# Angle of Arrival Estimation with Transformer: A Sparse and Gridless Method with Zero-Shot Capability

arXiv: https://arxiv.org/abs/2408.09362 · HTML: https://mlsanity.com/paper/2408.09362/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 18 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion](https://mlsanity.com/paper/2307.16532/) (2023)
- [ADCNet: Learning from Raw Radar Data via Distillation](https://mlsanity.com/paper/2303.11420/) (2023)
- [Human Detection in Realistic Through-the-Wall Environments using Raw Radar ADC Data and Parametric Neural Networks](https://mlsanity.com/paper/2403.15468/) (2024)

## Built on by (most cited first, 2 of 2)

- [Attention-Based Fusion of IQ and FFT Spectrograms with AoA Features for GNSS Jammer Localization](https://mlsanity.com/paper/2507.14167/) (2025)
- [GNSS Jammer Direction Finding in Dynamic Scenarios Using an Inertial-based Multi-Antenna System](https://mlsanity.com/paper/2512.05128/) (2025)

## Abstract (arXiv)

Automotive Multiple-Input Multiple-Output (MIMO) radars have gained significant traction in Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV) due to their cost-effectiveness, resilience to challenging operating conditions, and extended detection range. To fully leverage the advantages of MIMO radars, it is crucial to develop an Angle of Arrival (AOA) algorithm that delivers high performance with reasonable computational workload. This work introduces AAETR (Angle of Arrival Estimation with TRansformer) for high performance gridless AOA estimation. Comprehensive evaluations across various signal-to-noise ratios (SNRs) and multi-target scenarios demonstrate AAETR's superior performance compared to super resolution AOA algorithms such as Iterative Adaptive Approach (IAA). The proposed architecture features efficient, scalable, sparse and gridless angle-finding capability, overcoming the issues of high computational cost and straddling loss in SNR associated with grid-based IAA. AAETR requires fewer tunable hyper-parameters and is end-to-end trainable in a deep learning radar perception pipeline. When trained on large-scale simulated datasets then evaluated on real dataset, AAETR exhibits remarkable zero-shot sim-to-real transferability and emergent sidelobe suppression capability. This highlights the effectiveness of the proposed approach and its potential as a drop-in module in practical systems.