Iet Radar Sonar and Navigation最新文献

With the advent of constellations of SAR satellites, and the possibility of swarms of SAR UAV's, there is increased interest in multistatic SAR image formation. This may provide advantages including allowing three-dimensional image formation free of clutter overlay; the coherent combination of bistatic SAR geometries for improved image resolution; and the collection of additional scattering information, including polarimetric. The polarimetric collection may provide useful target information, such as its orientation, polarisability, or number of interactions with the radar signal; distributed receivers would be more likely to capture any bright specular responses from targets in the scene, making target outlines distinct. Highlight results from multistatic polarimetric SAR experiments at the Cranfield University GBSAR laboratory are presented, illustrating the utility of the approach for fully sampled 3D SAR image formation, and for sparse aperture SAR 3D point-cloud generation with a newly developed volumetric multistatic interferometry algorithm.

Robust localisation techniques that utilise distance observations to determine the location are focused upon. In urban environments with limited visibility and high population density, the presence of non-line-of-sight signals can introduce a positive measurement bias, negatively affecting the accuracy of estimation. To resolve this problem caused by multipath effects, robust localisation techniques have been explored, specifically the skipped filter weighted least squares (WLS) method for localisation. However, the squared estimation bias of the transformed distance estimate of the existing skipped filter WLS method is high in the low signal-to-noise ratio condition owing to the second-order noise terms. Therefore, the modified skipped filter WLS methods are proposed to reduce the squared estimation bias of transformed distance estimate. First, the closed-form modified skipped filter WLS method uses the maximum likelihood estimate (MLE) to reduce the squared estimation bias of the transformed distance estimate. In addition, the modified skipped filter WLS method using the online ML and online expectation maximisation (EM) algorithms are introduced whose advantage is that they do not require the number of Gaussian components unlike the existing Gaussian mixture model EM algorithm. The mean square error analysis of proposed closed-form skipped filter WLS and existing skipped filter WLS methods is performed. Furthermore, the localisation accuracy of the proposed techniques is found to outperform that of competing algorithms via simulation results.

Voice identification is being increasingly adopted in various domains, including security infrastructures, intelligent home systems, and personalised digital assistants. Notably, it harbours significant promise in transforming healthcare, especially in electronic health record detecting and speech impairment monitoring such as aphasia. Current strategies such as acoustic models based on deep learning, voice bio-metrics, and spectrogram analysis, have been identified with several drawbacks including vulnerability to altered voices, susceptibility to ambient noise, and the necessity for significant computational power. In response to these issues, the authors introduce a ground-breaking method of voice identification using Ultra-Wideband (UWB) technology. This method capitalises on the micro-Doppler shifts associated with movements of the laryngeal prominence. The distinctive nature of these bio-metric traits related to speech production provides superior resistance against common pitfalls of voice identification. The proposed model leverages the high-resolution characteristics of UWB to register tiny variations in laryngeal movements produced during speech, thus forming a distinct voice profile for each speaker. Through rigorous testing, the proposed system demonstrated significant progress in voice identification, achieving close to 90% accuracy in controlled experimental settings. This breakthrough indicates that UWB-enabled voice identification could have a profound effect on medical applications, providing potential improvements in diagnosing, monitoring, possibly treating speech disorders, and thereby shaping a future of enhanced and secured healthcare services.

The presence and importance of small drones (unmanned aerial vehicles) are increasing. With traditional radar detection the target radar cross section and its statistical properties are needed in order to design the detector and to assess detection performance. The authors make a statistical analysis of the radar cross section of two small fixed-wing drones using measured data from typical flights. Using data from a typical flight instead of measurements or electromagnetic calculations as a function of aspect angle is a convenient method. Work results of the authors in mean values, medians and standard deviations, in analyses of common target probability distributions and in decorrelation times for the radar cross section, are necessary for design and assessment of the detection performance. The authors suggest a new target probability distribution which agrees better with the measured data than the others tested. The authors also note significant differences between the drones.

Urban surveillance of slow-moving small targets such as drones and birds in low to medium airspace using radar presents significant challenges. Detecting, locating and identifying such low observable targets in strong clutter requires both innovation in radar hardware design and optimisation of processing algorithms. To this end, the University of Birmingham (UoB) has set-up a testbed of two L-band staring radars to support performance benchmarking using datasets of target and clutter from realistic urban environment. This testbed is also providing the vehicle to understand how novel radar architectures can enhance radar capabilities. Some of the challenges in installing the radar at the UoB campus are highligted. Detailed benchmarking results are provided from urban monostatic and bistatic field trials that form the basis for performance comparison against future hardware modification. The solution to the challenge of interfacing the radar to the external oscillators is described and stand-alone bench tests with the candidate oscillators are reported. The testbed provides a valuable capability to undertake detailed analysis of performance of Quantum photonic-enabled radar and allows for its comparison with conventional oscillator technology for surveillance of low observable targets in the presence of urban clutter.

Long baseline bistatic radar systems herald enhanced sensitivity and metric accuracy for space objects in geosynchronous orbits and beyond. Radio telescopes are ideal participants in such a system; in particular, they often feature large apertures with low-noise temperatures and have stable, synchronised clocks. Pairing radio telescopes with high-power radars creates new methodologies for Space Domain Awareness. This paper describes long baseline bistatic measurements using the Millstone Hill Radar in the USA, the Tracking and Imaging Radar in Germany, multiple receivers of the enhanced multi-element remotely linked interferometer network array in the United Kingdom, and the Westerbork Synthesis Radio Telescope in the Netherlands. The authors, a Research Task Group formed by the NATO Science and Technology Organisation Sensors and Electronic Technology Panel (SET-293), performed novel bistatic and monostatic radar imaging experiments with real on-orbit tumbling rocket bodies. These experiments on tumbling objects at near-geosynchronous orbits highlight successful demonstrations of advanced bistatic Doppler characterisation across diverse imaging geometries. Specialised Doppler processing on tumbling targets, such as the Doppler superpulse algorithm, enables high-fidelity rotation period estimation and determination of minimum target size.

Accurate bearing-time recordings play a crucial role in hydroacoustic target motion analysis and situation estimation. The target bearing estimated by vector hydrophone in the interference environment suffers from poor display and low signal-to-noise ratio. To solve this problem, a two-stage bearing-only motion analysis method is proposed. Firstly, the bearing peak information and bearing dispersion are extracted for each sampling moment of the bearing-time recording. Custom constraint regions and adaptive parameters are then set based on this information. The order truncate average algorithm is improved to enhance the quality of the low-quality bearing-time recording map. Secondly, a bearing-only target motion model is established based on the bearing-time recording distribution features, and a bearing-time recording trust interval is constructed. Then a modified pseudo-linear estimation algorithm is proposed to solve the target motion situation information, including target abeam time, target abeam bearing, target course, velocity-to-initial-distance ratio, and so forth. Finally, by comparing the solved values of the sea trial data with the automatic identification system calculation results, the enhancement effect of the target bearing-time recording map is significant and the solving error of the target motion elements meets the needs of engineering applications.

A novel two-stage profile maximum likelihood estimator is proposed to estimate the source location and the systematic errors jointly, with the aim of addressing the problem of source localisation using angle-only measurements from a single sensor with unknown sensor altitude and systematic measurement errors. The proposed two-stage profile maximum likelihood estimator algorithm is capable of decoupling the azimuth and elevation angle measurements while transforming the original maximum likelihood optimisation problem into two sub-problems, that is, two-dimensional-projected maximum likelihood estimator and relative altitude maximum likelihood estimator. In terms of the two-dimensional-projected maximum likelihood estimator, an algorithm combining pseudo-linear estimating and Kalman filtering is proposed to generate an initial estimate. Subsequently, a Gauss–Newton iterative method is developed to estimate the two-dimensional-projected target location and the azimuth systematic error jointly. The relative height maximum likelihood estimator is initialised using a pseudo-linear estimator. Next, a Gauss–Newton iterative algorithm is adopted to estimate the elevation systematic error and the relative height. As indicated by the result of the simulation studies, the proposed algorithm exhibits an estimation performance close to the Cramér–Rao lower bound with unknown sensor altitude and systematic measurement errors.

Robust Global Navigation Satellite System (GNSS) factors are introduced into a factor graph optimisation based integrated navigation system to address the challenge of occluded GNSS signals during ship navigation, which leads to increased errors in positioning results. To enhance the robustness of the GNSS tracking loop, a vector tracking method is applied to receiver tracking loop. Then, the Mahalanobis distance was employed to assess the pseudorange residual and identify and reject signals that exhibit anomalies. Specifically, the pseudorange residual is computed as the difference between the predicted pseudorange of the GNSS receiver and the measured pseudorange. Using the historical information in the window, robust GNSS factors were constructed for use in the factor graph. The robust factor graph optimisation method for a shipborne GNSS/Inertial Navigation System integrated navigation system was implemented by constructing robust GNSS factors and Inertial Measurement Unit factors. The experimental results confirm that the positioning accuracy of the proposed method is superior to those of the factor graph optimization and extended Kalman filter.

An important aspect of Space Situational Awareness is to estimate the intent of objects in space. This paper discusses how discriminating features can be obtained from Inverse Synthetic Aperture Radar images of such objects and how these discriminators can be used to recognise the objects or to estimate their intent. If the object is, for example, a satellite of a known type, the scheme proposed is able to recognise it. The ability of the scheme to detect damage to the object is also discussed. The focus is on imagery obtained in the sub-terahertz band (typically 300 GHz) because of the greater imaging capability given by the diffuse scattering which is observed at these frequencies. The paper also discusses the importance of being able to use images obtained by electromagnetic simulation to be able to train the subsystem which recognises features of the objects and describes a practical scheme for creating these simulations for large objects at these very short wavelengths.