Automatic target classification in a low frequency FSR network

This paper presents the evaluation of a low-frequency Forward Scattering Radar (FSR) network for the classification of ground targets. The experimental results of automatic targets classification for different operational frequencies are presented and discussed. The possibility of target recognition is shown for system operating frequencies in the VHF band

Neural network based for automatic vehicle classification in forward scattering radar

The paper is dedicated to the continuation and improvement of the vehicle classification method of SISAR micro-sensors for ground vehicles previously presented in RADAR2004 and RADAR2005 [1–2]. In spite of a number of theoretical research efforts in the application of SISAR for target classification [1–4] , there are only few research concentrate on the classification processing to confirm the feasibility of SISAR's practicality. This paper begins with an overview and summary of the authors' previous research. Then a new research topic in the improvement of the classification performance for various scenarios using Neural Network is proposed. Finally experimental results, conclusions and recommendations are presented

The concept of a forward scattering micro-sensors radar network for situational awareness

The concept of a novel forward scattering micro-radar wireless network for ground targets detection and recognition is presented. The system topology and structure are described first, followed by a summary of the system’s capabilities and applications. Signal processing strategies used for target detection, parameter estimation and automatic target recognition are briefly explained and supported with experimental results

Automatic target detection using wavelet technique in forward scattering radar

This paper describes aspects of ground target detection in forward scattering radar (FSR). The problem of extracting the Doppler signature in different interference environments is addressed. Hilbert transform and wavelet technique have been used to predict the existence of target. The paper begins with a brief description of the system, followed by a more detailed analytical study of predicting the presence of target in FSR. Two sets of practical experimentation have been realised to evaluate the proposed algorithm

COSMOS dataset for co-existence/ interference analysis and simultaneous scene representation by automotive radar and video with GPS/IMU ground truth - Sub-Dataset C

The objectives of the described radar trials were to conduct radar measurements at the background of interference in the 76 – 81 GHz frequency band to: • Estimate the impact of interference in an adaptive cruise control (ACC) and cross-traffic alert (CTA) scenarios. • Analyse radar field of view shadowing due to a close target. • Identify an oncoming vehicle from the received interference which is otherwise blind due to radar field of view (FoV) obstruction. • Estimate the multipath interference in a reflective scenario. Repository Overview: The full dataset collected during the trails is over 2 TB. Depending upon the scenario and data collection duration, the size of raw data captured from INRAS Radarlog varies from 1 GB to 7 GB whereas for INRAS Radarbook, it varies from 1 GB to 4 GB. Therefore, due to extremely large files sizes, only the most suitable representative of the defined use-cases is included in the repository. Moreover, the full post-processed radar imagery is only shown for a few example cases. Additional data may be available on request

77 GHz FMCW Imaging Radar for Low Observable and Small Marine Target Detection in Dynamic Sea Conditions Based on Combined MIMO and DBS

High resolution radar sensing is essential to provide situational awareness to small and medium sized marine platforms. However, detecting small targets on the sea surface is a challenging task for the marine surveillance radars because of the weak echoes and relatively low velocity. While there is a similarity and significant body of research on high resolution radar sensing in automotive environment, the direct translation of such techniques to marine sensing is difficult due to fundamentally dynamic underlaying sea surface. This paper addresses the need of developing novel radar sensing capabilities to image and, potentially, classify small marine targets, such as paddlers, buoys, flotsam and jetsam, or the incoming large waves. Our proposed approach combines Multiple Input, Multiple Output (MIMO) and Doppler Beam Sharpening (DBS) beamforming techniques with the Ordered Statistics – Cell Averaging Constant False Alarm Rate (OSCA-CFAR) for robust target detection, Density Based Spatial Clustering of Applications with Noise (DBSCAN) for clustering, and an adaptive focusing technique. With the developed methodology, multiple small ‘dynamic’ targets within the marine scene have been imaged and detected against substantially suppressed sea background