Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approach
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914913553743872 |
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| author | Potter, Michael Akcakaya, Murat Necsoiu, Marius Schirner, Gunar Erdogmus, Deniz Imbiriba, Tales |
| author_facet | Potter, Michael Akcakaya, Murat Necsoiu, Marius Schirner, Gunar Erdogmus, Deniz Imbiriba, Tales |
| contents | Radar Automated Target Recognition (RATR) for Unmanned Aerial Vehicles (UAVs) involves transmitting Electromagnetic Waves (EMWs) and performing target type recognition on the received radar echo, crucial for defense and aerospace applications. Previous studies highlighted the advantages of multistatic radar configurations over monostatic ones in RATR. However, fusion methods in multistatic radar configurations often suboptimally combine classification vectors from individual radars probabilistically. To address this, we propose a fully Bayesian RATR framework employing Optimal Bayesian Fusion (OBF) to aggregate classification probability vectors from multiple radars. OBF, based on expected 0-1 loss, updates a Recursive Bayesian Classification (RBC) posterior distribution for target UAV type, conditioned on historical observations across multiple time steps. We evaluate the approach using simulated random walk trajectories for seven drones, correlating target aspect angles to Radar Cross Section (RCS) measurements in an anechoic chamber. Comparing against single radar Automated Target Recognition (ATR) systems and suboptimal fusion methods, our empirical results demonstrate that the OBF method integrated with RBC significantly enhances classification accuracy compared to other fusion methods and single radar configurations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17987 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approach Potter, Michael Akcakaya, Murat Necsoiu, Marius Schirner, Gunar Erdogmus, Deniz Imbiriba, Tales Signal Processing Computer Vision and Pattern Recognition Machine Learning Probability Radar Automated Target Recognition (RATR) for Unmanned Aerial Vehicles (UAVs) involves transmitting Electromagnetic Waves (EMWs) and performing target type recognition on the received radar echo, crucial for defense and aerospace applications. Previous studies highlighted the advantages of multistatic radar configurations over monostatic ones in RATR. However, fusion methods in multistatic radar configurations often suboptimally combine classification vectors from individual radars probabilistically. To address this, we propose a fully Bayesian RATR framework employing Optimal Bayesian Fusion (OBF) to aggregate classification probability vectors from multiple radars. OBF, based on expected 0-1 loss, updates a Recursive Bayesian Classification (RBC) posterior distribution for target UAV type, conditioned on historical observations across multiple time steps. We evaluate the approach using simulated random walk trajectories for seven drones, correlating target aspect angles to Radar Cross Section (RCS) measurements in an anechoic chamber. Comparing against single radar Automated Target Recognition (ATR) systems and suboptimal fusion methods, our empirical results demonstrate that the OBF method integrated with RBC significantly enhances classification accuracy compared to other fusion methods and single radar configurations. |
| title | Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approach |
| topic | Signal Processing Computer Vision and Pattern Recognition Machine Learning Probability |
| url | https://arxiv.org/abs/2402.17987 |