Iet Radar Sonar and Navigation最新文献

In the existing velocity filtering based track-before-detect (VF-TBD), target existence is generally assumed to be constant, and multiple successive frames in a sliding window are jointly processed. However, when the target existence changes abruptly in a processing batch, the incorporation of noise-only frames may corrupt target energy integration. This results in degraded detection and estimation performances with serious decision delays. A pseudo-spectrum based VF-TBD is proposed to effectively detect and track targets with existence uncertainty. An adaptive integration strategy is developed to realise effective energy accumulation with only informative frames by matching target appearance and disappearance times in addition to the velocity. The change of target existence can be discovered in time, and the decision delay is reduced. An estimation strategy based on the maximum output SNR is presented to supply accurate estimations of not only target motion parameters (including the position and velocity) but also time parameters (including appearance and disappearance times). The target output envelope, SNR gain and receiver operating characteristic curve of targets with uncertain existence are analysed theoretically. Numerical simulations exhibit the validity of the proposed method.

The cover image is based on the Original Article Adaptive pseudo-spectrum based track-before-detect for targets with uncertain existence by Peiyuan Li et al., https://doi.org/10.1049/rsn2.12502.

Side-scan sonar is a lightweight acoustic sensor that is frequently deployed on autonomous underwater vehicles (AUVs) to provide high-resolution seafloor images. However, using side-scan images to perform simultaneous localization and mapping (SLAM) remains a challenge when there is a lack of 3D bathymetric information and discriminant features in the side-scan images. To tackle this, the authors propose a feature-based SLAM framework using side-scan sonar, which is able to automatically detect and robustly match keypoints between paired side-scan images. The authors then use the detected correspondences as constraints to optimise the AUV pose trajectory. The proposed method is evaluated on real data collected by a Hugin AUV, using as a ground truth reference both manually-annotated keypoints and a 3D bathymetry mesh from multibeam echosounder (MBES). Experimental results demonstrate that this approach is able to reduce drifts from the dead-reckoning system. The framework is made publicly available for the benefit of the community.

Radar-based human activity recognition is considered as a competitive solution for the elderly care health monitoring problem, compared to alternative techniques such as cameras and wearable devices. However, raw radar signals are often contaminated with noise, clutter, and other artifacts that significantly impact recognition performance, which highlights the importance of prepossessing techniques that enhance radar data quality and improve classification model accuracy. In this study, two different human activity classification models incorporated with pre-processing techniques have been proposed. The authors introduce wavelet denoising methods into a cyclostationarity-based classification model, resulting in a substantial improvement in classification accuracy. To address the limitations of conventional pre-processing techniques, a deep neural network model called Double Phase Cascaded Denoising and Classification Network (DPDCNet) is proposed, which performs end-to-end signal-level classification and achieves state-of-the-art accuracy. The proposed models significantly reduce false detections and would enable robust activity monitoring for older individuals with radar signals, thereby bringing the system closer to a practical implementation for deployment.

For the integration detection of near space hypersonic weak targets by high pulse repetition frequency (PRF) radar, a novel method named Multi-Hypothesis Fuzzy-Matching Radon transform (MHFM-RT) is proposed for the near space hypersonic target detection and tracking. For remote hypersonic target detection, to avoid range ambiguity, current radars always use a low PRF mode, which limit the number of pulse accumulations. Using the high PRF mode, the fuzzy folding will appear in the target range measurements when target trajectory crosses range fuzzy intervals. Therefore, there is a contradiction between range ambiguity and energy accumulation. The proposed method is used to match the fuzzy measurements, so as to realise the correct integration in the condition of range ambiguity. Firstly, considering the need of range ambiguity resolution, the mode of staggered PRF is used. Secondly, the first frame measurements are periodically extended for multiple-fuzzy hypothesis. Finally, the weak target track is accumulated in MHFM-RT domain, and the signal integration and ambiguity resolution can be realised simultaneously. The proposed method expands the Variable-Diameter-Arc-Helix Radon transform (VDAH-RT) method to fuzzy folding conditions. Compared with the existing methods, for 7-scan measurements non-coherent integration, the detection sensitivity of the proposed method is about 0.5–1 dB higher than that of the IMM hybrid filter algorithm, and about 1 dB higher than that of the RHT-TBD approach, and it needs less storage space and has higher detection probability.

The application and uses of drones in all areas are continuously rising, especially in civilian use cases. This increasing threat requires reliable drone surveillance in urban environments. Radar is the obvious candidate with its ability to detect small objects at range, in all weather conditions. The use of an L-band networked radar for urban sensing of S-UAS targets is explored. Small echoes from S-UAS places a premium on synchronisation, which is the fundamental key for high performance networked radar. The effect of timing errors on the operation of the network radar is investigated theoretically and experimentally, and the processing tools for synchronising data based on the direct signal returns of the transmitter are developed. Also, drone detection using bistatic L-band staring radar is achieved both in simulation and then in real field trials where the SNR and detection performance are computed and analysed. The updated direct signal synchronisation method for bistatic staring radar is shown to provide comparable SNR and positional accuracy for S-UAS targets as the monostatic staring radar.

With the development of electronic warfare, anti-jamming measure becomes more and more complex. There have been certain research results on jamming strategies, but only a few research materials on anti-jamming strategies. It is difficult to simulate the real jamming environment, and there is no appropriate anti-jamming decision-making model for research. Cognitive radar can perceive the environment and receive feedback, which provides the possibility to solve the problem of anti-jamming decision-making. This article regards the anti-jamming measure as a kind of interaction behaviour and establishes the cognitive radar antagonistic environment model and uses the reinforcement learning algorithm to solve the problem of anti-jamming decision-making. Finally, this article verifies the feasibility of applying reinforcement learning theory on making anti-jamming decision in the radar antagonistic environment model. The performance of different reinforcement learning algorithms is compared, and their advantages and disadvantages are discussed.

Unmanned Aerial Vehicles, or drones, pose a significant threat to privacy and security. To understand and assess this threat, classification between different drone models and types is required. One way in which this has been demonstrated experimentally is through this use of micro-Doppler information from radars. Classifiers capable of exploiting differences in micro-Doppler spectra will require large amounts of data but obtaining such data experimentally is expensive and time consuming. The authors present the methodology and results of a drone micro-Doppler simulation framework which uses accurate 3D models of drone components to yield detailed and realistic synthetic micro-Doppler signatures. This is followed by the description of a purpose-built validation radar that has been developed specifically to gather high-fidelity experimental drone micro-Doppler data with which is used to validate the simulation. Detailed comparisons between the experimental and simulated micro-Doppler spectra from three models of drones with differently shaped propellers are given, showing very good agreement. The aim is to introduce the simulation methodology. Validation using single propeller micro-Doppler is provided, although the simulation can be extended to multiple propellers. The simulation framework offers the potential to generate large quantities of realistic drone micro-Doppler signatures for training classification algorithms.

The millimetre wave radar signatures of sea lions collected from three animals in the outdoor seal pool available at the Sea Mammal Research Unit in St Andrews in the Autumn of 2021 is reported. The objective is to study the radar amplitude and Doppler signatures of the animals when their full body or part thereof is above water, which is important for the application of autonomous marine navigation. The data was collected using 24 GHz (K-band) and 77 GHz (W-band) Frequency Modulated Continuous Wave radars with linear polarisation. It has been demonstrated that the sea lions were very clearly detected by the radars with Signal to Noise Ratio greater than 30 dB at a range of 40 m. The calculated modal radar cross section (RCS) of the sea lions in HH polarisation at 24 and 77 GHz vary from −48 to −26 dBsm and −48 to −28 dBsm respectively, corresponding to the different body parts and the amount of exposure to the radar beam. In VV polarisation, the modal RCS value range is from −49 to −26 dBsm and −49 to −22 dBsm respectively. The corresponding maximum RCS and the Cumulative Distribution Function results are also reported.

A modified RELAX algorithm based on iterative coarray-domain beamforming for fast source direction-of-arrival (DOA) estimation with fully augmentable sparse arrays is proposed. The authors exploit the deterministic centralised nature of the noise in the coarray domain and the Hermitian symmetry of the spatial autocorrelation function to efficiently incorporate source number estimation within the iterative framework. In doing so, the proposed algorithm allows low-complexity, fast DOA estimation of more sources than sensors, without resorting to computationally expensive implementations of source number estimation using information theoretic criteria. Three variants of the proposed algorithm are presented, each differing in terms of the specific method employed for source number estimation. Extensive simulations are performed with a minimum redundancy array to compare and contrast the performance of the three variants in terms of their accuracy in estimating the number of sources in the field-of-view of the array. The results demonstrate that the modified RELAX algorithm can provide accurate estimates of the number and directions of sources, especially when the number of uncorrelated sources is equal to or higher than the number of sensors.

The marginal Fisher information (MFI) metric is used to design waveforms for the sake of informationally optimal adaptive-on-transmit radar operation. A framework for MFI waveform design is developed and the Polyphase-Coded FM (PCFM) waveform model is utilised to produce a constant-modulus, spectrally contained signal amenable to transmission with high-power amplifiers. The efficacy of the MFI waveform design and minimum mean square error (MMSE) estimation is experimentally demonstrated and extended into an adaptive and dynamic sensing paradigm. The radar transmit waveform is optimised to maximise the Fisher information with respect to the range profile. Upon observing new information from radar echoes, the iterative MMSE (iMMSE) estimator then minimises the estimation error variance according to prior observations. Sequential information maximisation (via waveform design) and error minimisation (via iMMSE) tends towards the Cramér–Rao lower bound (CRLB) with additional measurements improving radar resolution and accuracy. These concepts maximise the information extracted by a radar operating in a congested spectrum where the available bandwidth is limited.