Joint Mobile User Positioning and Passive Target Sensing using Optimized Sequential Beamforming

Aymen Hamrouni; Hazem Sallouha; Sofie Pollin

arxiv: 2605.15808 · v1 · pith:CPSH4NQInew · submitted 2026-05-15 · 📡 eess.SP · cs.NI

Joint Mobile User Positioning and Passive Target Sensing using Optimized Sequential Beamforming

Aymen Hamrouni , Sofie Pollin , Hazem Sallouha This is my paper

Pith reviewed 2026-05-20 16:16 UTC · model grok-4.3

classification 📡 eess.SP cs.NI

keywords integrated sensing and communicationsequential beamformingmonostatic sensingbistatic positioningCramer-Rao boundresource allocationmobile user localizationpassive target sensing

0 comments

The pith

Optimizing one shared beamformer across sequential monostatic sensing and bistatic positioning phases yields better accuracy than separate designs.

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

The paper sets out to show that a velocity-aware sequential framework, which runs monostatic sensing first to build a covariance prior on user and target locations, then passes that prior to regularize bistatic positioning under one jointly optimized beamformer, delivers higher performance than optimizing the two phases independently. This approach formulates the problem using position-domain Cramer-Rao bounds and solves a non-convex resource allocation task to balance limited symbols while accounting for mobility. A reader would care because it directly tackles how mobile integrated sensing and communication systems can share scarce resources without sacrificing localization precision or velocity estimates.

Core claim

The authors demonstrate that optimizing a single shared beamformer globally across the monostatic sensing phase and the subsequent bistatic positioning phase, using the covariance prior constructed from the first stage to regularize estimation in the second, produces superior synergistic gains over a two-stage greedy approach and reaches centimeter-level positioning accuracy for the mobile user equipment and passive targets together with robust velocity estimation.

What carries the argument

The sequential Bayesian optimization strategy that first builds a covariance prior from monostatic sensing and then uses it to regularize bistatic positioning under one globally optimized beamformer.

If this is right

Centimeter-level positioning accuracy for both the user equipment and passive targets.
Robust velocity estimation alongside the position estimates.
More efficient balancing of limited symbol resources across the two phases.
Significantly reduced computational runtime compared with independent optimization.

Where Pith is reading between the lines

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

The method could be tested in multi-user settings by extending the prior construction to track several mobile devices simultaneously.
Hardware validation on real arrays would reveal whether the Cramer-Rao-bound optimization holds when hardware impairments are present.
The sequential prior-sharing idea might apply to other integrated sensing tasks such as tracking moving targets with changing velocities.

Load-bearing premise

The covariance prior constructed from the monostatic sensing stage remains reliable enough to regularize bistatic positioning without introducing bias that would degrade the final Cramer-Rao bound performance.

What would settle it

A controlled experiment or simulation in which the monostatic sensing stage is deliberately under-resourced so the prior contains large errors, then checking whether the final positioning accuracy falls below the claimed centimeter level or deviates from the derived bounds.

Figures

Figures reproduced from arXiv: 2605.15808 by Aymen Hamrouni, Hazem Sallouha, Sofie Pollin.

**Figure 2.** Figure 2: Beamforming design for MS and BP. The UE and PTs are placed [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: PEB (log-scale) vs. number of PTs and VEB (log-scale) vs. number [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: VEB (log-scale) vs. speed for BP, Snapshot MS, and Extended MS [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

read the original abstract

Integrated sensing and communication (ISAC) relies on monostatic sensing (MS) and bistatic positioning (BP) to enable comprehensive environmental awareness and user localization. However, existing frameworks predominantly assume static geometries and optimize these modalities independently, neglecting user mobility and sequential information sharing. In this paper, we propose a velocity-aware sequential beamforming framework that dynamically couples MS and BP in time. We derive the Cramer-Rao bounds (CRBs) in the position domain to formulate a non-convex resource allocation problem. Instead of relying on static weighted-sum tradeoffs, we introduce a sequential Bayesian optimization strategy where MS is executed first to construct a reliable structural prior on the UE and passive targets (PTs). This covariance prior is subsequently passed to the UE to regularize the BP estimation stage. We demonstrate that optimizing a single shared beamformer globally across both phases yields superior synergistic gains compared to a two-stage greedy approach. Simulation results validate that the shared sequential design efficiently balances limited symbol resources, achieving centimeter-level positioning accuracy for both the UE and PTs, robust velocity estimation, and a significantly reduced computational runtime.

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 paper shows gains from passing a monostatic covariance prior into a shared-beamformer bistatic stage for mobile ISAC, but the prior's reliability under velocity needs tighter validation.

read the letter

The main thing to know is that this work couples monostatic sensing and bistatic positioning by running the first stage to build a covariance prior, then feeding that prior into a single optimized beamformer for the second stage. The claim is that this sequential approach beats independent two-stage optimization and delivers centimeter-level accuracy plus solid velocity estimates under mobility. That sequential prior transfer and the shared beamformer are the concrete new pieces beyond the usual static or separate optimizations in the ISAC literature. They derive position-domain CRBs, set up the non-convex resource allocation, and solve it with a sequential Bayesian strategy, which is a reasonable way to handle the coupling. The simulations are presented as confirming the synergistic gains and lower runtime, which is useful evidence if the parameters are realistic. The paper does a decent job moving past static-geometry assumptions and showing how limited symbols can be balanced across phases. On the soft spots, the stress-test concern lands: the prior built from the monostatic stage can easily become mismatched once the UE moves, especially if any time elapses before the bistatic phase. The abstract mentions velocity awareness and robust estimation, but it is not obvious from the given details how the prior is updated or whether the time gap is modeled in the CRB or the optimization. If that step is approximate, the claimed superiority over greedy methods could shrink. The non-convex solver is sequential rather than jointly global, so the reported gains might depend on good initialization or specific weight choices between phases. Those are fixable but worth checking in the full derivations. This is for people working on ISAC beamforming and 6G sensing resource allocation. A reader who wants a concrete sequential framework with CRB-based optimization will find usable ideas here. It deserves a serious referee because the problem is relevant and the method is spelled out enough to review, even if the mobility handling needs more scrutiny. I would send it to peer review.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a velocity-aware sequential beamforming framework for ISAC that couples monostatic sensing (MS) and bistatic positioning (BP) in time. It derives CRBs in the position domain to pose a non-convex resource-allocation problem, solved by a sequential Bayesian strategy in which an MS-derived covariance prior on the UE and passive targets is passed to regularize the subsequent BP stage. The central claim is that globally optimizing a single shared beamformer across both phases produces synergistic gains over a two-stage greedy approach, yielding centimeter-level positioning accuracy and robust velocity estimates while efficiently using limited symbol resources.

Significance. If the performance claims hold under mobility, the work would advance practical ISAC design by replacing independent or static optimizations with a sequential information-sharing approach, potentially improving resource efficiency and accuracy in dynamic scenarios.

major comments (2)

[sequential Bayesian optimization strategy] Description of the sequential Bayesian optimization strategy (abstract and formulation section): the claim that the MS-derived covariance prior reliably regularizes BP estimation without bias that degrades final CRB performance is load-bearing for the superiority result. Under user mobility and velocity, the time gap between phases risks rendering the prior mismatched; the manuscript must provide either an explicit bound on prior error or a sensitivity analysis showing that CRB gains persist when the prior is outdated.
[Simulation results] Simulation results section: the reported centimeter-level accuracy and runtime reduction are compared only to a two-stage greedy baseline. To support the global-optimization claim, an additional benchmark against an independently derived prior (or external localization data) is needed; without it, the synergistic-gain conclusion rests on an unverified assumption about prior quality.

minor comments (2)

[Notation] Notation for MS and BP phases should be introduced once and used consistently; occasional shifts between “monostatic sensing” and “MS” reduce readability.
[CRB derivation] The abstract states that CRBs are derived “in the position domain”; the corresponding derivation steps and any approximations (e.g., far-field or narrowband) should be explicitly referenced in the main text for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. The comments raise important points regarding the robustness of the sequential Bayesian strategy under mobility and the strength of the simulation benchmarks. We address each major comment below and indicate the revisions we will make.

read point-by-point responses

Referee: [sequential Bayesian optimization strategy] Description of the sequential Bayesian optimization strategy (abstract and formulation section): the claim that the MS-derived covariance prior reliably regularizes BP estimation without bias that degrades final CRB performance is load-bearing for the superiority result. Under user mobility and velocity, the time gap between phases risks rendering the prior mismatched; the manuscript must provide either an explicit bound on prior error or a sensitivity analysis showing that CRB gains persist when the prior is outdated.

Authors: We appreciate the referee's emphasis on this critical aspect. The proposed framework is explicitly velocity-aware: the MS phase jointly estimates both position and velocity, and the resulting covariance is propagated forward in time using the estimated velocity to align with the BP phase timing. This propagation step is designed to reduce mismatch arising from the inter-phase interval. Nevertheless, we acknowledge that an explicit error bound or sensitivity study would strengthen the presentation. In the revised manuscript we will add a dedicated sensitivity analysis subsection that varies the time gap and injects controlled prior mismatch, reporting the resulting CRB degradation for both the UE and passive targets. This will quantify the range of mobility conditions under which the reported synergistic gains remain intact. revision: partial
Referee: [Simulation results] Simulation results section: the reported centimeter-level accuracy and runtime reduction are compared only to a two-stage greedy baseline. To support the global-optimization claim, an additional benchmark against an independently derived prior (or external localization data) is needed; without it, the synergistic-gain conclusion rests on an unverified assumption about prior quality.

Authors: We agree that broadening the set of baselines will better isolate the contribution of the MS-derived prior and the globally optimized shared beamformer. In the revised simulation section we will introduce an additional benchmark in which the BP stage is supplied with an independently generated prior (e.g., a static covariance assumption or a prior obtained from a separate, non-ISAC localization source). Performance metrics (position CRB, velocity RMSE, and runtime) will be reported for this new baseline alongside the existing two-stage greedy and the proposed sequential design. This comparison will clarify the incremental benefit attributable to the quality of the MS-derived prior versus the global optimization itself. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation uses first-principles CRB and sequential prior construction without reduction to inputs

full rationale

The paper derives position-domain CRBs to set up a non-convex resource allocation problem, then proposes a sequential Bayesian strategy that runs monostatic sensing first to build a covariance prior which is then used to regularize the bistatic positioning stage. This prior is generated from an earlier phase rather than being fitted or defined in terms of the final result, so the optimization output is not equivalent to the input by construction. No self-citations appear as load-bearing for the central claims, no ansatz is smuggled, and no known result is merely renamed. Simulations serve as external validation, keeping the chain self-contained.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The framework rests on standard signal processing assumptions plus the modeling choice that a single shared beamformer can be globally optimized across sequential stages.

free parameters (1)

resource allocation weights between MS and BP phases
The non-convex problem formulation implies tunable weights or constraints on symbol resources that are not specified as fixed constants.

axioms (2)

standard math Cramer-Rao bounds can be derived in the position domain for the joint estimation problem
Invoked when formulating the resource allocation problem from the CRBs.
domain assumption The monostatic sensing stage produces a reliable covariance prior that can be passed without additional bias to the bistatic stage
Central to the sequential Bayesian optimization strategy described.

pith-pipeline@v0.9.0 · 5729 in / 1424 out tokens · 35446 ms · 2026-05-20T16:16:22.412977+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages

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K. Wu, Z. Wang, S.-L. Chen, J. A. Zhang, and Y . J. Guo, “Isac: From human to environmental sensing,”IEEE Journal of Selected Topics in Electromagnetics, Antennas and Propagation, vol. 1, no. 1, 2025

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Extracting the communication channel from monostatic sensing channels: From propagation to impact analysis,

Z. Cui and S. Pollin, “Extracting the communication channel from monostatic sensing channels: From propagation to impact analysis,” IEEE Transactions on Antennas and Propagation, vol. 73, no. 8, 2025

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Mono- static sensing for passive ris localization and tracking,

Z. Ye, F. Junaid, E. Ibrahim, R. Nilsson, and J. Van De Beek, “Mono- static sensing for passive ris localization and tracking,”IEEE Wireless Communications Letters, vol. 13, no. 5, pp. 1260–1264, 2024

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Bistatic information fusion for positioning and tracking in integrated sensing and communication,

M. Bauhofer, M. Henninger, T. Wild, S. Ten Brink, and S. Mandelli, “Bistatic information fusion for positioning and tracking in integrated sensing and communication,” in2025 IEEE Wireless Communications and Networking Conference (WCNC), 2025, pp. 1–6

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On the ground and in the sky: A tutorial on radio localization in ground- air-space networks,

H. Sallouha, S. Saleh, S. De Bast, Z. Cui, S. Pollin, and H. Wymeersch, “On the ground and in the sky: A tutorial on radio localization in ground- air-space networks,”IEEE Communications Surveys & Tutorials, vol. 27, no. 1, pp. 218–258, 2025

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Uwb and gnss sensor fusion using ml-based positioning uncertainty estimation,

M. Tommingas, T. Laadung, S. Varbla, I. M ¨u¨ursepp, and M. Mahtab Alam, “Uwb and gnss sensor fusion using ml-based positioning uncertainty estimation,”IEEE Open Journal of the Communications Society, vol. 6, pp. 2177–2189, 2025

work page 2025

[7] [7]

Fundamental trade-offs in monostatic isac: A holistic investigation toward 6g,

M. F. Keskin, M. M. Mojahedian, J. O. Lacruz, C. Marcus, O. Eriksson, A. Giorgetti, J. Widmer, and H. Wymeersch, “Fundamental trade-offs in monostatic isac: A holistic investigation toward 6g,”IEEE Transactions on Wireless Communications, vol. 24, no. 9, pp. 7856–7873, 2025

work page 2025

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Multi-target localization in multi-static integrated sensing and communication deployments,

M. Bauhoferet al., “Multi-target localization in multi-static integrated sensing and communication deployments,” in2023 2nd International Conference on 6G Networking (6GNet), 2023, pp. 1–4

work page 2023

[9] [9]

Multi-bs multi-target localization for isac systems,

H. Liu, Y . Zhuo, S. Jin, and Z. Wang, “Multi-bs multi-target localization for isac systems,” in2024 IEEE/CIC International Conference on Communications in China (ICCC), 2024, pp. 586–590

work page 2024

[10] [10]

Localization accuracy improvement in multistatic isac with los/nlos condition using 5g nr signals,

K. Khosroshahi, P. Sehier, S. Mekki, and M. Suppa, “Localization accuracy improvement in multistatic isac with los/nlos condition using 5g nr signals,” in2025 IEEE Wireless Communications and Networking Conference (WCNC), 2025, pp. 1–6

work page 2025

[11] [11]

Integrated monostatic and bistatic mmwave sensing,

Y . Ge, H. Kim, L. Svensson, H. Wymeersch, and S. Sun, “Integrated monostatic and bistatic mmwave sensing,” inGLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2023, pp. 3897–3903

work page 2023

[12] [12]

Joint bistatic positioning and monostatic sensing: Optimized beamforming and performance tradeoff,

Y . Zhang, H. Chen, P. Zheng, B. Ning, H. Niu, H. Wymeersch, and T. Y . Al-Naffouri, “Joint bistatic positioning and monostatic sensing: Optimized beamforming and performance tradeoff,”IEEE Transactions on Cognitive Communications and Networking, vol. 11, no. 5, 2025

work page 2025

[13] [13]

Doppler spread and coherence time of rural and highway vehicle-to-vehicle channels at 5.9 ghz,

L. Chenget al., “Doppler spread and coherence time of rural and highway vehicle-to-vehicle channels at 5.9 ghz,” inIEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference, 2008

work page 2008

[14] [14]

A waveform model for bistatic radar with arbitrary long coherent processing interval,

T. McKelvey and P. Dammert, “A waveform model for bistatic radar with arbitrary long coherent processing interval,” in2025 33rd European Signal Processing Conference (EUSIPCO), 2025, pp. 2232–2236

work page 2025

[15] [15]

Optimal spatial signal design for mmwave position- ing under imperfect synchronization,

M. F. Keskinet al., “Optimal spatial signal design for mmwave position- ing under imperfect synchronization,”IEEE Transactions on Vehicular Technology, vol. 71, no. 5, 2022

work page 2022

[16] [16]

Fundamental limits of wideband localization— part i: A general framework,

Y . Shen and M. Z. Win, “Fundamental limits of wideband localization— part i: A general framework,”IEEE Transactions on Information Theory, vol. 56, no. 10, pp. 4956–4980, 2010

work page 2010

[17] [17]

Accuracy bounds for array-based positioning in dense multipath channels,

T. Wilding, S. Grebien, U. M ¨uhlmann, and K. Witrisal, “Accuracy bounds for array-based positioning in dense multipath channels,”Sen- sors, vol. 18, no. 12, p. 4249, 2018

work page 2018