Anthony Torre, Abigael Taylor, Dominique Poullin, Thierry Chonavel
{"title":"Bio-Inspired Waveform and Processing for Radar Applications","authors":"Anthony Torre, Abigael Taylor, Dominique Poullin, Thierry Chonavel","doi":"10.1049/rsn2.70156","DOIUrl":"https://doi.org/10.1049/rsn2.70156","url":null,"abstract":"<p>The radar technology is continuously facing new challenges, such as an increasingly congested electromagnetic environment or the need for constant improvement in processing capacities. In response, waveform diversity has become a major topic of study for the radar community, providing a wide range of solutions to address the various issues encountered by radar systems. From a biological perspective, it may be of interest to follow the example of bats as their capacities in the domain of sonar processing could be a source of inspiration for resolving radar problems while simultaneously increasing the diversity of available waveforms. Therefore, this paper introduces a bioinspired waveform derived from the literature, with a comparison to the more standard chirp and the hyperbolic frequency modulation (HFM) waveforms. Its performance are discussed in conjunction with the calculations of the Cramér-Rao lower bounds (CRLB) and a classical radar processing analysis. Subsequently, a Doppler estimation scheme for fast-moving targets is described based on the proposed waveform.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"CRAVEN: A Camera–Radar Attention-Based Voxel Encoding Network for 3D Object Detection","authors":"Dong Wang, Junsheng Huang, Zhe Zhang","doi":"10.1049/rsn2.70166","DOIUrl":"https://doi.org/10.1049/rsn2.70166","url":null,"abstract":"<p>The development of intelligent vehicle perception systems has raised increasingly stringent requirements on the representation quality of 4D automotive millimetre-wave radar point clouds (RPC). However, the extremely sparse and irregular nature of RPC leads to insufficient structural cues for reliable 3D object perception, especially when conventional voxel encoders rely on heuristic aggregation (e.g., max-pooling), which limits feature expressiveness. In this paper, an attention-based radar pillar representation and BEV fusion framework for 3D object detection is proposed. Firstly, a multiscale aggregation (MSA) module is designed to aggregate local radar points under multiple receptive-field sizes, enabling robust local geometry modelling from sparse RPC. Secondly, a learnable attentive voxel encoding (LAVE) module is proposed to construct expressive voxel representations. In this module, a set of learnable latent vectors interact with neighbourhood point features via cross-attention to adaptively encode voxel-level features, whereas self-attention is further applied across voxels in BEV space to capture intervoxel contextual dependencies and enhance global structural reasoning. Lastly, an adaptive gated BEV fusion (AGBF) module is designed to fuse radar and camera BEV features with spatially varying modality weights, exploiting cross-modal complementarity whilst suppressing unreliable cues. Experiments conducted on the view-of-delft (VoD) dataset demonstrate the effectiveness of the proposed radar modelling and fusion strategy, yielding consistent improvements over representative baselines.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70166","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoxuan Xu, Haixuan Fu, Zhenyang Wu, Yuhao Xiao, Hong Li
{"title":"GNSS Spoofing Detection Based on Ionospheric Model Deviation Monitoring","authors":"Xiaoxuan Xu, Haixuan Fu, Zhenyang Wu, Yuhao Xiao, Hong Li","doi":"10.1049/rsn2.70147","DOIUrl":"https://doi.org/10.1049/rsn2.70147","url":null,"abstract":"<p>Spoofing attacks on global navigation satellite system (GNSS) are posing significant safety risks to receivers. However, the widely used dual-frequency (DF) receivers offer a new antispoofing inspiration against DF spoofing attacks. DF receivers can estimate ionospheric total electron content (TEC) using both the DF TEC measurement model and the empirical model. Because of the different accuracy of these two models, the deviation of TEC (DTEC) consistency between DF measurements and empirical model estimations in authentic scenarios is not zero but is predictable through ionospheric annual periodicity. However, DF spoofers typically struggle to accurately counterfeit the authentic TEC value, which can disrupt the consistency between DTEC measurements and predictions. Based on this gap, we propose a spoofing detection and recognition method named TEC model deviation monitoring (TEC-MDM) method by checking DTEC consistency between measurements and predictions. In spoofing scenarios, on one hand, simulator-based spoofers typically use empirical models to simulate ionospheric states, which is not accurate enough to maintain DTEC consistency. On the other hand, even if spoofers can simulate ionospheric states accurately, receivers may have prior authentic position information or use both authentic and spoofing signals for positioning because the spoofing signals are typically power-limited for covertness and hard to suppress authentic signals, which causes position estimations to deviate from false positions preset by the spoofer and disrupt the DTEC consistency due to ionospheric spatial variation. Simulation experiments are conducted to theoretically and numerically analyse the influencing factors of the proposed TEC-MDM method. Field experiments verify that the TEC-MDM method can effectively detect DF spoofing signals in full-channel spoofing scenarios (FSS) and recognise spoofing signals in partial-channel spoofing scenarios (PSS). Furthermore, if the receiver has prior authentic position information, spoofing recognition can also be achieved in FSS.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70147","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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