# CFARnet: deep learning for target detection with constant false alarm rate

arXiv: https://arxiv.org/abs/2208.02474 · HTML: https://mlsanity.com/paper/2208.02474/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 57 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 18 of 18)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Deep Learning Techniques for Inverse Problems in Imaging](https://mlsanity.com/paper/2005.06001/) (2020)
- [Generative Moment Matching Networks](https://mlsanity.com/paper/1502.02761/) (2015)
- [On Calibration and Out-of-domain Generalization](https://mlsanity.com/paper/2102.10395/) (2021)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Achieving Equalized Odds by Resampling Sensitive Attributes](https://mlsanity.com/paper/2006.04292/) (2020)
- [Learning to Estimate Without Bias](https://mlsanity.com/paper/2110.12403/) (2021)
- [Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis](https://mlsanity.com/paper/2207.10939/) (2022)
- [Laplace's Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables](https://mlsanity.com/paper/1302.6782/) (2013)
- [SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network](https://mlsanity.com/paper/2106.05490/) (2021)
- [Machine Learning For In-Region Location Verification In Wireless Networks](https://mlsanity.com/paper/1812.07853/) (2018)
- [Stochastic Convergence Rates and Applications of Adaptive Quadrature in Bayesian Inference](https://mlsanity.com/paper/2102.06801/) (2021)
- [Adaptive Radar Detection in Heterogeneous Clutter-dominated Environments](https://mlsanity.com/paper/2108.08011/) (2021)
- [Design of Customized Adaptive Radar Detectors in the CFAR Feature Plane](https://mlsanity.com/paper/2203.12565/) (2022)

## Built on by (most cited first, 4 of 4)

- [Out-of-Distribution Radar Detection in Compound Clutter and Thermal Noise through Variational Autoencoders](https://mlsanity.com/paper/2503.04861/) (2025)
- [Detecting radar targets swarms in range profiles with a partially complex-valued neural network](https://mlsanity.com/paper/2602.09597/) (2026)
- [Unfolding Target Detection with State Space Model](https://mlsanity.com/paper/2410.22774/) (2024)
- [RF-LEGO: Modularized Signal Processing-Deep Learning Co-Design for RF Sensing via Deep Unrolling](https://mlsanity.com/paper/2604.10183/) (2026)

## Abstract (arXiv)

We consider the problem of target detection with a constant false alarm rate (CFAR). This constraint is crucial in many practical applications and is a standard requirement in classical composite hypothesis testing. In settings where classical approaches are computationally expensive or where only data samples are given, machine learning methodologies are advantageous. CFAR is less understood in these settings. To close this gap, we introduce a framework of CFAR constrained detectors. Theoretically, we prove that a CFAR constrained Bayes optimal detector is asymptotically equivalent to the classical generalized likelihood ratio test (GLRT). Practically, we develop a deep learning framework for fitting neural networks that approximate it. Experiments of target detection in different setting demonstrate that the proposed CFARnet allows a flexible tradeoff between CFAR and accuracy.