# Detecting radar targets swarms in range profiles with a partially complex-valued neural network

arXiv: https://arxiv.org/abs/2602.09597 · HTML: https://mlsanity.com/paper/2602.09597/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [Unitary Evolution Recurrent Neural Networks](https://mlsanity.com/paper/1511.06464/) (2015)
- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [CFARnet: deep learning for target detection with constant false alarm rate](https://mlsanity.com/paper/2208.02474/) (2022)
- [Frequency Estimation Using Complex-Valued Shifted Window Transformer](https://mlsanity.com/paper/2309.09352/) (2023)

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

- [Graph neural networks for sampling-invariant embeddings of organized signal sets](https://mlsanity.com/paper/2609.35934/) (2026)

## Abstract (arXiv)

Correctly detecting radar targets is usually challenged by clutter and waveform distortion. An additional difficulty stems from the relative proximity of several targets, the latter being perceived as a single target in the worst case, or influencing each other's detection thresholds. The negative impact of targets proximity notably depends on the range resolution defined by the radar parameters and the adaptive threshold adopted. This paper addresses the matter of targets detection in radar range profiles containing multiple targets with varying proximity and distorted echoes. Inspired by recent contributions in the radar and signal processing literature, this work proposes partially complex-valued neural networks as an adaptive range profile processing. Simulated datasets are generated and experiments are conducted to compare a common pulse compression approach with a simple neural network partially defined by complex-valued parameters. Whereas the pulse compression processes one pulse length at a time, the neural network put forward is a generative architecture going through the entire received signal in one go to generate a complete detection profile.