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arxiv: 2605.23536 · v1 · pith:FNX6TVKAnew · submitted 2026-05-22 · 📡 eess.SP

Utilizing Missed Detections in Directional Sensitivity-Based DOA Estimation

Gustav Zetterqvist , Fredrik Gustafsson , Gustaf Hendeby This is my paper

Pith reviewed 2026-05-25 03:23 UTC · model grok-4.3

classification 📡 eess.SP
keywords direction of arrival estimationmissed detectionssignal strengthdirectional sensorslikelihood functionBluetooth Low Energysensor array processing
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The pith

Incorporating missed detections into the likelihood function improves signal strength-based DOA estimation accuracy.

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

The paper develops a probabilistic DOA estimation method for directional sensors that uses only received signal strength and explicitly models both detections and missed detections. It builds the likelihood from the known directional sensitivity pattern and detection threshold so that signals falling below threshold contribute negative information about possible directions. A sympathetic reader would care because many real sensors produce frequent misses, and standard methods discard that information while this one uses it to refine estimates. The approach is validated in simulations with high miss rates and in real BLE experiments with directional antennas.

Core claim

The central claim is that a likelihood function constructed from both detected signals and missed detections, using the directional sensitivity pattern and detection threshold, enables probabilistic DOA estimation that fully leverages the underlying measurement and detection models and yields higher accuracy than baselines that ignore misses.

What carries the argument

Likelihood function that includes the probability of missed detections computed from received signal strength and the known directional sensitivity pattern.

If this is right

  • DOA estimates improve most when missed-detection rates are high.
  • The method works with any directional sensor whose sensitivity pattern is known.
  • Only signal strength measurements are required, avoiding the need for phase data.
  • Real BLE experiments confirm substantial gains over methods that discard negative information.

Where Pith is reading between the lines

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

  • The same modeling step could be added to existing tracking filters to handle intermittent detections.
  • Sensor calibration effort might need to focus more on accurate sensitivity patterns than on lowering thresholds.
  • Extension to multiple simultaneous emitters would require a joint likelihood over all sources and their miss probabilities.

Load-bearing premise

The directional sensitivity pattern and detection threshold are known accurately enough to compute missed detection probabilities directly from the signal strength model.

What would settle it

An experiment or simulation in which the proposed likelihood produces no accuracy gain over a baseline that ignores missed detections, in controlled high-miss-rate scenarios.

Figures

Figures reproduced from arXiv: 2605.23536 by Fredrik Gustafsson, Gustaf Hendeby, Gustav Zetterqvist.

Figure 7
Figure 7. Figure 7: Overall, the simulation results validate the effectiveness [PITH_FULL_IMAGE:figures/full_fig_p005_7.png] view at source ↗
read the original abstract

This paper introduces a signal strength-based direction of arrival (DOA) estimation approach for directional sensors that explicitly accounts for missed detections. In traditional phase-based DOA estimation frameworks, negative information from expected emitters that fall below the detection threshold fall outside the scope of standard measurement models. Unlike phase-based DOA estimation methods, the proposed approach relies only on received signal strength measurements. As a result, missed detections arise naturally from the sensing and detection process and convey valuable information via the known detection thresholds. By incorporating both detected signals and missed detections into the likelihood function, we develop a probabilistic estimation method that fully leverages the underlying measurement and detection models. Simulation results show that the proposed method significantly improves DOA estimation accuracy compared to baseline techniques, particularly in challenging scenarios with high missed-detection rates. Real-world experiments using Bluetooth Low Energy (BLE) signals and directional antennas further validate the effectiveness of the approach, demonstrating substantial performance gains. These findings highlight the value of modeling missed detections in sensor array processing and open new avenues for enhancing localization performance in wireless communication systems.

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 paper proposes a received-signal-strength (RSS) based direction-of-arrival (DOA) estimator for directional sensors that augments the standard likelihood with an explicit term for the probability of missed detection conditioned on DOA and the known detection threshold. It claims that this use of negative information yields substantially better accuracy than baseline methods in both Monte-Carlo simulations and BLE experiments with directional antennas, especially when the missed-detection rate is high.

Significance. If the quantitative gains survive detailed scrutiny and are shown to be robust, the work would be of moderate significance for sensor-array processing: it supplies a concrete probabilistic mechanism for converting missed detections into usable constraints when only RSS (rather than phase) measurements are available.

major comments (2)
  1. [Abstract] Abstract (paragraph on likelihood construction): the central construction multiplies the usual RSS likelihood for detections by P(miss | DOA, threshold) derived from the directional sensitivity pattern; the manuscript supplies no analysis or experiment under pattern mismatch (orientation offset, gain ripple, or threshold drift), leaving the headline performance claim conditional on perfect model knowledge.
  2. [Abstract] Abstract (simulation and experiment claims): the abstract asserts 'significant improvement' and 'substantial performance gains' yet reports neither error metrics with confidence intervals, nor the precise baseline estimators, nor how detection thresholds were obtained or validated; without these quantities the central empirical claim cannot be assessed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. We address each major comment below, indicating the specific revisions that will be incorporated into the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract (paragraph on likelihood construction): the central construction multiplies the usual RSS likelihood for detections by P(miss | DOA, threshold) derived from the directional sensitivity pattern; the manuscript supplies no analysis or experiment under pattern mismatch (orientation offset, gain ripple, or threshold drift), leaving the headline performance claim conditional on perfect model knowledge.

    Authors: We agree that the performance claims are conditioned on accurate knowledge of the directional sensitivity pattern and threshold. The manuscript does not currently include robustness analysis under mismatch. We will add a new subsection to the simulation results section that quantifies estimator sensitivity to orientation offsets, gain ripple, and threshold drift via additional Monte Carlo trials. This will include plots of RMSE versus mismatch level and a discussion of when the method remains advantageous. revision: yes

  2. Referee: [Abstract] Abstract (simulation and experiment claims): the abstract asserts 'significant improvement' and 'substantial performance gains' yet reports neither error metrics with confidence intervals, nor the precise baseline estimators, nor how detection thresholds were obtained or validated; without these quantities the central empirical claim cannot be assessed.

    Authors: The current abstract is indeed qualitative. We will revise the abstract to report concrete RMSE values (with 95% confidence intervals from the Monte Carlo runs), explicitly name the two baseline estimators (a phase-based MUSIC variant and an RSS-only method that ignores missed detections), and state how detection thresholds were set (from the known antenna pattern plus measured noise floor in simulation; from calibration measurements in the BLE experiments). Corresponding quantitative details and validation procedures will also be expanded in the results sections. revision: yes

Circularity Check

0 steps flagged

No circularity detected in derivation

full rationale

The abstract and provided text describe a standard likelihood construction that multiplies RSS terms for detections by P(miss|DOA,threshold) computed from the known directional pattern and threshold. No equations are shown that reduce a prediction to a fitted parameter by construction, no self-citation is invoked as a uniqueness theorem, and the central claim (improved accuracy from modeling misses) rests on external simulation and BLE experiments rather than self-referential definitions. The method treats the sensitivity pattern as an input model, not an output derived from the estimator itself.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no visible free parameters, axioms, or invented entities; all modeling assumptions remain implicit.

pith-pipeline@v0.9.0 · 5712 in / 1141 out tokens · 34506 ms · 2026-05-25T03:23:20.554487+00:00 · methodology

discussion (0)

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Reference graph

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