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arxiv: 2604.15688 · v1 · submitted 2026-04-17 · 📡 eess.SP

Multi-site Radar Systems for High-Precision Indoor Positioning and Tracking

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

Pith reviewed 2026-05-10 08:36 UTC · model grok-4.3

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The pith

Multi-site SISO radars with a velocity synthesis-assisted localization algorithm achieve centimeter-level tracking accuracy for humans without MIMO hardware or strict phase synchronization.

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

This work focuses on tracking moving targets such as people inside buildings using several radars, each with one antenna for sending and receiving signals. The key idea is to combine the speed measurements toward each radar to refine position estimates inside range bins through an iterative process. This velocity synthesis creates geometric constraints that help the system stay accurate even when signals are weak, echoes bounce around, or the radars have timing differences. The approach avoids complex multi-antenna setups and tight synchronization, lowering hardware demands. Standardized test paths are defined for fair comparisons, and both computer simulations and real experiments reportedly show centimeter-level accuracy that beats prior techniques for complicated movements.

Core claim

our multi-site radar systems achieve centimeter-level tracking accuracy for human subjects, outperforming existing methods in complex trajectory tracking.

Load-bearing premise

The inherent geometric constraints introduced by velocity synthesis enable the proposed algorithm to remain robust under low signal-to-noise ratio (SNR), severe multipath propagation, and large synchronization latency.

Figures

Figures reproduced from arXiv: 2604.15688 by Lang Qin, Mandong Zhang, Wenting Song, Xiaoguang Liu, Xiaohu Wu, Zhiqiang Huang.

Figure 1
Figure 1. Figure 1: Indoor high-precision target single positioning. (a) Single MIMO radar positioning [24]. (b) Multi-site SISO radar systems for static target positioning. (c) Multi-site SISO radar systems for dynamic target positioning and tracking. by either a phased array configuration or a multiple￾input-multiple-output (MIMO) configuration as shown in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) depicts a coherent solution, where radars share a common physical clock source via cabling. In contrast, trigger synchronization entails only a synchronized start time, which is typically characterized as non-coherent synchronization. Software solutions can only realize trig￾ger, which often use networks. Precision Time Protocol (PTP) [40] and Network Time Protocol (NTP) [41] are rep￾resentative exampl… view at source ↗
Figure 3
Figure 3. Figure 3: Traditional position estimation using EKF method . The two-dimensional CFAR detection is formulated as: T[m, p] = α · Z, Z = 1 Nref N Xref i=1 S[mi , pi ] 2 . (9) For each detected target peak at indices (m1,2, p1,2) in the RDM, the post-detection radial velocity between the target and the two radars r1 and r2 is v1,2 = λp1,2 2LTP RI (10) where λ is radar wavelength, p1,2 is the Doppler bin index of the tw… view at source ↗
Figure 4
Figure 4. Figure 4: Proposed VSA method. against high-dynamic maneuvers. Algorithm 1 presents the steps of the proposed VSA algorithm. The explanation in detail is as follows. a) Spatial Candidate Generation: The search space is first initialized and discretized. The common distance interval B is determined as the intersection of all radar range measurements based on (6), confining the search to the immediate proximity of the… view at source ↗
Figure 5
Figure 5. Figure 5: Monte-Carlo positioning simulations. (a) Positioning distri￾bution. (b) Positioning errors. The positioning performance of the proposed algorithm (Section III-A) is compared against the conventional trilateration method (Section II-B). The robustness of the proposed algorithm is validated through 50 Monte Carlo simulations, with results shown in [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Monte-Carlo tracking simulations. (a) SNR. (b) Multipath interference. (c) Inter-radar synchronization latency. (d) The number of PRI. (e) The number of points. (f) Data throughput. the proposed VSA demonstrates significantly enhanced robustness compared to the EKF, particularly in low-SNR regimes. As illustrated, at an SNR of 0 dB, the EKF yields an RMSE of approximately 0.25 m, whereas the VSA effectivel… view at source ↗
Figure 7
Figure 7. Figure 7: Simulation results of standard trajectory tracking. (a) Rhombus trajectory. (b) Circular trajectory. (c) Star-shape trajectory. 10 15 20 25 EKF Proposed 0 5 EKF Proposed 2 4 6 8 10 Time (s) 0 Error (cm) 0 10 20 30 10 15 20 25 0 5 EKF Proposed 2 4 6 8 10 Time (s) 0 0 10 20 30 20 30 40 50 0 10 3 6 9 12 15 Time (s) 0 0 15 30 45 EKF Proposed (a) (b) (c) (d) (e) (f) Rhombus Circle Star EKF Proposed Error (cm) E… view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of error in EKF and proposed method. (a), (c) and (e) illustrate the variation of error over time. (b), (d), and (f) are error distribution boxplot. and a bandwidth of 1500 MHz, corresponding to a range resolution of 0.10 m. The frame periodicity is set to 50 ms, with 256 pulses integrated per frame, resulting in a maximum unambiguous radial velocity of 3 m/s. The detection thresholds for range … view at source ↗
Figure 10
Figure 10. Figure 10: Experiment results of single radar and multi-site radar trajectory tracking. (a) MIMO radar 1; (b) MIMO radar 2; (c) Multi-site SISO radar; Proposed SAV-PF Reference (a) (b) (c) [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Experiment results of radar trajectory tracking. (a) Rhombus trajectory. (b) Circular trajectory. (c) Star-shaped trajectory. Proposed SAV-PF Proposed SAV-PF Position Error (cm) 0.2 CDF 0 0.4 0.6 0.8 1 100 20 30 40 Proposed SAV-PF (a) 0.2 CDF 0 0.4 0.6 0.8 1 Position Error (cm) 100 20 30 40 (b) Position Error (cm) 100 20 30 40 (c) 0.2 CDF 0 0.4 0.6 0.8 1 [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: CDF of estimation error of SAV-PF method and proposed method. (a) Rhombus trajectory. (b) Circular trajectory. (c) Star-shaped trajectory. 10.5 cm and 16.5 cm in rhombus trajectory, 14.0 cm and 15.0 cm in circular trajectory, 16.3 cm and 22.5 cm in star￾shaped trajectory. These results clearly demonstrate that the proposed method exhibits superior robustness across different trajectory patterns. C. Multit… view at source ↗
read the original abstract

This paper introduces a high-precision indoor positioning and tracking method that utilizes multi-site single-input single-output (SISO) radar systems. We propose a novel velocity synthesis-assisted (VSA) localization algorithm that iteratively refines target position estimates within range bins by fusing radial velocity measurements from multiple radars. This approach ensures enhanced accuracy in both velocity and position estimation. Moreover, the inherent geometric constraints introduced by velocity synthesis enable the proposed algorithm to remain robust under low signal-to-noise ratio (SNR), severe multipath propagation, and large synchronization latency. Notably, our method eliminates the use of multiple-input-multiple-output (MIMO) configurations and stringent phase synchronization requirements, substantially reducing hardware complexity while maintaining high positioning accuracy. We define standardized reference trajectories to facilitate a comprehensive and reproducible performance evaluation. Extensive simulations and experimental validations demonstrate that our multi-site radar systems achieve centimeter-level tracking accuracy for human subjects, outperforming existing methods in complex trajectory tracking.

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.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based solely on the abstract, the central claim rests on standard radar signal processing assumptions with no explicit free parameters, invented entities, or ad-hoc axioms detailed.

axioms (1)
  • domain assumption Standard models for radar propagation, multipath, and radial velocity measurements apply in indoor settings.
    Implicit foundation for the velocity synthesis and robustness claims under low SNR and multipath.

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Reference graph

Works this paper leans on

47 extracted references

  1. [1]

    Indoor localization of passive uhf rfid tags based on phase-of-arrival evaluation,

    M. Scherhäufl, M. Pichler, E. Schimbäck, D. J. Müller, A. Ziroff, and A. Stelzer, “Indoor localization of passive uhf rfid tags based on phase-of-arrival evaluation,” IEEE Trans. Microw. Theory Techn., vol. 61, no. 12, pp. 4724–4729, Dec. 2013

    2013

  2. [2]

    Fall detection and 3-d indoor localization by a custom rfid reader embedded in a smart e-health platform,

    G. Paolini, D. Masotti, F. Antoniazzi, T. Salmon Cinotti, and A. Costanzo, “Fall detection and 3-d indoor localization by a custom rfid reader embedded in a smart e-health platform,” IEEE Trans. Microw. Theory Techn., vol. 67, no. 12, pp. 5329– 5339, Dec. 2019

    2019

  3. [3]

    Precise distance and ve- locity measurement for real time locating in multipath environ- ments using a frequency-modulated continuous-wave secondary radar approach,

    S. Roehr, P. Gulden, and M. Vossiek, “Precise distance and ve- locity measurement for real time locating in multipath environ- ments using a frequency-modulated continuous-wave secondary radar approach,” IEEE Trans. Microw. Theory Techn., vol. 56, no. 10, pp. 2329–2339, Oct. 2008

    2008

  4. [4]

    A 24-ghz wireless locating system for human–robot interaction,

    Y. Dobrev, T. Pavlenko, J. Geiß, M. Lipka, P. Gulden, and M. Vossiek, “A 24-ghz wireless locating system for human–robot interaction,” IEEE Trans. Microw. Theory Techn., vol. 67, no. 5, pp. 2036–2044, May 2019

    2036

  5. [5]

    Real-time noncoherent uwb positioning radar with millimeter range accuracy: Theory and experiment,

    C. Zhang, M. J. Kuhn, B. C. Merkl, A. E. Fathy, and M. R. Mahfouz, “Real-time noncoherent uwb positioning radar with millimeter range accuracy: Theory and experiment,” IEEE Trans. Microw. Theory Techn., vol. 58, no. 1, pp. 9–20, Jan. 2010

    2010

  6. [6]

    Investigation of high-accuracy indoor 3-d positioning using uwb technology,

    M. R. Mahfouz, C. Zhang, B. C. Merkl, M. J. Kuhn, and A. E. Fathy, “Investigation of high-accuracy indoor 3-d positioning using uwb technology,” IEEE Trans. Microw. Theory Techn., vol. 56, no. 6, pp. 1316–1330, Jun. 2008. 14

    2008

  7. [7]

    Stereo vision-based 3d positioning and tracking,

    A. Islam, M. Asikuzzaman, M. O. Khyam, M. Noor-A-Rahim, and M. R. Pickering, “Stereo vision-based 3d positioning and tracking,” IEEE Access, vol. 8, pp. 138 771–138 787, Jul. 2020

    2020

  8. [8]

    A comprehen- sive survey of indoor localization methods based on computer vision,

    A. Morar, A. Moldoveanu, I. Mocanu, F. Moldoveanu, I. E. Radoi, V. Asavei, A. Gradinaru, and A. Butean, “A comprehen- sive survey of indoor localization methods based on computer vision,” Sensors, vol. 20, no. 9, p. 2641, May 2020

    2020

  9. [9]

    Indoor scene segmentation using a structured light sensor,

    N. Silberman and R. Fergus, “Indoor scene segmentation using a structured light sensor,” in Proc. IEEE Int. Conf. Comput. Vis. Workshops. IEEE, Nov. 2011, pp. 601–608

    2011

  10. [10]

    Vision-based gait recognition: A survey,

    J. P. Singh, S. Jain, S. Arora, and U. P. Singh, “Vision-based gait recognition: A survey,” Ieee Access, vol. 6, pp. 70 497–70 527, Nov. 2018

    2018

  11. [11]

    A review of vision-based gait recognition methods for human identification,

    J. Wang, M. She, S. Nahavandi, and A. Kouzani, “A review of vision-based gait recognition methods for human identification,” in Proc. Int. Conf. Digit. Image Comput.: Tech. Appl., Dec. 2010, pp. 320–327

    2010

  12. [12]

    Device-free localization: A review of non-rf techniques for unobtrusive indoor positioning,

    F. Alam, N. Faulkner, and B. Parr, “Device-free localization: A review of non-rf techniques for unobtrusive indoor positioning,” IEEE Internet Things J., vol. 8, no. 6, pp. 4228–4249, Mar. 2021

    2021

  13. [13]

    Indoor multihu- man device-free tracking system using multiradar cooperative sensing,

    W. Li, Y. Wu, R. Chen, H. Zhou, and Y. Yu, “Indoor multihu- man device-free tracking system using multiradar cooperative sensing,” IEEE Sensors J., vol. 23, no. 22, pp. 27 862–27 871, Nov. 2023

    2023

  14. [14]

    Un- derstanding and modeling of wifi signal based human activity recognition,

    W. Wang, A. X. Liu, M. Shahzad, K. Ling, and S. Lu, “Un- derstanding and modeling of wifi signal based human activity recognition,” in Proc. ACM Int. Conf. Mobile Comput. Netw., Sep. 2015, pp. 65–76

    2015

  15. [15]

    3-d indoor local- ization and identification through rssi-based angle of arrival estimation with real wi-fi signals,

    H.-C. Yen, L.-Y. Ou Yang, and Z.-M. Tsai, “3-d indoor local- ization and identification through rssi-based angle of arrival estimation with real wi-fi signals,” IEEE Trans. Microw. Theory Techn., vol. 70, no. 10, pp. 4511–4527, Oct. 2022

    2022

  16. [16]

    Uwtracking: Passive human tracking under los/nlos scenarios using ir-uwb radar,

    Z. Guo, D. Wang, L. Gui, B. Sheng, H. Cai, F. Xiao, and J. Han, “Uwtracking: Passive human tracking under los/nlos scenarios using ir-uwb radar,” IEEE Trans. Mobile Comput., vol. 23, no. 12, pp. 11 853–11 870, Dec. 2024

    2024

  17. [17]

    Catch your breath: Simultaneous rf tracking and respiration monitoring with radar pairs,

    T. Zheng, Z. Chen, S. Zhang, and J. Luo, “Catch your breath: Simultaneous rf tracking and respiration monitoring with radar pairs,” IEEE Trans. Mobile Comput., vol. 22, no. 11, pp. 6283– 6296, Nov. 2023

    2023

  18. [18]

    Oracle: Occlusion-resilient and self-calibrating mmwave radar network for people tracking,

    M. Canil, J. Pegoraro, A. Shastri, P. Casari, and M. Rossi, “Oracle: Occlusion-resilient and self-calibrating mmwave radar network for people tracking,” IEEE Sensors J., vol. 24, no. 3, pp. 3157–3171, Feb. 2024

    2024

  19. [19]

    Sequential human gait classification with distributed radar sensor fusion,

    H. Li, A. Mehul, J. Le Kernec, S. Z. Gurbuz, and F. Fioranelli, “Sequential human gait classification with distributed radar sensor fusion,” IEEE Sensors J., vol. 21, no. 6, pp. 7590–7603, Mar. 2021

    2021

  20. [20]

    Radar-based millimeter-wave sensing for accurate 3-d indoor positioning: Potentials and challenges,

    A. Sesyuk, S. Ioannou, and M. Raspopoulos, “Radar-based millimeter-wave sensing for accurate 3-d indoor positioning: Potentials and challenges,” IEEE J. Indoor Seamless Position. Navig., vol. 2, pp. 61–75, Jan. 2024

    2024

  21. [21]

    Moving target localization and activity/gesture recognition for indoor radio frequency sensing applications,

    Y. Sun, H. Xiong, D. K. P. Tan, T. X. Han, R. Du, X. Yang, and T. T. Ye, “Moving target localization and activity/gesture recognition for indoor radio frequency sensing applications,” IEEE Sensors J., vol. 21, no. 21, pp. 24 318–24 326, Nov. 2021

    2021

  22. [22]

    Soft fall detection with a height-tracking method based on mimo radar system,

    C. Ding, H. Zhao, Y. Ma, H. Hong, and X. Zhu, “Soft fall detection with a height-tracking method based on mimo radar system,” IEEE Geosci. Remote Sens. Lett., vol. 20, pp. 1–5, May 2023

    2023

  23. [23]

    Biomedical radar system for real-time contactless fall detection and indoor localization,

    M. Mercuri, P. J. Soh, P. Mehrjouseresht, F. Crupi, and D. Schreurs, “Biomedical radar system for real-time contactless fall detection and indoor localization,” IEEE J. Electromagn. RF Microw. Med. Biol., vol. 7, no. 4, pp. 303–312, Dec. 2023

    2023

  24. [24]

    Multitarget vital signs measure- ment 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 measure- ment with chest motion imaging based on mimo radar,” IEEE Trans. Microw. Theory Techn., vol. 69, no. 11, pp. 4735–4747, Nov. 2021

    2021

  25. [25]

    Socionext Inc., 60GHz 3D Detection model SC1220AT2, Dec. 2023. [Online]. A vailable: https://www.socionext.com/en/ products/assp/radar-sensor/SC1220/

    2023

  26. [26]

    Texas Instruments Inc., Single-chip 60-GHz to 64-GHz automotive radar sensor integrating DSP, MCU and radar accelerator, Apr. 2025. [Online]. A vailable: https://www.ti. com/product/AWR6843

    2025

  27. [27]

    2-d self-injection-locked doppler radar for locating multiple people and monitoring their vital signs,

    W.-C. Su, P.-H. Juan, D.-M. Chian, T.-S. J. Horng, C.-K. Wen, and F.-K. Wang, “2-d self-injection-locked doppler radar for locating multiple people and monitoring their vital signs,” IEEE Trans. Microw. Theory Techn., vol. 69, no. 1, pp. 1016–1026, Jan. 2021

    2021

  28. [28]

    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, Oct. 2024

    2024

  29. [29]

    Efficient near-field radar microwave imaging based on joint constraints of low-rank and structured sparsity at low snr,

    S. Song, Y. Dai, Y. Song, and T. Jin, “Efficient near-field radar microwave imaging based on joint constraints of low-rank and structured sparsity at low snr,” IEEE Trans. Microw. Theory Techn., vol. 73, no. 5, pp. 2962–2977, May 2025

    2025

  30. [30]

    A cooperative mimo radar network using highly integrated fmcw radar sen- sors,

    A. Frischen, J. Hasch, and C. Waldschmidt, “A cooperative mimo radar network using highly integrated fmcw radar sen- sors,” IEEE Trans. Microw. Theory Techn., vol. 65, no. 4, pp. 1355–1366, Apr. 2017

    2017

  31. [31]

    High angular resolution digital beamforming method for coherent FMCW MIMO radar networks,

    M. Q. Nguyen, R. Feger, J. Bechter, M. Pichler-Scheder, and A. Stelzer, “High angular resolution digital beamforming method for coherent FMCW MIMO radar networks,” in IEEE MTT-S Int. Microw. Symp. Dig., Jun. 2021, pp. 756–759

    2021

  32. [32]

    Con- stellation estimation, coherent signal processing, and multiper- spective imaging in an uncoupled bistatic cooperative radar network,

    P. Fenske, T. Koegel, R. Ghasemi, and M. Vossiek, “Con- stellation estimation, coherent signal processing, and multiper- spective imaging in an uncoupled bistatic cooperative radar network,” IEEE J. Microw., vol. 4, no. 3, pp. 486–500, May 2024

    2024

  33. [33]

    Federated target recognition for multi- radar sensor data security,

    Y. Song and G. Dong, “Federated target recognition for multi- radar sensor data security,” IEEE Trans. Geosci. Remote Sens., vol. 61, pp. 1–10, May 2023

    2023

  34. [34]

    Hybrid cognition for target tracking in cognitive radar networks,

    W. W. Howard and R. M. Buehrer, “Hybrid cognition for target tracking in cognitive radar networks,” IEEE Trans. Radar Syst., vol. 1, pp. 118–131, Jun. 2023

    2023

  35. [35]

    Continuous human activity recognition for arbi- trary directions with distributed radars,

    R. G. Guendel, M. Unterhorst, E. Gambi, F. Fioranelli, and A. Yarovoy, “Continuous human activity recognition for arbi- trary directions with distributed radars,” in 2021 IEEE Radar Conf., Feb. 2021, pp. 1–6

    2021

  36. [36]

    User identifica- tion under the collaborative auto-calibration of multi-mmwave radars,

    Y. Wu, W. Li, R. Chen, Y. Yu, and H. Zhou, “User identifica- tion under the collaborative auto-calibration of multi-mmwave radars,” IEEE Sensors J., vol. 25, no. 3, pp. 4930–4942, Feb. 2025

    2025

  37. [37]

    Indoor localization with distributed 5g small cells considering time alignment errors,

    B. Wang, Y. Xu, S. Li, X. Tan, and G. Battistelli, “Indoor localization with distributed 5g small cells considering time alignment errors,” IEEE Sensors Journal, vol. 24, no. 13, pp. 20 813–20 823, May. 2024

    2024

  38. [38]

    Efficient methods for time synchronization in distributed radar systems,

    D. U. Mugil, F. D. Girolamo, and S. Tanzini, “Efficient methods for time synchronization in distributed radar systems,” in ICYRIME, 2024. [Online]. A vailable: https: //api.semanticscholar.org/CorpusID:274789881

    2024

  39. [39]

    Massive mimo for high-accuracy target localization and tracking,

    X. Zeng, F. Zhang, B. Wang, and K. J. R. Liu, “Massive mimo for high-accuracy target localization and tracking,” IEEE Internet Things J., vol. 8, no. 12, pp. 10 131–10 145, Jun. 2021

    2021

  40. [40]

    IEEE Std 1588-2019, 2019

    IEEE Standard for a Precision Clock Synchronization Protocol for Networked Measurement and Control Systems, IEEE Standards Association Std. IEEE Std 1588-2019, 2019. [Online]. A vailable:https://standards.ieee.org/standard/1588-2019.html

    2019

  41. [41]

    Network time protocol version 4: Protocol and algorithms specification,

    D. L. Mills, J. Burbank, and W. Kasch, “Network time protocol version 4: Protocol and algorithms specification,” Internet Engineering Task Force (IETF), Tech. Rep. RFC 5905, Jun. 2010. [Online]. A vailable: https://datatracker.ietf. org/doc/html/rfc5905

    2010

  42. [42]

    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,” in Proc. IEEE MTT-S Int. Wireless Symp. (IWS), May 2024, pp. 1–3

    2024

  43. [43]

    An indoor fall monitoring system: Robust, multistatic radar sensing and explainable, feature-resonated deep neural network,

    M. Shen, K.-L. Tsui, M. A. Nussbaum, S. Kim, and F. Lure, “An indoor fall monitoring system: Robust, multistatic radar sensing and explainable, feature-resonated deep neural network,” IEEE J. Biomed. Health Inform., vol. 27, no. 4, pp. 1891–1902, Apr. 2023

    1902

  44. [44]

    Velocity vector synthesis method based on distributed millimeter wave radar system,

    M. Ling, Y. Mao, and L. Zhao, “Velocity vector synthesis method based on distributed millimeter wave radar system,” in 2024 16th Int. Conf. Commun. Softw. Netw. (ICCSN), Dec. 2024, pp. 317–322

    2024

  45. [45]

    Short- range indoor positioning and tracking under monochromatic illuminations,

    R. Cai, C. Zhu, Y. Yu, X. Zhi, Z. Zhu, and L. Ran, “Short- range indoor positioning and tracking under monochromatic illuminations,” IEEE Trans. Microw. Theory Techn., vol. 73, no. 10, pp. 8106–8117, May 2025. 15

    2025

  46. [46]

    Survey of maneuvering target track- ing. part i. dynamic models,

    X. Rong Li and V. Jilkov, “Survey of maneuvering target track- ing. part i. dynamic models,” IEEE Trans. Aerosp. Electron. Syst., vol. 39, no. 4, pp. 1333–1364, Oct. 2003

    2003

  47. [47]

    Bar-Shalom, X

    Y. Bar-Shalom, X. R. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software. Hoboken, NJ, USA: John Wiley & Sons, Aug. 2004

    2004