Iet Radar Sonar and Navigation最新文献

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.

To address the array aperture loss caused by mainstream direction of arrival (DOA) estimation algorithms for coherent signals using matrix interpolation techniques, a non-circular (NC) coherent signal direction-finding method that fully utilises covariance matrix information is proposed. The received data is firstly extended by leveraging its NC properties. Then, eigenvalue decomposition is performed on the covariance matrix to extract the signal subspace, which is mapped to a uniform linear array signal subspace via a mapping matrix. The first row of the covariance matrix corresponding to the signal subspace is employed to construct a Toeplitz matrix, and nuclear norm minimisation method is applied to recover the missing information. Finally, to avoid extra NC phase searching, the reduced-dimension method is applied to obtain the estimation results. Performance analysis and simulation results show that the proposed algorithm achieves improvements in computational complexity, source estimation capability, and estimation accuracy.

Model-free deep reinforcement learning (DRL) is regarded as an effective approach for multi-target cognitive electronic reconnaissance (MCER) missions. However, DRL networks with poor generalisation can significantly reduce mission completion rates when parameters such as reconnaissance area size, target number, and platform speed vary slightly. To address this issue, this paper introduces a novel scene reconstruction method for MCER missions and a mission group adaptive transfer deep reinforcement learning (MTDRL) algorithm. The algorithm enables quick adaptation of reconnaissance strategies for varied mission scenes by transferring strategy templates and compressing multi-target perception states. To validate the method, the authors developed a transfer learning model for unmanned aerial vehicle (UAV) MCER. Three sets of experiments are conducted by varying the reconnaissance area size, the target number, and the platform speed. The results show that the MTDRL algorithm outperforms two commonly used DRL algorithms, with an 18% increase in mission completion rate and a 5.49 h reduction in training time. Furthermore, the mission completion rate of the MTDRL algorithm is much higher than that of a typical non-DRL algorithm. The UAV demonstrates stable hovering and repeat reconnaissance behaviours at the radar detection boundary, ensuring flight safety during missions.

The parametric scattering centre (SC) model of the target has been widely used in the rapid simulation of radar image. Most of the existing SC models are suitable for far-field scattering where the radar cross section (RCS) is independent of the distance from the target to the radar. However, when the electromagnetic scattering of the target is in the near-field region of radiation, the RCS of the target changes dramatically in distance. The existing SC model cannot characterise these important near-field characteristics. In this paper, the near-field model with the dependence description of the SC on the distance is proposed. Firstly, the scattering characteristics of several main geometric structures of the extended target in the near-field radiation region are analysed. Then, on the basis of far-field SC model expressions, the description of the relationship between amplitude and phase of the SC model and distance is added. Finally, the correctness of the near-field SC model is verified by comparing with the accurate full-wave numerical method. In order to further illustrate the practicability of the near-field SC model, an actual aircraft target is used. The results proved that the near-field SC model has the advantages of high accuracy and high computational efficiency.

In the field of automotive radar testing, radar target simulators (RTS) based on fibre delay technology are limited by production processes, typically achieving distance steps of only 1 cm. Meanwhile, simulators utilising digital DRFM (Digital Radio Frequency Memory) technology face similar constraints, with a minimum distance step size of 3 cm, dictated by the ADC sampling rate (corresponding to a sampling rate of 5 GHz). In both types of simulators, abrupt changes in target distance result in phase discontinuities due to the step size being greater than the wavelength, ultimately distorting the target velocity spectrum. This paper proposes a phase compensation method based on instantaneous frequency measurement for a digital DRFM simulator, which ensures the continuity of the phase in the RTS output signal, achieving an ideal simulation of the target velocity spectrum. Additionally, it enables millimetre-level distance stepping.

It is notoriously challenging work to track an unknown number of low-observable manoeuvering targets. In this paper, a sequential Bayesian inference method based on the multiple-model dynamic model and track-before-detect measurement (TBD) model is proposed for tracking low-observable manoeuvering targets using multiple sensors. The multiple-model dynamic model is capable to characterise the dynamic behaviour of manoeuvering targets. The TBD measurement model can completely capture an echo signal without any preprocessing, furtherly handling with low-observable targets. The authors’ proposed method is based on a new multi-sensor statistical model that allows targets to interact and contribute to more than one data cell for the pixeled image TBD approach. Based on the factor graph representing the multi-sensor statistical model, the marginal posterior densities are derived by performing the message passing equations of the proposed belief propagation algorithm for target detection and target state estimation. The simulation results validate that the computational complexity of our proposed multi-sensor BP-TBD algorithm scales in the number of sensor nodes and demonstrate that its performance is superior among the state-of-the-art multi-sensor TBD methods.

As low-cost global navigation satellite systems receiver and antenna become increasingly prevalent for water vapour monitoring, precise millimetre-level characterisation of instrument biases is crucial for maintaining measurement accuracy and reliability. This study examines the impact of phase centre variation (PCV) corrections on zenith wet delay (ZWD) accuracy in precise point positioning (PPP) solutions using low-cost systems. The results show that PCV corrections significantly improve ZWD estimation, reducing bias and RMS by 66% and 31% in ambiguity float PPP, and by 71% and 38% in ambiguity fixed PPP. In addition, integer ambiguity resolution offers no significant improvement in ZWD accuracy without PCV correction. When PCV corrections and ambiguity fixing are applied, low-cost receivers demonstrate ZWD estimates comparable to those of high-precision geodetic receivers, with a deviation of 0.2 mm and an RMS of 2 mm. However, variations in signal tracking and multipath suppression can still influence ambiguity fixing rates, resulting in slightly lower performance for low-cost receivers compared to their high-precision counterparts. The study concludes that deploying low-cost receivers with calibrated antennas offers a cost-effective approach to increasing the spatial resolution of atmospheric water vapour measurements, thereby enhancing weather monitoring and early warning systems, particularly for localised extreme weather events.

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.