, SCNR) are analysed. The effectiveness of the proposed JSV-based method is validated using simulated PolInSAR data.

Walking is fundamental to normal human life. However, many people suffer from walking impairments due to various diseases that may severely affect their daily activities. Early detection of an abnormal gait can aid subsequent treatment and rehabilitation. This paper proposes a novel abnormal gait recognition method based on a perceptual loss convolutional temporal autoencoder (PLCTAE) network. It comprises upstream and downstream tasks, both of which utilise radar micro-Doppler spectrograms as inputs. The upstream task employs a convolutional autoencoder with the perceptual loss to encode and decode micro-Doppler spectrograms, achieving unsupervised pretraining and obtaining the initial parameters for the convolutional part of the PLCTAE. The downstream task fine-tunes the convolutional part of the PLCTAE through supervised training to extract spatial features from the micro-Doppler spectrograms and incorporates a bidirectional long short-term memory (BiLSTM) network to further extract temporal features, accomplishing the task of abnormal gait classification. The experimental results demonstrate that the proposed method achieves good classification performance on the self-established dataset which is collected by Texas Instruments' IWR6843ISK millimetre-wave radar and contains eight types of abnormal gaits. The generalisation performance is also validated on a public dataset from the University of Glasgow containing six types of human activities.

It is our great pleasure to present you with this IET Radar, Sonar and Navigation special issue on “Electronic Attack and Protection for Modern Radar Systems and Radar Networks”. The recent development of fast digital to analogue converters (DACs) and analogue to digital converters (ADCs), field programmable gate arrays (FPGAs) and parallel computing has contributed to the development of modern radars that, at the same time, can also be more easily attacked using digital radio frequency memories (DRFMs). The development of passive and netted multiband, multistatic, multichannel radars has also changed the EW scenario significantly. Classical EA can be less effective against passive, multistatic and multichannel radars and, as a result, new methods and new technologies have to be developed for effective countermeasures. Multichannel and multistatic jammers have also started to play a role in the EW scene. The aim of this special issue was to gather some of the most recent work in this area. The result is a collection of 12 interesting and timely papers aiming to address current technical challenges in electronic warfare. The papers included in this collection covers areas around the more general electronic warfare context as well as address specific challenges of electronic attack and electronic protection as summarised below.

Global Navigation Satellite System (GNSS)-based passive bistatic radar (PBR) systems hold promise for use as low-altitude surveillance mechanisms in critical urban and suburban zones, attributed to their low power consumption, good concealment, and worldwide reach. However, the increasing demand for airspace regulation presents challenges for multi-target tracking. Additionally, the limited power budget of GNSS signals results in low target SNR, restricting the detection range. Hence, a novel multi-target track-before-detect (TBD) scheme is proposed. This strategy employs a dual-channel coarse focusing (DCCF)-based cluster centroid extraction algorithm to identify potential target information in the range Doppler (RD) domain. Subsequently, a modified cardinalised probability hypothesis density (MCPHD) filter is utilised, enhanced with a birth target intensity estimation module assisted by Doppler and a trajectory management module, to accurately track multiple targets under conditions of low SNR. Simulation results and performance analysis using the Optimal Sub-pattern Assignment (OSPA) metric confirm the effectiveness of our approach. Furthermore, in a real-world experiment using the GPS L5 signal as an illuminator, the authors successfully processed experimental data to track a civil aircraft over 10 frames, demonstrating the practical applicability of the proposed method in GNSS-based PBR systems.

This paper proposes a multi-target tracking fusion algorithm based on consensus arithmetic averaging to address the limitations of the sensor detection field of view and information fusion difficulty when tracking multiple targets in a distributed Airborne passive bistatic radar (APBR) network. Labelled Multi-Bernoulli (LMB) filters are executed locally when each node estimates multi-target tracks. A consensus strategy is utilised to execute the information interaction among sensors, taking into account the limited detection field of view of a single sensor. The paper proposes the LMB Consensus Arithmetic Average (LMB-CAA) fusion algorithm to address the issue of label inconsistency in multi-sensor LMB density fusion. The proposed algorithm is based on label decomposition and reconstruction and aims to avoid the influence of label mismatch on the fusion effect while simultaneously ensuring that the fusion is closed during information interaction and fusion. Simulation results demonstrate that the proposed algorithm can stably estimate multi-target tracks and improve the multi-target tracking fusion accuracy of distributed APBR networks.

This paper describes an optimised scattering power decomposition method suitable for ship detection in polarimetric synthetic aperture radar imagery. Based on the principal component analysis technique for dimension reduction and the scattering power decompositions with strong physical interpretation, a novel optimised scattering power decomposition model is proposed which comprises surface scattering, double-bounce scattering, ±45° oriented dipole, and volume scattering components. Wherein, the oriented dipole component can be used to characterise the compounded scattering of the target which is composed of even-bounce and odd-bounce reflectors. Furthermore, pocket algorithm and support vector machine are adopted to solve linear non-separable problems under complex experimental conditions in this study. Extensive experiments carried out on RADARSAT-2 data and NASA/JPL AIRSAR data show that the oriented dipole component significantly contributes to the optimised four-component decomposition method and can play a vital role in ship detection. Compared to other scattering power decompositions, the optimised decomposition method not only achieves better performance but is more in accordance with the real scattering mechanisms, which is more applicable to ship detection.

Jammers aim to interfere with active radars during military operations. Still, they can strengthen our capabilities on the battlefield if there are proper methods to use them. This paper demonstrates the possibility of utilising a ground-based barrage jammer (to jam radars or communications) as an illumination source for passive radar detection, tracking and inverse synthetic aperture radar (ISAR) imaging. It shows the feasibility of using a jammer for cooperative (friendly jammer) and non-cooperative (hostile jammer) operations. The results come from the European Defence Agency project no B 1516 IAP2 GP entitled jammer-based passive radar. This article will present some of the outcomes of field trials conducted in June 2022 in Poland within the international electronic warfare exercises. Designed jammer demonstrators, with a maximum bandwidth of 115 MHz, were used as illuminators of opportunity and made it possible to test the operation of passive radars with five signal types (within two bands—L-band and S-band) for military targets (i.e. fighters, transport planes, helicopters). In the first part of the article, detection, ISAR imaging and tracking issues will be addressed. The second part of the article will focus on the results obtained during the project's joint field trials.

Automation and self-sufficiency in the complex environment of modern electronic warfare (EW) are critical and necessary issues in electronic intelligence and support systems to detect real-time and accurate threat radars. The task of these systems is to search, discover, analyse, and identify the parameters of radar signals. However, recognition pulse repetition interval (PRI) modulation is challenging in natural environments due to destructive factors, including missing pulses (MP), spurious pulses (SP), and large outliers (LO) (caused by antenna scanning), which lead to noisy sequences of PRI variation patterns. The current article examines the effects of destructive factors on recognising PRI modulation in radar signals using deep convolutional neural networks (DCNNs). The article uses simulations based on the actual environment to generate data and consider destructive factors with different percentages. The number of images obtained by applying the sum of destructive factors for each range of destructive factors (with different percentages) considered is 30,000. It is common for six types of modulation. Then, the DCNN models, including VGG16, ResNet50V2, InceptionV3, Xception, and MobileNetV2, are trained using the transfer learning method. The simulation results show that the accuracy of training and testing the models decreases significantly with the increase in the percentage of destructive factors. Also, the effects of the model type on the performance of the models have been investigated, and the results have shown that some models are more resistant to destruction and retain more accuracy. Finally, this analysis shows that to improve the performance of deep neural network (DNN) techniques in the face of changes caused by destructive factors, it is necessary to pay attention to these factors and apply appropriate strategies.