# Deep Learning-based Estimation for Multitarget Radar Detection

arXiv: https://arxiv.org/abs/2305.05621 · HTML: https://mlsanity.com/paper/2305.05621/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Convolutional Neural Networks at Constrained Time Cost](https://mlsanity.com/paper/1412.1710/) (2014)
- [Data-driven Estimation of Sinusoid Frequencies](https://mlsanity.com/paper/1906.00823/) (2019)
- [Enabling Joint Communication and Radar Sensing in Mobile Networks -- A Survey](https://mlsanity.com/paper/2006.07559/) (2020)
- [On Outage-based Beamforming Design for Dual-Functional Radar-Communication 6G Systems](https://mlsanity.com/paper/2207.04921/) (2022)
- [An Efficient Radar Receiver for OFDM-based Joint Radar and Communication Systems](https://mlsanity.com/paper/2209.03883/) (2022)
- [On Integrated Sensing and Communication Waveforms with Tunable PAPR](https://mlsanity.com/paper/2210.02892/) (2022)

## Built on by (most cited first, 3 of 3)

- [FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks](https://mlsanity.com/paper/2405.11079/) (2024)
- [Adaptive Quantized Planetary Crater Detection System for Autonomous Space Exploration](https://mlsanity.com/paper/2508.18025/) (2025)
- [Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets](https://mlsanity.com/paper/2606.31473/) (2026)

## Abstract (arXiv)

Target detection and recognition is a very challenging task in a wireless environment where a multitude of objects are located, whether to effectively determine their positions or to identify them and predict their moves. In this work, we propose a new method based on a convolutional neural network (CNN) to estimate the range and velocity of moving targets directly from the range-Doppler map of the detected signals. We compare the obtained results to the two dimensional (2D) periodogram, and to the similar state of the art methods, 2DResFreq and VGG-19 network and show that the estimation process performed with our model provides better estimation accuracy of range and velocity index in different signal to noise ratio (SNR) regimes along with a reduced prediction time. Afterwards, we assess the performance of our proposed algorithm using the peak signal to noise ratio (PSNR) which is a relevant metric to analyse the quality of an output image obtained from compression or noise reduction. Compared to the 2D-periodogram, 2DResFreq and VGG-19, we gain 33 dB, 21 dB and 10 dB, respectively, in terms of PSNR when SNR = 30 dB.