# Echo-Conditioned Denoising Diffusion Probabilistic Models for Multi-Target Tracking in RF Sensing

arXiv: https://arxiv.org/abs/2510.25464 · HTML: https://mlsanity.com/paper/2510.25464/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 12 of 12)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [Generative AI based Secure Wireless Sensing for ISAC Networks](https://mlsanity.com/paper/2408.11398/) (2024)
- [Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective](https://mlsanity.com/paper/2310.01036/) (2023)
- [Fundamental CRB-Rate Tradeoff in Multi-Antenna ISAC Systems with Information Multicasting and Multi-Target Sensing](https://mlsanity.com/paper/2307.11337/) (2023)
- [Performance Analysis and Power Allocation for Massive MIMO ISAC Systems](https://mlsanity.com/paper/2411.10723/) (2024)
- [Mutual Information Based Pilot Design for ISAC](https://mlsanity.com/paper/2306.13003/) (2023)
- [Conditional Generative Adversarial Networks for Channel Estimation in RIS-Assisted ISAC Systems](https://mlsanity.com/paper/2502.17128/) (2025)

## Abstract (arXiv)

In this paper, we consider a dynamic radio frequency sensing system aiming to spatially track multiple targets over time. We develop a conditional denoising diffusion probabilistic model (C-DDPM)-assisted framework that learns the temporal evolution of target parameters by leveraging the noisy echo observations as conditioning features. The proposed framework integrates a variational autoencoder (VAE) for echo compression and utilizes classifier-free guidance to enhance conditional denoising. In each transmission block, VAE encodes the received echo into a latent representation that conditions DDPM to predict future target states, which are then used for codebook beam selection. Simulation results show that the proposed approach outperforms classical signal processing, filtering, and deep learning benchmarks. The C-DDPM-assisted framework achieves significantly lower estimation errors in both angle and distance tracking, demonstrating the potential of generative models for integrated sensing and communications.