Iet Radar Sonar and Navigation最新文献

To solve the difficulties in threshold selection and poor performance under low signal-to-noise ratio (SNR) conditions in existing spectrum sensing algorithms, a graph-based spectrum sensing algorithm using nonlinear function regulation was proposed. The idea was to add a specific nonlinear transformation between the normalisation and quantization steps of the existing signal-to-graph converter (SGC). If the autocorrelation function of the observed signal selected as the input fed to SGC, the nonlinear function has the ability to adjust the uniformity of its probability distribution, increasing the probability of the observed signal being transformed into a complete graph under the alternative hypothesis, whereas remaining a noncomplete graph under the null hypothesis. Thus transformed the graph-based spectrum sensing into a complete graph-detection problem. Based on the theory of dispersive ordering, a theoretical analysis of the mechanism by which nonlinear transformations affect graph connectivity was conducted. The simulation results showed that the detection performance of the proposed algorithm was superior to that of existing graph-based spectrum sensing algorithms. When SNR was −7 dB, the detection probability of the proposed algorithm exceeded 95%. Moreover, among the existing graph-based spectrum sensing algorithms, the proposed algorithm exhibited the lowest computational complexity apart from the block range-based method.

The use of WiFi signals for sensing purposes has attracted a lot of interest from both the radar and communications communities and several techniques have been explored. In the attempt of meeting the requirements for small sensor size, compactness, and easy deployment, the authors consider reference-free approaches, namely approaches that do not require a good copy of the transmitted waveform to be available at the radar receiver. To this end, the authors first resort to a passive radar-based processing scheme that only exploits the invariant a priori known initial portion of the physical layer protocol data unit, that is, the PHY Preamble, and its limitations in practical applications is investigated. Specifically, the authors show that, with this approach, an accurate time, phase, and frequency synchronization is essential and a possible strategy is investigated. As an alternative solution the authors consider a forward scatter radar-based approach where only the amplitude modulation of the received signal is exploited to detect the presence of a moving target thus avoiding the need to know the transmitted signal. For the first time, the authors comparatively investigate advantages and drawbacks of the two reference-free approaches and present practical strategies to handle the limitations observed. The results are reported for experimental tests with people and drones using WiFi transmissions in the 2.4 and 5 GHz band.

The High-Dynamic enhanced Loran (eLoran) signal simulator must accurately model the motion parameters and dynamic transmission delays of the carriers in the channel. A method was proposed based on B-Spline interpolation, capable of arbitrary ratio interpolation and multi-channel simulation. This method can regenerate carrier-to-transmitter distances from available discrete motion asynchronous data, it can effectively reproduce more motion details for carrier. The algorithm can be implemented using digital circuits, allowing for real-time simulation on a hardware platform. The circuit structure is divided into integer and fractional calculations, which can satisfy the interpolation of asynchronous data with arbitrary ratios. Compared with existing methods, the approach simulates the motion parameters with satisfactory accuracy.

A signal time-frequency results inversion method is proposed for extracting micro-motion features of rotor targets under low signal-to-noise ratios (SNRs). In the case of low SNRs, the echo's energy of rotor targets is mainly concentrated in the flash's echo component. Conventional micro-motion feature extraction of rotor targets primarily utilises the sinusoidal modulation feature in time-frequency results, whose energy is much lower than the flash. Under low SNRs, the sinusoidal modulation in the echo's time-frequency results will be submerged in the noise, making feature extraction challenging. A deep learning network is used to inverse the time-frequency results containing sinusoidal modulation based on the flash's features in the time-frequency results. Based on the inversion time-frequency results, the GS-IRadon algorithm is used to extract micro-motion features, which can significantly reduce the times of IRadon transformations and improve feature extraction speed and accuracy. Through simulation and analysis, a novel method using a deep learning network like UNet can effectively inverse time-frequency results under low SNRs, providing a new technical approach for micro-motion feature extraction. Time-frequency results inversion is a novelty method used to achieve micro-motion feature extraction of rotor targets.

Two-dimensional direction-of-arrival (DoA) estimation in azimuth and elevation via radar systems equipped with uniform rectangular arrays (URAs) will play an important role in various application areas—most distinctively in future urban air mobility settings with unmanned aerial vehicles. A key factor is the fast and reliable provision of target detections in terms of range and DoA for safe autonomous operation of the vehicle using on-board antenna arrays with compact installation size. The authors present a technique for improving the performance of DoA estimation using compressive sensing in conjunction with multiple-input multiple-output arrays with electronically steered beams in the transmit direction. The simulation study investigates the impact of different design considerations on radar signal processing performance. An optimisation of a radar system using electronic beamsteering in the transmit domain is presented numerically. Based on the architecture of the URAs used, performance and detection accuracy can be improved.

An analysis of the parameter estimation uncertainty for the target location and velocity achievable using a single-transmitter-multiple-receiver multistatic radar system is presented. A framework for establishing measures of multistatic radar parameter uncertainties by expansion of the bistatic radar parameter uncertainty measures is presented for systems containing omnidirectionally radiating nodes. The methodology uses analytical methods based on the Cramér–Rao Lower Bounds applied to scenarios in a two-dimensional physical space with a single target exhibiting Doppler characteristics and a bistatic angle dependent radar cross-section. A set of geometric descriptors is proposed to characterise the system, and parameter uncertainty results are reported as a function of these descriptors. The results indicate that angular separation between the transmitter and the centre of the receiver distribution is of greater importance than the quantity of receivers within the system when low uncertainty estimation capabilities are desired, though a minimum of two receivers must be available. The proportion of receivers within the system which contributed information crucial to obtaining the minimum estimation uncertainty is reported for systems containing different quantities of receivers. It was observed that, as the total number of receivers available increased, the proportion of receivers required to achieve the minimal uncertainty level reduces significantly.

Time-frequency analysis based on Wigner-Ville distribution (WVD) plays a significant role in analysing non-stationary signals, but it is susceptible to interference from cross-terms (CTs) for multi-component signals. To address this issue, a novel WVD enhancement method based on generative adversarial networks (namely WVD-GAN) is proposed, to achieve highly-concentrated time-frequency (TF) representation. Specifically, a deep feature extraction module is designed with multiple residual connections in the generator of WVD-GAN to leverage the latent information encoded in the shallow representations. Meanwhile, a simple and effective attention module is introduced to enhance auto-term features. Moreover, a multi-scale discriminator is proposed based on dilated convolutions to guide the generator to reconstruct high-resolution TF images by discriminating CT. Finally, a comparative analysis is provided to demonstrate the effectiveness and robustness of the proposed method on different simulated and real-life datasets. Extensive experiments demonstrate that the proposed method outperforms several state-of-the-art methods.

The IET RSN journal has always published a significant number of Special Issues. In some cases, these have been based on the theme of a new technology or set of applications. In others, they have presented developments of the best papers from a particular conference. Both approaches have led to coherent, high-quality collections of papers that are easy to locate.

In this rapidly-changing landscape of publications, we have taken the decision to continue and to grow such Special Issues and have appointed Professor Alessio Balleri to oversee this. We welcome proposals for Special Issues from potential Guest Editors but emphasise that papers in Special Issues will receive the same degree of review and scrutiny as regular papers.

Open Access has become an increasingly popular means of handling the publication process. In partnership with Wiley, 27 of IET's hybrid journal programmes successfully transitioned to Gold OA in 2020. There are now 43 OA journals in the IET's portfolio, and the programme is expanding.

The editors of the RSN journal have always striven to maintain a reputation for rapid processing and publication. Statistics for Q1–Q3 of 2023 show that the average time from submission to first decision is 29 days and from acceptance to e-first publication is 25 days. This achievement has depended on rapid responses by our reviewers, and we express our gratitude to the reviewers for their time, expertise and hard work in refereeing papers and maintaining the reputation of the journal, both for quality and for rapid publication. This is an essential part of the publication process, but necessarily the reviewers must remain anonymous. This editorial is one place where we can acknowledge their contribution and record our thanks to them.

In image processing, image segmentation is the process of partitioning a digital image into multiple image segments. Among state-of-the-art methods, Markov random fields can be used to model dependencies between pixels and achieve a segmentation by minimising an associated cost function. Currently, finding the optimal set of segments for a given image modelled as a Markov random fields appears to be NP-hard. The authors aim to take advantage of the exponential scalability of quantum computing to speed up the segmentation of synthetic aperture radar images. For that purpose, the authors propose a hybrid quantum annealing classical optimisation expectation maximisation algorithm to obtain optimal sets of segments. After proposing suitable formulations, the authors discuss the performances and the scalability of authors’ approach on the D-Wave quantum computer. The authors also propose a short study of optimal computation parameters to enlighten the limits and potential of the adiabatic quantum computation to solve large instances of combinatorial optimisation problems.