Pith. sign in

REVIEW 2 major objections 5 minor 32 references

Near-Field Position and Orientation Tracking With Hybrid ELAA Architecture

T0 review · 2 major / 5 minor · reviewed 2026-08-03 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A hybrid antenna array with only three RF chains can track a mobile device's position and orientation nearly as well as a fully digital array, by configuring its analog combiner one step ahead.

desk verdict Clear-thinking extension of near-field tracking to hybrid arrays; the predictive combiner idea is good, but the abstract's accuracy claim is only supported in a low-uncertainty regime and the BCRB skips the adaptive nature of the combiner. read the letter →

arxiv 2512.17274 v2 pith:OBN3ZINT submitted 2025-12-19 eess.SP

Lin Chen , Xiaojun Yuan , Ying-Jun Angela Zhang This is my paper · ORCID
classification eess.SP
keywords near-fieldposetrackinghybridarrayanalogcombiningextendedKalmanfilterFisherinformationCramér-Raoboundquasi-orthogonalmodes
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that a base station with an extremely large antenna array but very few radio-frequency chains can still track a moving device's position and orientation accurately, provided the analog combiner is chosen predictively from the device's predicted pose. The authors propose a predictive analog combining-assisted extended Kalman filter (PAC-EKF) in which the analog combiner is set during the prediction stage, before the uplink pilot arrives, using the predicted state as a surrogate for the unknown true state. They derive a Bayesian Cramér-Rao bound and Fisher information matrix to quantify how much pose information survives analog compression, and show that two low-complexity combiners (one based on the SVD of the observation Jacobian, one based on quasi-orthogonal modes) nearly match fully digital tracking accuracy with three RF chains. A sympathetic reader cares because this directly addresses the hardware bottleneck of extremely large arrays: if correct, near-field pose tracking does not require thousands of RF chains, only a smart way to compress the signal before digitization.

What carries the argument

The key machinery is the predictive analog combining matrix Q_k, constrained to have unit-modulus entries, together with the projection matrix P_{Q_k} = Q_k^H (Q_k Q_k^H)^{-1} Q_k that determines what fraction of the near-field channel's pose sensitivity survives compression. The Fisher information identity F_k = (2/σ_o^2) Re{B_k^H P_{Q_k} B_k} ties the combiner to the observation Jacobian B_k, and two constructions—SVD phase extraction (SVD-PE) and quasi-orthogonal modes (QOMs) ordered by an edge-center rule—provide computationally light ways to approximately maximize this information.

What would settle it

Simulate a scenario with large process noise or a deliberately wrong initial state (e.g., σ_v = 10 m/s² or an initial position error of several meters) and measure whether the SVD-PE and QOM combiners still track within a small factor of the fully digital EKF; if the tracking error diverges or the gap to FD grows without bound, the predictive design's reliance on an accurate predicted state is exposed.

Watch

Extended reading notes

Core claim

The central claim is that predictive analog combining can preserve the pose-relevant components of the near-field uplink signal even under unit-modulus hardware constraints and channel uncertainty. Concretely, the paper shows that the Fisher information about the mobile's position and orientation contained in the compressed observation depends on the projection of the channel Jacobian onto the row space of the analog combiner, and that this information can be retained by aligning the combiner with the dominant left singular subspace of the predicted observation Jacobian. Two low-complexity algorithms achieve this: SVD-PE, which extracts the phases of the unconstrained SVD combiner, and a geo

Load-bearing premise

The analog combiner is chosen from the predicted state before the pilot arrives, so if the prediction is poor—due to high process noise, low SNR, or a bad initial estimate—the combiner may discard exactly the signal components that carry information about the true pose.

Editorial extensions

If this is right

  • If the claim holds, a hybrid ELAA base station can perform near-field pose tracking with as few as three RF chains, making the hardware cost comparable to that of a small subarray while retaining nearly full-array accuracy.
  • The transmit power saving of up to 20 dB relative to a random combiner could directly reduce mobile device battery drain in uplink tracking.
  • The QOM-based combiner, being geometry-driven and tied to the channel's effective degrees of freedom, extends naturally to unified uplink tracking and downlink beamforming in the same hybrid architecture.
  • The asymptotic scaling laws (information grows linearly in array size and quadratic in the mobile's effective aperture) give system designers a rule of thumb for how large an array or how long a mobile antenna array must be to achieve a desired tracking accuracy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same predictive-combining principle could apply to other sensing tasks with hybrid arrays, such as radar or terahertz imaging, wherever the unknown quantity evolves smoothly over time and the channel Jacobian can be predicted.
  • The 20 dB power saving claim is likely specific to the simulated geometry, SNR range, and motion model; extrapolating it to other scenarios (e.g., very low SNR or abrupt maneuvers) requires the prediction covariance to remain small, which the paper does not guarantee.
  • A testable extension would be to replace the EKF with a sigma-point or particle filter that propagates the full posterior through the combiner design, potentially improving robustness when the prediction covariance is large.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. This paper studies uplink near-field position and orientation tracking of a multi-antenna mobile station by a hybrid ELAA base station with N_rf RF chains. The BS observes a compressed signal z_k = Q_k H(p_k) x_k + Q_k n_k, where Q_k is a unit-modulus analog combiner. The authors propose PAC-EKF, which designs Q_k during the EKF prediction step from the predicted state s_{k|k-1}. They derive the FIM of the compressed observation and a Bayesian CRB recursion, analyze scaling of average Fisher information with N_b, SNR, distance, and effective MS aperture, and propose two low-complexity combiners: SVD-PE (phase-extracted SVD of the Jacobian) and QOM (quasi-orthogonal modes from prior work). Simulations show near-fully-digital accuracy with 3 RF chains and up to 20 dB transmit power savings over a random combiner.

Significance. If the claims are correct, the paper offers a practical answer to a real hardware bottleneck: with only a few RF chains, a hybrid ELAA can retain most pose-relevant information by exploiting temporal correlation. The FIM expressions and scaling laws are clean and clearly connect the NF channel geometry to estimation limits. The authors credit and re-validate the QOM construction from [27], and the Monte Carlo setup uses identical pilot/noise realizations across schemes, which makes the comparisons fair. The main limitations are that the BCRB analysis does not model the adaptivity of Q_k, and the headline numerical claim is demonstrated only in a small-prediction-error regime.

major comments (2)
  1. [Sec. IV-A, Eq. (20)] The Bayesian CRB recursion treats Q_k as a fixed, non-random matrix. In PAC-EKF, Q_k is a deterministic function of s_{k|k-1}, hence of past observations. The expectation in Eq. (20) is only over s_k given s_{k-1}; it does not average over the distribution of Q_k induced by the predictive filtering loop. Consequently the Bayesian FIM F_{b,k} in Eqs. (18)-(19) is not a valid lower bound for the adaptive PAC-EKF scheme. This matters because Sec. IV is used to derive fundamental performance limits for the proposed framework. Please either restrict the BCRB statement to a fixed-combiner or genie-aided scenario, or extend the recursion to account for the dependence of the observation likelihood on past observations through Q_k, and state conditions under which the bound holds.
  2. [Sec. VI-A / VI-B, Fig. 6] The headline claim that SVD-PE and QOM nearly achieve fully digital tracking accuracy is demonstrated only under a near-perfect prediction regime. Sec. VI-A sets s_{0|0}=s_0 exactly, P_{0|0} is tiny (position variance 0.05^2, orientation variance 0.001^2), and the process noise is small (sigma_v=2 m/s^2, sigma_omega=0.1 rad/s^2, tau=20 ms), so P_{k|k-1} remains small throughout. Since Q_k is designed from s_{k|k-1} (Sec. V-A), the proposed combiners may project out informative components when the prediction covariance is large, and no closed-loop stability or error bound is provided. Please add robustness experiments (e.g., nonzero initial estimation error of several meters/radians, larger P_{0|0}, larger sigma_v/sigma_omega, or mismatch between true and assumed process noise). If the nearly-FD claim is intended only for small prediction error, state this explicitly and temper the contri
minor comments (5)
  1. [Sec. II-A] First sentence: 'to to be odd' contains a duplicated word.
  2. [Appendix C, Eq. (64)] In the displayed equation, the first term in step (b) appears to be the Frobenius norm of the tilde term rather than its squared Frobenius norm; the surrounding text indicates squares are intended.
  3. [Appendix C] The sentence 'Plugging this result into (67)' should refer to Eq. (62), since (67) is the expression being derived.
  4. [Sec. V-A, after Eq. (32)] The text says 'SVD of a small N_b x 3 matrix', but B_k in Eq. (11) is N_b x 5 with two zero columns. Please clarify whether the SVD is computed on the three nonzero columns only.
  5. [Sec. VI-B, Fig. 6] The value of N_rf used in Fig. 6a is not stated in the caption or text; the reader must infer it from context. Please state N_rf explicitly. Also, the up-to-20-dB transmit power saving comparison is based on a single operating point with 50 Monte Carlo trials; reporting error bars or multiple seeds would strengthen the claim.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: FIM/BCRB and SVD-PE are derived in-paper; QOM import from [27] is disclosed and re-validated.

full rationale

The central derivation chain is self-contained. Section IV derives the Bayesian CRB and Fisher information matrix (Propositions 1-4) from the system model of Section II, with no target outcome used as an input. The SVD-PE combiner (Algorithm 1) is obtained by maximizing Tr(F_k) in Eq. (31), taking the unconstrained SVD solution in Eq. (32), and applying phase extraction in Eq. (33); this is a design rule derived from the model, not a fitted predictor. The QOM combiner (Algorithm 2) is imported from the authors' prior work [27], but the paper does not use that citation as a black-box uniqueness or optimality proof: Section V-B re-derives the connection via Proposition 3 and re-validates the subspace alignment in Figs. 5a-5b, and the final tracking claim rests on the Monte-Carlo simulations in Fig. 6. The 'nearly fully digital tracking accuracy' statement is a simulation outcome under the stated settings, not a quantity constructed from those settings. The robustness caveat that predictive combining is tested with accurate initialization (s_{0|0}=s_0 and small P_{0|0}) is an assumption about the operating regime; it affects correctness/generality, not circularity. No fitted parameter is renamed as a prediction, and no uniqueness theorem from the authors' prior work is used to force the design. One minor self-citation exists ([27] for QOMs), but it is not load-bearing because the relevant claims are independently re-validated in this paper.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

No entirely new physical entities are introduced. The QOM construct is borrowed from the same authors' prior work [27] and is the main imported ingredient. Free parameters are conventional simulation inputs, not fitted constants. The central analytical machinery is the FIM/BCRB framework applied to compressed near-field observations.

free parameters (2)
  • Process noise standard deviations σ_v, σ_ω = σ_v = 2 m/s², σ_ω = 0.1 rad/s² (simulation setting)
    Set in Sec. VI-A for the CTRV state-transition model; tracking performance depends on them, but they are standard motion-model inputs rather than fitted data.
  • Initial EKF covariance P_{0|0} = diag([0.05², 0.05², 0.001², v0²/100, ω0²/100])
    Chosen in Sec. VI-A; early-time tracking behavior depends on this initialization, but it is a conventional EKF initialization, not tuned to the final result.
assumptions (5)
  • domain assumption LoS-dominant single-path channel model
    Sec. II-A: NLoS components are assumed severely attenuated at high frequencies; the channel is modeled only by the LoS ray. Blockage is mentioned as triggering re-initialization, but multipath is never modeled.
  • domain assumption Uniform spherical wavefront (A1)
    Sec. IV-C: the asymptotic Fisher-information expressions assume uniform amplitude across the array. The simulations use exact ray-tracing, so numerical claims are not hostage to this assumption, but the scaling laws are.
  • domain assumption CTRV motion model
    Sec. II-B, Eq. (4): constant turn rate and velocity. If the vehicle accelerates or turns non-constantly beyond the process-noise model, prediction errors grow and the predictive combiner degrades.
  • domain assumption QOM properties from [27]
    Sec. V-B: the quasi-orthogonal modes, the resolution formula (34), and the claims about aligning with the dominant channel subspace and preserving >95% channel gain are taken from the authors' prior IEEE TWC paper and not re-proved here.
  • standard math Bayesian CRB recursion of Tichavsky et al. [31]
    Sec. IV-A, Eqs. (18)-(20): the recursive posterior CRB is imported as a standard result.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Near-Field Position and Orientation Tracking With Hybrid ELAA Architecture." pith.science (2026). https://pith.science/paper/OBN3ZINT

@misc{pith2026251217274,
  author       = {Pith},
  title        = {Pith review of: Near-Field Position and Orientation Tracking With Hybrid ELAA Architecture},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OBN3ZINT}},
  note         = {Machine review of arXiv:2512.17274}
}
read the original abstract

This paper investigates near-field (NF) position and orientation tracking of a multi-antenna mobile station (MS) using an extremely large antenna array (ELAA)-equipped base station (BS) with a limited number of radio frequency (RF) chains. Under this hybrid array architecture, the received uplink pilot signal at the BS is first combined by analog phase shifters, producing a low-dimensional observation before digital processing. Such analog compression provides only partial access to the ELAA measurement, making it essential to design an analog combiner that can preserve pose-relevant signal components despite channel uncertainty and unit-modulus hardware constraints. To address this, we propose a predictive analog combining-assisted extended Kalman filter (PAC-EKF) framework, where the analog combiner can leverage the temporal correlation in the MS pose variation to capture the most informative signal components predictively. We then analyze fundamental performance limits via Bayesian Cram\'er-Rao bound and Fisher information matrix, explicitly quantifying how the analog combiner, array size, signal-to-noise ratio, and MS pose influence the pose information contained in the uplink observation. Building on these insights, we develop two methods for designing a low-complexity analog combiner. Numerical results show that the proposed predictive analog combining approach significantly improves tracking accuracy, even with fewer RF chains and lower transmit power.

Figures

Figures reproduced from arXiv: 2512.17274 by the authors.

Figure 1
Figure 1. System Model. (a) A geometric model of the LoS MIMO channel between ULAs with a hybrid array architecture at the BS. (b) A [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Schematic diagram of PAC-EKF. 2) EKF Update Stage: The new compressed observation zk = Qkyk under the chosen analog combiner Qk is then used to refine the prior estimate. Note that the system is mixed with the complex-valued observation model and the real-valued state transition model. The Kalman-gain form of the EKF [15] is usually employed for a pure real-valued system. Therefore, we consider an information form o… view at source ↗
Figure 3
Figure 3. MS’s physical and effective array apertures ( [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: Connection between QOMs and average Fisher Information, where [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Pose estimation accuracy and channel reconstruction accuracy of the proposed PAC-EKF. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

32 extracted references · 2 linked inside Pith

  1. [27]

    Quasi-orthogonal beamforming in near-field line-of-sight MIMO channel,

    L. Chen, X. Yuan, and Y .-J. A. Zhang, “Quasi-orthogonal beamforming in near-field line-of-sight MIMO channel,”IEEE Trans. Wirel. Commun., pp. 1–1, 2025

    2025

  2. [1]

    Vision, requirements, and technology trend of 6G: How to tackle the challenges of system coverage, capacity, user data-rate and movement speed,

    S. Chen, Y .-C. Liang, S. Sun, S. Kang, W. Cheng, and M. Peng, “Vision, requirements, and technology trend of 6G: How to tackle the challenges of system coverage, capacity, user data-rate and movement speed,”IEEE Wirel. Commun., vol. 27, no. 2, pp. 218–228, 2020

    2020

  3. [2]

    A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,

    W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,”IEEE Netw., vol. 34, no. 3, pp. 134–142, 2019

    2019

  4. [3]

    Near-field MIMO communications for 6G: Fundamentals, challenges, potentials, and future directions,

    M. Cui, Z. Wu, Y . Lu, X. Wei, and L. Dai, “Near-field MIMO communications for 6G: Fundamentals, challenges, potentials, and future directions,”IEEE Commun. Mag., vol. 61, no. 1, pp. 40–46, 2023

    2023

  5. [4]

    Fraunhofer and Fresnel distances: Unified derivation for aperture antennas,

    K. T. Selvan and R. Janaswamy, “Fraunhofer and Fresnel distances: Unified derivation for aperture antennas,”IEEE Antennas Propag. Mag., vol. 59, no. 4, pp. 12–15, 2017

    2017

  6. [5]

    Near- field communications: A tutorial review,

    Y . Liu, Z. Wang, J. Xu, C. Ouyang, X. Mu, and R. Schober, “Near- field communications: A tutorial review,”IEEE Open J. Commun. Soc., vol. 4, pp. 1999–2049, 2023

    1999

  7. [6]

    Near and far field model mismatch: Implications on 6G communications, localization, and sensing,

    A. Elzanaty, J. Liu, A. Guerra, F. Guidi, Y . Ma, and R. Tafazolli, “Near and far field model mismatch: Implications on 6G communications, localization, and sensing,”IEEE Internet Thing Mag., vol. 7, no. 5, pp. 120–126, 2024

    2024

  8. [7]

    Near-field localization and sensing with large-aperture arrays: From signal modeling to pro- cessing,

    Z. Wang, P. Ramezani, Y . Liu, and E. Bj ¨ornson, “Near-field localization and sensing with large-aperture arrays: From signal modeling to pro- cessing,”IEEE Signal Process. Mag., vol. 42, no. 1, pp. 74–87, 2025

    2025

Show all 32 references
  1. [8]

    Scalable near- field localization based on partitioned large-scale antenna array,

    X. Yuan, M. Zhang, Y . Zheng, B. Teng, and W. Jiang, “Scalable near- field localization based on partitioned large-scale antenna array,”IEEE Trans. Wirel. Commun., vol. 24, no. 3, pp. 2203–2217, Mar. 2025

    2025

  2. [9]

    Array partitioning based near-field attitude and location estimation,

    M. Zhang, X. Yuan, B. Teng, and L. Wang, “Array partitioning based near-field attitude and location estimation,” 2025. [Online]. Available: https://arxiv.org/abs/2504.16800

    2025 arXiv

  3. [10]

    Near-field tracking with large antenna arrays: Fundamental limits and practical algorithms,

    A. Guerra, F. Guidi, D. Dardari, and P. M. Djuri ´c, “Near-field tracking with large antenna arrays: Fundamental limits and practical algorithms,” IEEE Trans. Signal Process., vol. 69, pp. 5723–5738, 2021

    2021

  4. [11]

    Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems,

    X. Yu, J.-C. Shen, J. Zhang, and K. B. Letaief, “Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems,” IEEE J. Sel. Top. Signal Process., vol. 10, no. 3, pp. 485–500, 2016

    2016

  5. [12]

    Hybrid beamforming for millimeter wave systems using the MMSE criterion,

    T. Lin, J. Cong, Y . Zhu, J. Zhang, and K. Ben Letaief, “Hybrid beamforming for millimeter wave systems using the MMSE criterion,” IEEE Trans. Commun., vol. 67, no. 5, pp. 3693–3708, 2019

    2019

  6. [13]

    Spatially sparse precoding in millimeter wave MIMO systems,

    O. El Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. W. Heath, “Spatially sparse precoding in millimeter wave MIMO systems,”IEEE Trans. Wirel. Commun., vol. 13, no. 3, pp. 1499–1513, 2014

    2014

  7. [14]

    Channel estimation and hybrid combining for mmWave: Phase shifters or switches?

    R. M ´endez-Rial, C. Rusu, A. Alkhateeb, N. Gonz ´alez-Prelcic, and R. W. Heath, “Channel estimation and hybrid combining for mmWave: Phase shifters or switches?” inProc. 2015 Information Theory and Applications Workshop (ITA). San Diego, CA, USA: IEEE, Feb. 2015, pp. 90–97

    2015

  8. [15]

    Simon,Optimal state estimation: Kalman, H infinity, and nonlinear approaches

    D. Simon,Optimal state estimation: Kalman, H infinity, and nonlinear approaches. John Wiley & Sons, 2006

    2006

  9. [16]

    Detection and tracking of MIMO propagation path parameters using state-space approach,

    J. Salmi, A. Richter, and V . Koivunen, “Detection and tracking of MIMO propagation path parameters using state-space approach,”IEEE Trans. Signal Process., vol. 57, no. 4, pp. 1538–1550, 2008

    2008

  10. [17]

    Joint device positioning and clock syn- chronization in 5G ultra-dense networks,

    M. Koivisto, M. Costa, J. Werner, K. Heiska, J. Talvitie, K. Lepp ¨anen, V . Koivunen, and M. Valkama, “Joint device positioning and clock syn- chronization in 5G ultra-dense networks,”IEEE Trans. Wirel. Commun., vol. 16, no. 5, pp. 2866–2881, 2017

    2017

  11. [18]

    Orientation and location tracking of XR devices: 5G carrier phase-based methods,

    J. Talvitie, M. S ¨aily, and M. Valkama, “Orientation and location tracking of XR devices: 5G carrier phase-based methods,”IEEE J. Sel. Top. Signal Process., vol. 17, no. 5, pp. 919–934, 2023

    2023

  12. [19]

    Channel parameter estimation and TX positioning with multi-beam fusion in 5G mmwave networks,

    M. Koivisto, J. Talvitie, E. Rastorgueva-Foi, Y . Lu, and M. Valkama, “Channel parameter estimation and TX positioning with multi-beam fusion in 5G mmwave networks,”IEEE Trans. Wirel. Commun., vol. 21, no. 5, pp. 3192–3207, 2021

    2021

  13. [20]

    Near-field sensing enabled predictive beamforming: From estimation to tracking,

    H. Jiang, Z. Wang, and Y . Liu, “Near-field sensing enabled predictive beamforming: From estimation to tracking,” 2024. [Online]. Available: https://arxiv.org/abs/2408.02027

    2024 arXiv

  14. [21]

    Attitude estimation assisted short-range UA V localization and tracking based on extremely large antenna array,

    X. Dai, M. Zhang, B. Teng, X. Yuan, and X. Wang, “Attitude estimation assisted short-range UA V localization and tracking based on extremely large antenna array,”IEEE Trans. Wirel. Commun., 2025

    2025

  15. [22]

    Channel estimation for extremely large-scale MIMO: Far-field or near-field?

    M. Cui and L. Dai, “Channel estimation for extremely large-scale MIMO: Far-field or near-field?”IEEE Trans. Commun., vol. 70, no. 4, pp. 2663–2677, 2022

    2022

  16. [23]

    Near-field channel estimation in mixed LoS/NLoS environments for extremely large-scale MIMO systems,

    Y . Lu and L. Dai, “Near-field channel estimation in mixed LoS/NLoS environments for extremely large-scale MIMO systems,”IEEE Trans. Commun., vol. 71, no. 6, pp. 3694–3707, 2023

    2023

  17. [24]

    Finite beam depth analysis for large arrays,

    A. Kosasih and E. Bj ¨ornson, “Finite beam depth analysis for large arrays,”IEEE Trans. Wirel. Commun., vol. 23, no. 8, pp. 10 015–10 029, 2024

    2024

  18. [25]

    Line-of-sight extra-large MIMO systems with angular- domain processing: Channel representation and transceiver architecture,

    A. Tang, J.-B. Wang, Y . Pan, W. Zhang, Y . Chen, H. Yu, and R. C. de Lamare, “Line-of-sight extra-large MIMO systems with angular- domain processing: Channel representation and transceiver architecture,” IEEE Trans. Commun., vol. 72, no. 1, pp. 570–584, 2023

    2023

  19. [26]

    Line-of-sight spatial modulation for indoor mmWave communication at 60 GHz,

    P. Liu, M. Di Renzo, and A. Springer, “Line-of-sight spatial modulation for indoor mmWave communication at 60 GHz,”IEEE Trans. Wirel. Commun., vol. 15, no. 11, pp. 7373–7389, 2016

    2016

  20. [28]

    Comparison and evaluation of advanced motion models for vehicle tracking,

    R. Schubert, E. Richter, and G. Wanielik, “Comparison and evaluation of advanced motion models for vehicle tracking,” inProc. 2008 11th International Conference on Information Fusion, Cologne, Germany, June 2008, pp. 1–6

    2008

  21. [29]

    Advanced object tracking in self-driving cars: EKF and UKF performance evaluation,

    H. Ragab, S. Khamis, and S. A. Napoleon, “Advanced object tracking in self-driving cars: EKF and UKF performance evaluation,” inProc. 2024 6th Novel Intelligent and Leading Emerging Sciences Conference (NILES). Giza, Egypt: IEEE, Oct. 2024, pp. 25–29

    2024

  22. [30]

    Tracking angles of departure and arrival in a mobile millimeter wave channel,

    C. Zhang, D. Guo, and P. Fan, “Tracking angles of departure and arrival in a mobile millimeter wave channel,” inProc. 2016 IEEE international conference on communications (ICC). Kuala Lumpur, Malaysia: IEEE, May 2016, pp. 1–6

    2016

  23. [31]

    Posterior Cram ´er- Rao bounds for discrete-time nonlinear filtering,

    P. Tichavsky, C. H. Muravchik, and A. Nehorai, “Posterior Cram ´er- Rao bounds for discrete-time nonlinear filtering,”IEEE Trans. Signal Process., vol. 46, no. 5, pp. 1386–1396, 1998

    1998

  24. [32]

    Autonomous tracking of intermittent RF source using a UA V swarm,

    F. Koohifar, I. Guvenc, and M. L. Sichitiu, “Autonomous tracking of intermittent RF source using a UA V swarm,”IEEE Access, vol. 6, pp. 15 884–15 897, 2018

    2018

Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.