arxiv: 2512.00435 · v3 · submitted 2025-11-29 · 📡 eess.SP

Rotatable Antenna-array-enhanced Direction-sensing for Low-altitude Communication Network: Method and Performance

Jinbing Jiang , Feng Shu , Bin Deng , Maolin Li , Jiatong Bai , Yan Wang , Cunhua Pan , Jiangzhou Wang This is my paper

Pith reviewed 2026-05-17 03:39 UTC · model grok-4.3

classification 📡 eess.SP

keywords rotatable antenna arraydirection sensingRoot-MUSICCRLBdirective antenna patternRMSElow-altitude network

0 comments p. Extension

The pith

Rotating a directive antenna array during sensing improves direction estimates for targets far from the broadside.

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

Practical multi-antenna receivers suffer degraded direction sensing when emitters lie far from the array normal because each element's directive pattern attenuates the received energy. The paper builds a rotatable array model that includes these patterns, derives the associated Cramer-Rao lower bound, and introduces a recursive rotation Root-MUSIC algorithm that repeatedly reorients the array. A reader would care because the approach restores usable accuracy in low-altitude networks where perfect alignment cannot be assumed. Simulations confirm the iteration count stays small and the resulting root-mean-squared error falls markedly closer to the bound than with fixed-orientation Root-MUSIC.

Core claim

A rotatable array system is established with the directive antenna pattern of each element taken into account, where each element has the same antenna pattern. The corresponding Cramer-Rao lower bound is derived. A recursive rotation Root-MUSIC (RR-Root-MUSIC) direction-sensing method is proposed. Simulation results show that the proposed rotation method converges rapidly with about ten iterations and makes a significant enhancement on the direction-sensing accuracy in terms of RMSE when the target direction departs seriously far away from the normal vector of array. Compared with conventional Root-MUSIC, the sensing performance of the proposed RR-Root-MUSIC method is much closer to the CRLB

What carries the argument

The recursive rotation Root-MUSIC (RR-Root-MUSIC) procedure, which iteratively reorients the array to reduce pattern-induced attenuation while estimating direction of arrival.

Load-bearing premise

The array can be rotated without adding mechanical errors or extra costs and the identical-element directive pattern model fully describes real hardware behavior.

What would settle it

A hardware test that rotates a physical array and measures RMSE for off-normal angles, checking whether the observed error stays below conventional Root-MUSIC and approaches the derived CRLB without added rotation noise.

Figures

Figures reproduced from arXiv: 2512.00435 by Bin Deng, Cunhua Pan, Feng Shu, Jiangzhou Wang, Jiatong Bai, Jinbing Jiang, Maolin Li, Yan Wang.

**Figure 2.** Figure 2: Illustration of the geometric relationship for the r [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Illustration of boresight deflection angle and direc [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Convergence curves of the proposed RR-Root-MUSIC me [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Convergence curves of the proposed RR-Root-MUSIC me [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 7.** Figure 7: MSE versus the elevation angles θ for proposed RR-Root-MUSIC method with transmit powers Pt ∈ {0 dBm, 20 dBm} [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

**Figure 8.** Figure 8: The motion path of the UAV around BS, and MSE of propose [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗

**Figure 9.** Figure 9: MSE of proposed RR-Root-MUSIC method versus the bore [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗

read the original abstract

In a practical multi-antenna receiver, each element of the receive antenna array has a directive antenna pattern, which is still not fully explored and investigated in academia and industry until now. When the emitter is deviated greatly from the normal direction of antenna element or is close to the null-point direction, the sensing energy by array will be seriously attenuated such that the direction-sensing performance is degraded significantly. To address such an issue, a rotatable array system is established with the directive antenna pattern of each element taken into account, where each element has the same antenna pattern. Then, the corresponding the Cramer-Rao lower bound (CRLB) is derived. Finally, a recursive rotation Root-MUSIC (RR-Root-MUSIC) direction-sensing method is proposed and its root-mean-squared-error (RMSE) performance is evaluated by the derived CRLB. Simulation results show that the proposed rotation method converges rapidly with about ten iterations, and make a significant enhancement on the direction-sensing accuracy in terms of RMSE when the target direction departs seriously far away from the normal vector of array. Compared with conventional Root-MUSIC, the sensing performance of the proposed RR-Root-MUSIC method is much closer to the CRLB.

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.

Desk Editor's Note private letter to a colleague

The rotatable array with directive patterns improves off-normal RMSE in simulations via RR-Root-MUSIC, but the gains rest on idealized rotations that ignore mechanical and pattern errors.

read the letter

The paper tackles direction sensing when directive antenna patterns attenuate signals for targets far from broadside in low-altitude networks. They set up a rotatable array where all elements share the same pattern, derive the CRLB for that geometry, and introduce RR-Root-MUSIC that recursively rotates the array and refines the estimate. Simulations indicate convergence in about ten iterations and RMSE much closer to the bound than plain Root-MUSIC for those off-normal cases. That is the concrete advance: a practical adjustment to Root-MUSIC that accounts for real element patterns instead of assuming omnidirectional ones. The work is straightforward and directly addresses a limitation that shows up in actual hardware. The soft spot is the idealization. Each rotation is treated as perfect reorientation with no added phase jitter, amplitude drift, or mechanical delay, and the CRLB derivation appears to fold in only the post-rotation geometry. If those assumptions slip in real deployments, the reported accuracy boost shrinks. The stress-test concern about omitted hardware imperfections lands here; nothing in the abstract or described results shows they modeled rotation costs or element variation. This is for researchers working on array signal processing for wireless links, especially low-altitude or UAV scenarios. A reader who already uses Root-MUSIC variants and wants a simple rotation fix will get usable ideas and numbers. It deserves peer review because the problem is real, the method is explicit, and the simulation evidence is presented, even though robustness checks against non-ideal rotation would strengthen it.

Referee Report

1 major / 1 minor

Summary. The manuscript proposes a rotatable antenna-array system for direction sensing that incorporates identical directive antenna patterns at each element. It derives the corresponding Cramer-Rao lower bound (CRLB) and introduces a recursive rotation Root-MUSIC (RR-Root-MUSIC) algorithm. Simulations are used to show that the rotation procedure converges in roughly ten iterations and yields substantially lower RMSE than conventional Root-MUSIC for targets far from the array normal, with performance approaching the derived CRLB.

Significance. If the reported RMSE gains hold under realistic rotation conditions, the work would offer a practical means to mitigate the severe attenuation that occurs when targets lie near the nulls of directive elements, a common situation in low-altitude networks. The rapid convergence and explicit CRLB comparison are useful benchmarks; the approach also highlights the value of treating antenna patterns as part of the sensing model rather than assuming ideal omnidirectional elements.

major comments (1)

The simulation results that claim RR-Root-MUSIC RMSE is much closer to the CRLB than conventional Root-MUSIC for off-normal targets rest on the premise that each rotation perfectly reorients identical directive patterns without introducing phase noise, amplitude variation, or mechanical latency. This modeling choice is load-bearing for the central performance claim; any realistic error model could erase or reverse the reported advantage.

minor comments (1)

Abstract: the phrase 'the corresponding the Cramer-Rao lower bound' contains a duplicated article and should be corrected for clarity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the thorough review and valuable feedback. The comment raises an important point about modeling assumptions in our simulations. We address it point-by-point below and are prepared to revise the manuscript accordingly to strengthen the presentation of our results.

read point-by-point responses

Referee: The simulation results that claim RR-Root-MUSIC RMSE is much closer to the CRLB than conventional Root-MUSIC for off-normal targets rest on the premise that each rotation perfectly reorients identical directive patterns without introducing phase noise, amplitude variation, or mechanical latency. This modeling choice is load-bearing for the central performance claim; any realistic error model could erase or reverse the reported advantage.

Authors: We agree that the reported performance gains are demonstrated under the assumption of ideal rotations that perfectly reorient the identical directive patterns without additional impairments such as phase noise, amplitude variation, or mechanical latency. This idealization was chosen to isolate and clearly quantify the benefits of the proposed rotatable array architecture and the RR-Root-MUSIC algorithm relative to the derived CRLB, allowing direct comparison in a controlled setting. The manuscript does not claim robustness to these practical imperfections. In the revised version, we will add an explicit discussion of this modeling choice in the simulation section, including a note on its implications for real-world low-altitude networks, and we will clarify that the results represent an upper-bound performance under perfect mechanical control. If space permits, we will also outline how such non-idealities could be incorporated in future extensions. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained

full rationale

The paper models a rotatable array incorporating identical directive element patterns, derives the CRLB for direction estimation under this geometry, introduces the RR-Root-MUSIC recursive rotation procedure, and reports simulation RMSE results that approach the CRLB for off-normal angles. None of these steps reduce by construction to fitted parameters, self-referential definitions, or load-bearing self-citations; the CRLB and algorithm are obtained from standard array signal processing starting points, and performance claims rest on explicit Monte-Carlo evaluation rather than tautological renaming or input-output equivalence.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review limited to abstract; the central claims rest on standard array signal processing assumptions plus the stated identical directive pattern model.

axioms (1)

domain assumption Each element of the receive antenna array has the same directive antenna pattern
Explicitly stated in the abstract as the basis for the rotatable system model.

pith-pipeline@v0.9.0 · 5531 in / 1191 out tokens · 73501 ms · 2026-05-17T03:39:33.915506+00:00 · methodology

discussion (0)

Reference graph

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