# A Novel Joint Angle-Range-Velocity Estimation Method for MIMO-OFDM ISAC Systems

arXiv: https://arxiv.org/abs/2308.03387 · HTML: https://mlsanity.com/paper/2308.03387/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2023.

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

- [PLAIN: Scalable Estimation Architecture for Integrated Sensing and Communication](https://mlsanity.com/paper/2503.21242/) (2025)
- [Deep Learning-Driven Friendly Jamming for Secure Multicarrier ISAC Under Channel Uncertainty](https://mlsanity.com/paper/2603.05062/) (2026)
- [Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networks](https://mlsanity.com/paper/2608.03319/) (2026)

## Abstract (arXiv)

Integrated sensing and communication (ISAC) is emerging as a key technique for next-generation wireless systems. In order to expedite the practical implementation of ISAC in pervasive mobile networks, it is crucial to have widely deployed base stations with radar sensing capabilities. Thus, the utilization of standardized multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) hardware architectures and waveforms is pivotal for realizing seamless integration of effective communication and sensing functionalities. In this paper, we introduce a novel joint angle-range-velocity estimation algorithm for MIMO-OFDM ISAC systems. This approach exclusively depends on the format of conventional MIMO-OFDM waveforms that are widely adopted in wireless communications. Specifically, the angle-range-velocity information of potential targets is jointly extracted by utilizing all the received echo signals within a coherent processing interval (CPI). The proposed joint estimation algorithm can achieve larger signal-to-noise-ratio (SNR) processing gains and higher resolution by fully exploiting the echo signals and jointly estimating the angle-range-velocity information. A theoretical analysis for maximum unambiguous range, resolution, and SNR processing gains is provided to verify the advantages of the proposed joint estimation algorithm. Finally, the results of extensive numerical experiments are presented to demonstrate that the proposed joint estimation approach can achieve significantly lower root-mean-square-error (RMSE) performance for angle/range/velocity estimation for both single- and multi-target scenarios.