Iet Radar Sonar and Navigation最新文献

The expansion of air traffic has led to an increase in mixed secondary surveillance radar (SSR) signal replies, which occur when multiple replies arrive at the receiver antenna simultaneously. These overlapping signals render the messages unrecoverable, leading to a loss of information. Additionally, the existing methods do not effectively address the issue of handling different types of signals with varying structures and characteristics. The authors validate the effectiveness of the disjoint component analysis (DCA) criterion for SSR signals and introduce a new DCA-based algorithm designed to optimise the separation process. Through several simulations, the proposed algorithm demonstrates robust performance across various reception parameters. Additionally, it achieves good results when applied to real-world data, showing its practical applicability and efficiency.

The design of waveforms plays a critical role in integrated sensing and communication (ISAC) systems. An ISAC waveform with a 0dB peak-to-average power ratio (PAPR) is designed by combining a Golay complementary sequence with a constant envelope orthogonal frequency-division multiplexing. By adjusting the phase modulation parameters, this waveform allows for a trade-offs between communication and sensing capabilities. The authors focus on several key performance metrics for the proposed ISAC waveform, notably using mutual information as a holistic performance indicator to assess both sensing and communication effectiveness. Through extensive numerical simulations, the authors demonstrate that the ISAC waveform significantly enhances detection probability compared to traditional phase-modulated waveforms. The findings suggest that this approach is beneficial for designing low PAPR phase-modulated ISAC waveforms, enhancing both the functionality and efficiency of ISAC systems.

Aiming at the problem that the overlapping of multipath signals seriously affects the radar's performance of the elevation angle estimation for low-altitude targets, the authors combine the unitary ESPRIT (UESPRIT) and the multi-input and multi-output (MIMO) radar system and propose an algorithm based on UESPRIT to estimate the direction of arrival for low-altitude targets. Firstly, the virtual matrix after generalised matched filtering of MIMO radar multipath received signals is vectorised. Secondly, for the coherence of direct and reflected wave signals, which cannot be directly processed by the UESPRIT algorithm, the signal preprocessing is performed by spatial smoothing of the sampled data matrices. Finally, the low-altitude target estimation is carried out by using the UESPRIT algorithm. The Cramer–Rao bound (CRB) for arbitrary unbiased estimation of angle estimation is derived. The relationship between the estimation performance of the algorithm and the signal-to-noise ratio, the number of snapshots and the number of elements is analysed by simulation and compared with CRB. The simulation results show that the algorithm can still effectively estimate the elevation angle of low-altitude targets under the mutual weakening of direct and multipath reflection signals, and has better performance for low-altitude targets than generalised MUSIC.

The authors consider a quantum radar which operates on the quantum illumination principle. The authors’ attention is focused on its function of target detection in a noisy environment. The role of the optical parametric amplifier (OPA) in detection is first examined by the authors, and a dual-OPA design for more flexible combination of optimised gains is proposed, resulting in a detector substantially improved in its performance from the normally used 1-OPA design. Then, the use of the entanglement information in the covariance matrix (CM) between the returned signal and idler beams for detection is considered, and a technique to extract such information is proposed. By employing some statistical relationships between positive definite matrices, the authors come up with a new target detection method. Numerical experiments confirm the superior detection performance of the CM detectors compared to that of the OPA detectors.

The validation of seafloor scattering models used for seabed characterisation requires quantifying the contributions from the sediment interface and volume to the total acoustic returns. At low-frequencies, direct measurements of sediment volume scattering have rarely been made, due to the bias in interface roughness scattering caused by large beamwidths of low-frequency sonars. Endfire Synthetic Aperture Sonar (EF-SAS) can achieve narrower beamwidths by forming a vertically oriented synthetic array as a transmitter and/or receiver and moving it through the water column. The narrower beamwidths achieved by EF-SAS allow for more accurate measurements of volume scattering by reducing interface scattering bias in acoustic returns. The application of EF-SAS for sediment characterisation is explored for the first time. The authors demonstrate that EF-SAS can be used to construct the angular response curve for both interface and volume scattering as well as to estimate the attenuation and reflection coefficients, which can be inverted for grain size.

The low frequency line spectrum noise radiated by ships has strong stability and is difficult to eliminate, which is the key information required for passive signal detection. A vector coherent frequency-domain batch adaptive line enhancement method is proposed to address the issue of insufficient detection capability of traditional scalar adaptive line enhancement (ALE) algorithms for ship characteristic line spectra in complex deep-sea environments. This method not only introduces the idea of frequency-domain batch processing, but also uses synchronously collected sound pressure and particle velocity as dual input, fully utilising the coherence characteristics between vector channels to output high gain line spectrum signals and improve computational efficiency. In simulation and sea trial data validation, compared with the time-domain vector coherent adaptive line enhancement algorithm, this method has shorter time consumption, higher efficiency, and can improve the detection ability of line spectrum signals under low signal-to-noise ratio conditions. The bearing estimation results output by this algorithm is also more accurate.

Unmanned Aerial Vehicles (UAVs) are widely used in both military and civilian sectors due to their maneuverability and versatility. However, UAVs rely on the Global Navigation Satellite System (GNSS) for real-time accurate navigation and are therefore vulnerable to attacks, particularly spoofing attacks, in GNSS-challenging environments. Furthermore, UAV payloads are generally limited to carrying only a single antenna, significantly restricting the spatial Degrees of Freedom (DoFs) available. A new scheme is presented to address the challenges posed by multiple spoofing sources for UAV GNSS navigation. Unlike conventional multi-antenna techniques, our approach extends centralised multi-antenna Direction of Arrival (DOA) estimation to distributed scenarios using UAV collaboration techniques. The multi-source DOA estimation of GNSS spoofing is achieved using the space-time DOA matrix (ST-DOAMatrix) technique, which enhances system resilience and spatial DoFs. Simulation results validate the effectiveness of the proposed method and demonstrate the feasibility and potential of the technique in ensuring the proper and safe operation of UAVs using GNSS navigation.

A conditional generative adversarial network (CGAN) framework is proposed to address the issue of incomplete circular synthetic aperture radar (CSAR) azimuthal information due to motion errors. Specifically, the authors propose a novel CGAN architecture that can control the azimuth angle for arbitrary angle generation, capable of complementing missing CSAR sub-aperture information. The network incorporates angular labels for various scenarios and integrates a dynamic region-aware convolution (DRconv) module. Additionally, to counteract the common challenge of mode collapse in GAN training, a mode seeking regularisation technique is innovativrly introduced into the authors’ loss function. The efficacy of the proposed network is rigorously tested using both the MSTAR dataset and an X-band SAR dataset. The results demonstrate that the authors’ network can generate high-fidelity SAR images with controllable azimuths, closely resembling authentic images. Furthermore, the proposed method excels in complementing missing CSAR sub-aperture information, effectively supplying the lost angular information due to motion errors. A new technical approach for SAR image generation is not only offered but it also has the potential to significantly expand SAR datasets. This advancement is expected to enhance the quality and utility of SAR imagery in applications such as surveillance, reconnaissance, and environmental monitoring.

The classification and recognition of underwater targets by an active sonar system remain challenging and complex. Traditional methods have limited classification performance in time and spatially varying ocean channels. An active sonar target recognition method is proposed based on multi-domain transformations and an attention-based fusion network. Initially, the active target echo undergoes time-frequency analysis, auditory signal processing, and matched filtering to represent target attributes in joint spatial-time-frequency domains. Subsequently, multiple attention-based fusion models fuse the multi-domain transformations either early or late in the processing stages. An attention module further enhances significant feature channels through adaptive weight assignment. Experiment results demonstrate that the recognition accuracy of active sonar echoes using multi-domain transformations improves significantly compared to that of single-domain methods, with an increase of up to 10.5%. The incorporation of multiple transformation domains provides complementary information about the target, thereby enhancing the network's representation ability, especially with limited data samples. Furthermore, the findings indicate that feature fusion of multiple transformations in a high-level feature space yields more informative and effective results for active sonar echoes compared to low-level feature spaces.

Leveraging the advanced functionalities of modern radio frequency (RF) modeling and simulation tools, specifically designed for adaptive radar processing applications, this paper presents a data-driven approach to improve accuracy in radar target localization post adaptive radar detection. To this end, we generate a large number of radar returns by randomly placing targets of variable strengths in a predefined area, using RFView^®, a high-fidelity, site-specific, RF modeling & simulation tool. We produce heatmap tensors from the radar returns, in range, azimuth [and Doppler], of the normalized adaptive matched filter (NAMF) test statistic. We then train a regression convolutional neural network (CNN) to estimate target locations from these heatmap tensors, and we compare the target localization accuracy of this approach with that of peak-finding and local search methods. This empirical study shows that our regression CNN achieves a considerable improvement in target location estimation accuracy. The regression CNN offers significant gains and reasonable accuracy even at signal-to-clutter-plus-noise ratio (SCNR) regimes that are close to the breakdown threshold SCNR of the NAMF. We also study the robustness of our trained CNN to mismatches in the radar data, where the CNN is tested on heatmap tensors collected from areas that it was not trained on. We show that our CNN can be made robust to mismatches in the radar data through few-shot learning, using a relatively small number of new training samples.