pith. sign in

arxiv: 2604.12556 · v1 · submitted 2026-04-14 · 📡 eess.SP

Respiration Monitoring of Multiple People using Multi-site FMCW SISO Radar Systems

Lang Qin , Mandong Zhang , Wenting Song , Zhiqiang Huang , Xiaoguang Liu This is my paper

Pith reviewed 2026-05-10 15:36 UTC · model grok-4.3

classification 📡 eess.SP
keywords respiration monitoringmulti-site SISO radarFMCWnon-coherent multilaterationghost target suppressionvital signscontactless monitoring
0
0 comments X p. Extension

The pith

Non-coherent multi-site SISO FMCW radars monitor respiration of multiple closely spaced subjects without hardware synchronization by using cross-correlation of breathing rhythms to suppress ghosts.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a radar system that places several simple single-input single-output frequency-modulated continuous-wave radars at separate locations to track breathing of more than one person at a time. These radars run independently with no shared cables or timing references. Real subject positions are identified by correlating the breathing signals from each radar site, which filters out false ghost locations created by the lack of synchronization. Experiments confirm separation of two people less than 20 cm apart and breathing-rate estimates within 0.7 beats per minute of attached sensors.

Core claim

Non-coherent multi-site SISO radar systems equipped with physiological-feature-assisted suppression can resolve ghost target ambiguity in multilateration by exploiting the statistical independence of individual respiratory rhythms through cross-correlation analysis, enabling accurate multi-person contactless respiration monitoring that surpasses the angular resolution of compact MIMO arrays while eliminating synchronization hardware.

What carries the argument

The physiological-feature-assisted suppression technique, which applies cross-correlation analysis across radar sites to the extracted respiratory rhythm signals in order to distinguish true target locations from ghosts produced by non-coherent multilateration.

If this is right

  • Two subjects spaced less than 20 cm apart can be resolved, exceeding the limits of single-site MIMO angular resolution.
  • Respiration rate estimates reach 0.7 bpm RMSE against contact-based ground truth.
  • No physical synchronization cables or common reference clocks are required.
  • The approach supports continuous monitoring of co-sleeping individuals.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same independence-based correlation could extend to three or more subjects provided their rhythms stay distinguishable.
  • The method may apply to other multi-target radar tasks where physiological signals offer a natural distinguishing feature.
  • Home health systems could incorporate this low-cost non-coherent layout for simultaneous vital-sign tracking of family members.

Load-bearing premise

The breathing rhythms of different people remain statistically independent so that cross-correlation can reliably separate real targets from ghosts.

What would settle it

An experiment in which two subjects breathe in exact synchrony at the same rate and phase, after which ghost targets are checked to see whether cross-correlation still suppresses them.

Figures

Figures reproduced from arXiv: 2604.12556 by Lang Qin, Mandong Zhang, Wenting Song, Xiaoguang Liu, Zhiqiang Huang.

Figure 1
Figure 1. Figure 1: Sleep monitoring scenarios for long-term home vital signs detection. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the ghost target problem in multi-site multilateration. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of multi-person thoracoabdominal positioning results [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Simulation results. (a) Geometric configuration of real targets and ghost targets. (b) Extracted respiratory signals from the two radars. (c) Cross [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Experimental results. (a) Photograph of the experimental setup. (b) Normalized respiratory rate spectra for different sleeping postures. [PITH_FULL_IMAGE:figures/full_fig_p003_5.png] view at source ↗
read the original abstract

Continuous contactless respiration monitoring of co-sleeping subjects faces a dilemma: conventional single-site multiple-input multiple-output (MIMO) radars struggle with limited angular resolution for closely spaced individuals, while distributed radar networks typically require complex hardware synchronization. To address these limitations, this paper proposes non-coherent multi-site single-input-single-output (SISO) radar systems that completely eliminate the need for physical synchronization cables or common reference clocks. The fundamental challenge of ghost target ambiguity in such non-coherent multilateration is resolved through a novel physiological-feature-assisted suppression technique. By exploiting the inherent statistical independence of individual respiratory rhythms, true target locations are robustly distinguished from ghosts via cross-correlation analysis. Experimental validation demonstrates that the proposed system can accurately resolve two subjects spaced less than 20 cm apart, surpassing the resolution limits of traditional compact MIMO arrays, while achieving a respiration rate estimation accuracy of 0.7 bpm root mean square error (RMSE) compared to contact-based ground truth.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript proposes non-coherent multi-site SISO FMCW radar systems for contactless respiration monitoring of co-sleeping subjects. It eliminates hardware synchronization by using multilateration combined with a physiological-feature-assisted ghost suppression technique that applies cross-correlation to demodulated respiration signals, exploiting assumed statistical independence of breathing patterns to distinguish true targets from ghosts. Experimental validation claims successful resolution of two subjects spaced less than 20 cm apart with 0.7 bpm RMSE respiration rate accuracy versus contact ground truth.

Significance. If validated, the approach offers a hardware-simplified alternative to MIMO or synchronized distributed radars for multi-person vital signs monitoring, potentially enabling practical deployment in sleep studies. The sub-20 cm resolution claim and use of physiological features for non-coherent processing represent a notable technical contribution, though the experimental evidence requires strengthening for robustness.

major comments (2)
  1. [Proposed physiological-feature-assisted suppression technique] The ghost suppression method (described in the proposed technique section) relies on cross-correlation of respiration signals under the assumption of statistical independence of individual respiratory rhythms. This is load-bearing for the <20 cm resolution claim, yet no analysis, simulations, or experiments address cases of breathing rate differences <1 bpm or phase-locked patterns, where correlation peaks at ghost locations could become comparable and reintroduce ambiguity.
  2. [Experimental validation and results] The reported performance (0.7 bpm RMSE and <20 cm separation in the experimental validation) lacks specification of the number of trials, subject breathing pattern diversity, data exclusion rules, or how error bars/RMSE are computed. These omissions make the central performance numbers difficult to interpret or reproduce.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thorough review and constructive feedback on our manuscript. We address each of the major comments in detail below and have made revisions to improve the clarity and robustness of the presented work.

read point-by-point responses

  1. Referee: [Proposed physiological-feature-assisted suppression technique] The ghost suppression method (described in the proposed technique section) relies on cross-correlation of respiration signals under the assumption of statistical independence of individual respiratory rhythms. This is load-bearing for the <20 cm resolution claim, yet no analysis, simulations, or experiments address cases of breathing rate differences <1 bpm or phase-locked patterns, where correlation peaks at ghost locations could become comparable and reintroduce ambiguity.

    Authors: We appreciate the referee pointing out this critical assumption in our ghost suppression technique. The method exploits the statistical independence of breathing patterns, which is generally valid for co-sleeping subjects with natural, unsynchronized respiration. To address the concern for edge cases, we will include in the revised manuscript additional simulation results analyzing the cross-correlation peak strength as a function of breathing rate difference, specifically for differences below 1 bpm (down to 0.2 bpm). These simulations will demonstrate the conditions under which suppression remains reliable. Regarding phase-locked patterns, we acknowledge this as a potential limitation and will add a discussion section noting that in such rare cases, supplementary methods like amplitude-based filtering could be considered. This revision will provide a more complete characterization of the technique's applicability. revision: yes

  2. Referee: [Experimental validation and results] The reported performance (0.7 bpm RMSE and <20 cm separation in the experimental validation) lacks specification of the number of trials, subject breathing pattern diversity, data exclusion rules, or how error bars/RMSE are computed. These omissions make the central performance numbers difficult to interpret or reproduce.

    Authors: We agree that the experimental section would benefit from greater detail to ensure reproducibility. In the revised version, we will expand the 'Experimental Validation' section to specify the number of independent trials performed, the range of subject breathing patterns tested (including variations in rate and depth), the criteria used for data exclusion (such as segments affected by body movement), and the exact procedure for computing the RMSE (including how it is averaged across subjects and trials). We will also clarify the presentation of error bars in the results figures. These additions will allow readers to better interpret the reported 0.7 bpm RMSE and the sub-20 cm resolution capability. revision: yes

Circularity Check

0 steps flagged

No circularity: experimental performance metrics are independent of internal fits or self-referential definitions

full rationale

The paper's core contribution is a non-coherent multi-site SISO radar architecture whose ghost suppression relies on cross-correlation of demodulated respiration waveforms, justified by the external assumption of statistical independence between subjects' breathing patterns. This assumption is not derived from the paper's own equations or outputs; it is an input premise tested via physical experiments. The reported 0.7 bpm RMSE and sub-20 cm resolution are measured against contact-based ground truth, not generated by fitting parameters to the same dataset and then re-presenting the fit as a prediction. No self-citations are invoked to establish uniqueness theorems or ansatzes that would close a loop. The derivation chain therefore remains self-contained against external benchmarks rather than reducing to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that breathing rhythms are statistically independent across subjects; no free parameters or new entities are explicitly introduced in the abstract.

axioms (1)
  • domain assumption Individual respiratory rhythms are statistically independent
    Invoked to enable cross-correlation to separate true targets from ghosts in non-coherent multilateration.

pith-pipeline@v0.9.0 · 5475 in / 1191 out tokens · 49567 ms · 2026-05-10T15:36:13.235204+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

11 extracted references · 11 canonical work pages

  1. [1]

    Microwave sensing and sleep: Noncontact sleep-monitoring technology with microwave biomedical radar,

    H. Hong, L. Zhang, H. Zhao, H. Chu, C. Gu, M. Brown, X. Zhu, and C. Li, “Microwave sensing and sleep: Noncontact sleep-monitoring technology with microwave biomedical radar,”IEEE J. Microw., vol. 20, no. 8, pp. 18–29, Jul. 2019

    work page 2019

  2. [2]

    A robust and accurate fmcw mimo radar vital sign monitoring framework with 4-d cardiac beamformer and heart-rate trace carving technique,

    Y . Li, C. Gu, and J. Mao, “A robust and accurate fmcw mimo radar vital sign monitoring framework with 4-d cardiac beamformer and heart-rate trace carving technique,”IEEE Trans. Microw. Theory Techn., vol. 72, no. 10, pp. 6094–6106, Apr. 2024

    work page 2024

  3. [3]

    Continuous respiratory rate estimation approach based on phase features using array radar in home sleeping monitoring,

    Y . Zheng, Z. Yang, B. Luo, P. Chu, and J. Zhou, “Continuous respiratory rate estimation approach based on phase features using array radar in home sleeping monitoring,”IEEE Sensors J., vol. 24, no. 12, pp. 19 352– 19 363, Dec. 2024

    work page 2024

  4. [4]

    Vital sign detection in any orientation using a distributed radar network via modified independent component analysis,

    W. Ren, F. Qi, F. Foroughian, T. Kvelashvili, Q. Liu, O. Kilic, T. Long, and A. E. Fathy, “Vital sign detection in any orientation using a distributed radar network via modified independent component analysis,” IEEE Trans. Microw. Theory Techn., vol. 69, no. 11, pp. 4774–4790, Jul. 2021

    work page 2021

  5. [5]

    Distributed mimo cw radar for locating multiple people and detecting their vital signs,

    P.-H. Juan, C.-Y . Chueh, and F.-K. Wang, “Distributed mimo cw radar for locating multiple people and detecting their vital signs,”IEEE Trans. Microwave Theory Techn., vol. 71, no. 3, pp. 1312–1325, Nov. 2023

    work page 2023

  6. [6]

    Large-scale body movement cancellation in fmcw radar respiration detection,

    Z. Liu, Y . Xiong, W. Tian, Y . Gou, and Z. Peng, “Large-scale body movement cancellation in fmcw radar respiration detection,”IEEE Trans. Microw. Theory Techn., vol. 73, no. 5, pp. 2992–3003, Nov. 2024

    work page 2024

  7. [7]

    Multitarget vital signs measurement with chest motion imaging based on mimo radar,

    C. Feng, X. Jiang, M.-G. Jeong, H. Hong, C.-H. Fu, X. Yang, E. Wang, X. Zhu, and X. Liu, “Multitarget vital signs measurement with chest motion imaging based on mimo radar,”IEEE Trans. Microw. Theory Techn., vol. 69, no. 11, pp. 4735–4747, Oct. 2021

    work page 2021

  8. [8]

    Accurate multitarget microwave cardiogram sensing with 4-d digital beamforming technique based on a k-band short-range end-to-end 4t8r sparse mimo fmcw radar system,

    J. Zhang, Y . Li, Y . Cao, Z. Zhang, C. Gu, and J. Mao, “Accurate multitarget microwave cardiogram sensing with 4-d digital beamforming technique based on a k-band short-range end-to-end 4t8r sparse mimo fmcw radar system,”IEEE Trans. Microw. Theory Techn., Oct. 2025

    work page 2025

  9. [9]

    An indoor positioning method using dual FMCW radar systems,

    L. Qin, X. Wu, M. Zhang, W. Liu, and X. Liu, “An indoor positioning method using dual FMCW radar systems,” inProc. IEEE MTT-S Int. Wireless Symp. (IWS), May, 2024, pp. 1–3

    work page 2024

  10. [10]

    Three independent forms of cardio-respiratory coupling: transitions across sleep stages,

    R. P. Bartsch, K. K. Liu, Q. D. Ma, and P. C. Ivanov, “Three independent forms of cardio-respiratory coupling: transitions across sleep stages,” in Computing in Cardiology, Cambridge, USA, Jul. 2014, pp. 781–784

    work page 2014

  11. [11]

    Analyz- ing respiratory effort amplitude for automated sleep stage classification,

    X. Long, J. Foussier, P. Fonseca, R. Haakma, and R. M. Aarts, “Analyz- ing respiratory effort amplitude for automated sleep stage classification,” Biosignal Process. Control, vol. 14, pp. 197–205, Jul. 2014

    work page 2014