# Classification of Intra-Pulse Modulation of Radar Signals by Feature Fusion Based Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2205.09834 · HTML: https://mlsanity.com/paper/2205.09834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)

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

- [AIMC-Spec: A Benchmark Dataset for Automatic Intrapulse Modulation Classification under Variable Noise Conditions](https://mlsanity.com/paper/2601.08265/) (2026)

## Abstract (arXiv)

Detection and classification of radars based on pulses they transmit is an important application in electronic warfare systems. In this work, we propose a novel deep-learning based technique that automatically recognizes intra-pulse modulation types of radar signals. Re-assigned spectrogram of measured radar signal and detected outliers of its instantaneous phases filtered by a special function are used for training multiple convolutional neural networks. Automatically extracted features from the networks are fused to distinguish frequency and phase modulated signals. Simulation results show that the proposed FF-CNN (Feature Fusion based Convolutional Neural Network) technique outperforms the current state-of-the-art alternatives and is easily scalable among broad range of modulation types.