# Multi-modal Iterative and Deep Fusion Frameworks for Enhanced Passive DOA Sensing via a Green Massive H2AD MIMO Receiver

arXiv: https://arxiv.org/abs/2411.06927 · HTML: https://mlsanity.com/paper/2411.06927/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 11 Nov 2024.

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

- [Semi-supervised source localization in reverberant environments with deep generative modeling](https://mlsanity.com/paper/2101.10636/) (2021)
- [SDOA-Net: An Efficient Deep Learning-Based DOA Estimation Network for Imperfect Array](https://mlsanity.com/paper/2203.10231/) (2022)
- [Fast Ambiguous DOA Elimination Method of DOA Measurement for Hybrid Massive MIMO Receiver](https://mlsanity.com/paper/2105.06634/) (2021)
- [Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array](https://mlsanity.com/paper/2204.12991/) (2022)
- [A New Heterogeneous Hybrid Massive MIMO Receiver with An Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning](https://mlsanity.com/paper/2308.08100/) (2023)
- [Machine-learning-aided Massive Hybrid Analog and Digital MIMO DOA Estimation for Future Wireless Networks](https://mlsanity.com/paper/2201.04452/) (2022)
- [Learning Deep Direct-Path Relative Transfer Function for Binaural Sound Source Localization](https://mlsanity.com/paper/2202.07841/) (2022)
- [IRS-Based Integrated Location Sensing and Communication for mmWave SIMO Systems](https://mlsanity.com/paper/2208.05479/) (2022)
- [Deep Learning Based DOA Estimation for Hybrid Massive MIMO Receive Array with Overlapped Subarrays](https://mlsanity.com/paper/2209.04806/) (2022)
- [Low-Cost Beamforming and DOA Estimation Based on One-Bit Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2211.07910/) (2022)

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

- [Multi-Branch DNN and CRLB-Ratio-Weight Fusion for Enhanced DOA Sensing via a Massive H2AD MIMO Receiver](https://mlsanity.com/paper/2506.23203/) (2025)
- [DNN-based Methods of Jointly Sensing Number and Directions of Targets via a Green Massive H2AD MIMO Receiver](https://mlsanity.com/paper/2507.22906/) (2025)

## Abstract (arXiv)

Most existing DOA estimation methods assume ideal source incident angles with minimal noise. Moreover, directly using pre-estimated angles to calculate weighted coefficients can lead to performance loss. Thus, a green multi-modal (MM) fusion DOA framework is proposed to realize a more practical, low-cost and high time-efficiency DOA estimation for a H2AD array. Firstly, two more efficient clustering methods, global maximum cossimilarity clustering (GMaxCS) and global minimum distance clustering (GMinD), are presented to infer more precise true solutions from the candidate solution sets. Based on this, an iteration weighted fusion (IWF)-based method is introduced to iteratively update weighted fusion coefficients and the clustering center of the true solution classes by using the estimated values. Particularly, the coarse DOA calculated by fully digital (FD) subarray, serves as the initial cluster center. The above process yields two methods called MM-IWF-GMaxCS and MM-IWF-GMinD. To further provide a higher-accuracy DOA estimation, a fusion network (fusionNet) is proposed to aggregate the inferred two-part true angles and thus generates two effective approaches called MM-fusionNet-GMaxCS and MM-fusionNet-GMinD. The simulation outcomes show the proposed four approaches can achieve the ideal DOA performance and the CRLB. Meanwhile, proposed MM-fusionNet-GMaxCS and MM-fusionNet-GMinD exhibit superior DOA performance compared to MM-IWF-GMaxCS and MM-IWF-GMinD, especially in extremely-low SNR range.