# Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approach

arXiv: https://arxiv.org/abs/2402.17987 · HTML: https://mlsanity.com/paper/2402.17987/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 70 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges](https://mlsanity.com/paper/1805.00881/) (2018)
- [A Survey on Security and Privacy Issues of UAVs](https://mlsanity.com/paper/2109.14442/) (2021)
- [Recursive Estimation of User Intent from Noninvasive Electroencephalography using Discriminative Models](https://mlsanity.com/paper/2211.02630/) (2022)
- [Bayesian and Neural Inference on LSTM-based Object Recognition from Tactile and Kinesthetic Information](https://mlsanity.com/paper/2306.06423/) (2023)
- [Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies](https://mlsanity.com/paper/2102.11954/) (2021)
- [Comparative Analysis of Radar Cross Section Based UAV Classification Techniques](https://mlsanity.com/paper/2112.09774/) (2021)
- [Spatio-Temporal-Frequency Graph Attention Convolutional Network for Aircraft Recognition Based on Heterogeneous Radar Network](https://mlsanity.com/paper/2204.07360/) (2022)

## Built on by (most cited first, 1 of 1)

- [Advance and Refinement: The Evolution of UAV Detection and Classification Technologies](https://mlsanity.com/paper/2409.05985/) (2024)

## Abstract (arXiv)

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.