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Experimental Validation of Combined Imaging and Vibration Mitigation for High-Altitude Platforms

Ákos Radványi, Anita Keszler, Dániel Balogh, Béla Takarics, András Majdik, Andrej Tokarjev, Gábor Kovács, Tamás Szirányi, Bálint Vanek

arXiv:2609.09376v1eess.SYcs.SY

TL;DR

HAPS imaging must balance payload limits against resolution, while flexible-wing motion complicates multi-camera image fusion. The paper combines distributed cameras, super-resolution, and active vibration control on a flexible-wing demonstrator, reporting improved image quality with AVC across nine metrics.

  • Problem

    No integrated framework had been documented that combines active vibration suppression with multi-camera super-resolution on HAPS under realistic structural deformation, despite flexible wings and limited payload capacity.

    Source: §1 · pp. 2–3

  • Method

    A scaled-down flexible-wing demonstrator combines lightweight distributed cameras, super-resolution processing, and active vibration control to evaluate imaging under low-frequency oscillations.

    Source: §1 · p. 3

  • Results

    With AVC, results improve over without AVC across nine image-quality measures and in several cases approach disturbance-free values, supporting improved super-resolution image quality on the demonstrated platform.

    Source: §6.4 · p. 20, §8 · p. 27

  • Takeaways & Limitations

    The demonstrated system uses lightweight commercial sensors, software post-processing, and AVC as an alternative to expensive or heavy optical systems for HAPS imaging.

    Source: §1 · p. 3, §8 · p. 27

    Real stratospheric suitability remains unverified; gust dynamics, temperature-dependent sensor behavior, and optical disturbances are unmodeled, motivating more robust control synthesis.

    Source: §7 · p. 27

Abstract

from arXiv · show

The demand for continuous, high-resolution aerial monitoring is driving interest in High- Altitude Pseudo-Satellites (HAPS), which offer a cost-effective alternative to satellites with greater flexibility. While HAPS platforms provide valuable Earth observation potential, se- lecting an optical sensor requires balancing resolution and payload constraints, given their lightweight structures. We propose a multidisciplinary concept to enhance the Earth obser- vation capabilities of HAPS using a commercially available, weight-efficient distributed camera network, combined with advanced image processing and active vibration control. A super- resolution pipeline is introduced, where images from individual cameras are preprocessed and fed into a super-resolution algorithm. The low structural rigidity of HAPS platforms increases their sensitivity to low-frequency vibrations, which must be mitigated through active control to preserve imaging performance. A nominal H-infinity mixed-sensitivity controller is designed to suppress resonance peaks at camera locations. The methodology is experimentally validated on a simplified wing platform designed for real-time testing. Quantitative evaluation based on nine metrics demonstrates significant image quality improvements, highlighting the effectiveness of H-infinity control in mitigating low-frequency elastic modes and stabilizing the platform for super- resolution. These results show that lightweight commercial sensors, combined with advanced image processing and control, can deliver high-quality imaging for future HAPS remote sensing missions.

1 Introduction

HAPS imaging must balance payload limits against resolution, while flexible-wing motion complicates image fusion. The paper proposes an integrated distributed-camera, super-resolution, and vibration-control framework, tested on a flexible-wing demonstrator.

1 Introduction

  • Multiple lightweight commercial cameras distributed along the wingspan offer an alternative to scaling conventional high-performance cameras, which is impractical under HAPS payload constraints.The individual viewpoints are fused through image-processing techniques.Source: §1 · pp. 2–3
  • Flexible-wing vibration and aeroelastic deformation change relative camera poses, degrading registration accuracy and super-resolution reconstruction quality.The proposed setting combines multi-view and multi-frame inputs by fusing multiple frames from each camera.Source: §1 · p. 2
  • Prior flexible-aircraft control work focused on flight dynamics and structural control, while aerial super-resolution generally assumed single-camera or rigid configurations.The cited work did not account for structural motion or time-varying inter-camera geometry.Source: §1 · pp. 2–3
  • A scaled-down flexible-wing demonstrator with two synchronized cameras and embedded actuation enables hardware-in-the-loop evaluation of super-resolution under low-frequency oscillations.The study examines how active vibration suppression affects image fusion.Source: §1 · p. 3
  • The framework couples distributed super-resolution imaging with active vibration control on a flexible-wing platform, addressing an integration gap under realistic structural deformation.The authors describe this as the first experimental framework to couple these elements on a flexible wing.Source: §1 · p. 3
  • The paper presents the combined framework as addressing HAPS imaging needs within lightweight, flexible platforms that have limited payload capacity.The contribution links distributed imaging with the interdependence between structural dynamics and multi-sensor fusion quality.Source: §1 · pp. 2–3

2 The Demonstrator Platform

The demonstrator is a flexible-wing laboratory platform for studying distributed imaging and vibration mitigation. Two cameras observe a controlled target while actuators generate and suppress structural vibrations.

2 The Demonstrator Platform

  • The platform simulates a distributed Earth-observation payload on a flexible wing, with two lightweight optical sensors and vibration control central to obtaining high-quality sensor information.The setup is designed to capture structural characteristics of real-life HAPS platforms in a laboratory environment.Source: §2 · p. 3
  • Two cameras, two actuators, and four IMUs are mounted at different locations, with cameras positioned so the considered elastic modes affect their imaging.One actuator generates gust-like disturbance; the secondary actuator mitigates vibration using IMU measurements.Source: §2 · p. 3, Figure 1 · p. 4
  • The cameras image predefined laboratory patterns as a controlled target, and their recordings are post-processed with super-resolution under varied disturbances, with results compared against active mitigation.Figure 2 pairs a real-life HAPS platform with its mechanical model.Source: §2 · p. 4

3 The Proposed Framework

The framework models a scaled flexible wing using finite-element simulations updated with experimental modal data, then designs nominal H∞ control around camera-location deformation rates. Modal reduction and frequency-dependent weights focus the model and controller on the platform’s relevant dynamics.

3.1 The modeling procedure of the wing platform

  • The 5-meter laboratory platform uses a hollow-square AlMgSi0.5 beam, designed to place low-frequency modes near HAPS values while remaining stiff against permanent deformation.Typical HAPS first bending modes are below 10 rad/s; platform length is limited by laboratory size constraints.Source: §3.1 · p. 4
  • Static deflection tests led to an effective modulus of 60 GPa, rather than the alloy’s nominal 70 GPa, to capture assembled-structure effects; experimental modal data then updated the FE model.Vibration tests used a 0.5–25 Hz sine sweep with IMU measurements.Source: §3.1 · pp. 5–6
  • The first three vertical bending modes account for cumulative β of 0.911 for translation and 0.999 for rotation, supporting model reduction along the primary disturbance direction.The reduction criterion retains modes whose cumulative effective modal mass exceeds 90%.Source: §3.1 · p. 7, Table 3 · p. 7

3.2 Active vibration control design

  • The controller uses plant transfers from disturbance and control moments to IMU measurements, with camera-location elastic deformation rates defining the performance output.The measurement vector includes angular rates and linear accelerations from available IMUs.Source: §3.2 · pp. 7–8
  • Frequency-dependent weights balance transfer magnitudes over 0.5–23 Hz; disturbance weighting reflects the 60 A motor limit, control weighting limits extreme-frequency actuation, and noise weighting uses sensor covariance.The control-action weight is implemented as a band-stop filter.Source: §3.2 · pp. 8–9
  • The nominal H∞ controller minimizes the induced L2 norm of closed-loop transfers, while actuator saturation constraints trade control performance against hardware limits.The control architecture separates disturbance and control inputs and feeds IMU measurements to the controller.Source: §3.2 · pp. 7, 8, 10

4 Distributed-Camera Super-Resolution Methodology

The pipeline adapts a handheld burst super-resolution algorithm to synchronized images from distributed cameras through alignment and brightness preprocessing. It uses 16 images per camera as a quality–computation compromise, while brightness adjustment improves most reported metrics.

Pipeline overview

  • The pipeline adapts the handheld burst SR algorithm [28] to distributed-camera images by preprocessing synchronized image pairs before super-resolution.The stages include grouping frames into bursts, image transformation, and brightness adjustment.Source: §4 · p. 10, Figure 6 · p. 10

Image burst

  • Sixteen images per camera provided a good quality–computation compromise: eight of nine metrics showed no noticeable gain beyond this burst size, while smaller bursts degraded quality.DISTS peaked around eight images per camera, but its subsequent decline was marginal relative to gains across the other eight metrics.Source: §4 · p. 11, Figure 7 · p. 11

Image transformation

  • Aligning burst images to the first primary-camera image with rounded SIFT-derived translations makes the different viewpoints suitable for fusion.The passage notes that accurately replicating or quantifying the reduced viewpoint differences expected at real-world target distances remains challenging in the laboratory.Source: §4 · pp. 11–12

Brightness adjustment

  • Brightness adjustment improved seven metrics across 62 with-AVC test bursts, while ERQA and DISTS changed slightly within measurement standard deviations.The seven improving metrics were MAE, RMSE, PSNR, SSIM, MS-SSIM, FSIM, and PieAPP.Source: §4 · p. 12, Table 4 · p. 13

5 Experimental Protocol

The protocol keeps actuator-control and image-processing inputs distinct, then evaluates control, super-resolution, and image quality under defined laboratory conditions. It specifies chirp excitation, three disturbance scenarios, a constructed ground truth, nine metrics, and qualitative visual tests.

5 Experimental Protocol

  • Actuator currents and inertial measurements form the hardware/control path, while synchronized camera frames are grouped, aligned, brightness-normalized, and sent to super-resolution.Keeping the paths distinct permits comparison of vibration-control conditions with image quality without treating actuator commands as SR inputs.Source: §5 · p. 14

5.1 Control excitation and actuator operating envelope

  • The control test uses 300-second sine sweeps from 0.5 to 25 Hz at three amplitudes; severe-profile control demand peaks near 21 A against a 60 A saturation limit.That operating point leaves a 65% safety margin; explicit anti-windup is omitted in the prototype, with adding a scheme identified as future work for generic atmospheric conditions that may exceed the threshold.Source: §5.1 · pp. 14–15, Figure 10 · p. 14, Figure 11 · p. 15

5.2 Image-processing inputs and datasets

  • Imaging tests apply the disturbance at 6.78 Hz, identified as the frequency with the highest spectral density, and compare disturbance-free, without-AVC, and with-AVC scenarios.The paired camera frames are formed into bursts of 16 images per camera, with alignment and intensity normalization before SR.Source: §5.2 · p. 15

5.3 Region of interest, ground truth, and quantitative protocol

  • For laboratory evaluation, a 1024 × 1024 region from the first camera serves as reference, with the corresponding second-camera region aligned using the first image pair’s transformation.Averaged frames from a fixed camera in a disturbance-free scenario form the ground truth; burst inputs are resized to 512 × 512 for comparison, operations not required in the normal SR workflow.Source: §5.3 · p. 16

5.4 Evaluation metrics

  • Nine metrics cover pixel accuracy, edge quality, structural and texture preservation, and perceptual quality.They comprise MAE, RMSE, PSNR, SSIM, DISTS, ERQA, FSIM, MS-SSIM, and PieAPP.Source: §5.4 · p. 16

5.5 Visual-test protocol

  • Qualitative tests use geometric-pattern images from 16-image-per-camera bursts; the 2048×2048 SR outputs show sharper edges, finer detail, and less noise than 1024×1024 inputs.These examples are not metric-based comparisons.Source: §5.5 · p. 16

6 Experimental Evaluation

On the laboratory platform, real-time AVC remained stable across the modeled uncertainty envelope and attenuated the first two vertical bending modes. It moderately suppressed the third mode, while the torsional mode remained unaffected by the actuator configuration.

6 Experimental Evaluation

  • The evaluation links inertial measurements of AVC’s structural effect with visual and metric-based tests of SR output across three recorded laboratory scenarios.The additional analyses assess robustness of the evidence without extending the claim beyond the experiment.Source: §6 · p. 16

6.1 Inertial/control performance

  • The nominal controller was evaluated with 500 perturbed plant variants, varying natural frequencies by ±10%, damping ratios by ±15%, and actuator gain by ±60%.The reported nominal design has γ = 23.12, a 3.34 dB multiloop gain margin, and a 21.5° multiloop phase margin at the critical frequency.Source: §6.1 · p. 19
  • Low-frequency vibration at the first two vertical bending modes was substantially attenuated, the third was moderately suppressed, and closed-loop responses remained stable across the modeled uncertainty envelope.Some amplification above 20 Hz was consistent with the Bode sensitivity integral; the 10.67 Hz torsional mode was unaffected because the actuators exert torque mainly about the y-axis.Source: §6.1 · p. 19, Figure 14 · p. 19

6.2 Visual SR output

With-AVC results improve over without-AVC results across nine complementary image-quality measures and approach disturbance-free values in several cases. The section also presents qualitative SR examples and describes the laboratory ground-truth comparison used for metric evaluation.

6.2 Visual SR output

  • AVC-enabled SR examples show sharper edges, finer structural detail, and less visible noise than the raw camera samples, but are qualitative and limited to recorded laboratory scenes.Each output has four times the input pixel count.Source: §6.2 · p. 19

6.3 Quantitative ground-truth construction

  • The metric comparison uses a 1024×1024 SR output and an aligned, brightness-adjusted ground-truth window; burst inputs are resized to 512×512 for this evaluation.Figure 15 presents a burst sample, the SR output, and the ground-truth image; resizing and region selection are evaluation operations rather than normal SR workflow steps.Source: §6.3 · p. 20, Figure 15 · p. 20

6.4 Nine-metric summary

  • Across nine complementary metrics, with-AVC results improve over without-AVC results and approach disturbance-free values in several cases.This agreement with the inertial result supports the conclusion that AVC improves SR image quality on the demonstrated platform.Source: Table 5 · p. 20, §6.4 · p. 20

6.5 Per-burst metric distributions

With-AVC results were statistically significant across all nine metrics, and per-burst results show quality close to the disturbance-free case and above the without-AVC case. The analysis uses independent burst samples and accounts for unequal variances.

6.5 Per-burst metric distributions

  • Per-burst quality was generally highest without disturbance, close with AVC, and lower without AVC; burst spread also reflected the starting phase of the 6.78 Hz vibration.Inspecting individual bursts verifies that the accepted mean result is not an artifact of reporting only an aggregate.Source: §6.5 · pp. 20–21, Figure 16 · p. 21

6.6 Statistical significance analysis

  • The with-AVC and without-AVC samples each contained 62 independent bursts, which were not necessarily paired and were not assumed to have equal variances.The camera bursts were unsynchronized to the 6.78 Hz disturbance, so they began and ended at different vibration phases.Source: §6.6 · pp. 21–22
  • A separate Welch t-test was applied to each metric using the two samples’ means, standard deviations, and sample sizes.Table 6 reports the resulting t-statistics and degrees of freedom.Source: §6.6 · p. 21
  • All nine metrics showed a with-AVC improvement that the analysis concludes is statistically significant with high confidence.The Welch t-test accounts for sample variability, but does not establish performance on untested platforms or in untested environments.Source: Table 6 · p. 22, §6.6 · p. 22

6.7 Ground-truth sensitivity analysis

The ground-truth sensitivity study found that metric responses varied in magnitude, but the comparative AVC result held within the recorded data. Averaging fixed-camera frames suppressed random noise while preserving visible fine detail.

6.7 Ground-truth sensitivity analysis

  • The sensitivity test used eleven disturbance-free frames selected at regular intervals as alternative ground truths for the same with-AVC SR outputs.Results were averaged per burst and compared with results using the original averaged ground truth.Source: §6.7 · p. 22, Figure 19 · p. 25
  • Averaging fixed-camera frames suppressed random noise without removing visible fine structure in the disturbance-free reference.Figure 17 shows three source frames; Figure 18 shows the averaged ground truth alongside two disturbance-free SR outputs.Source: §6.7 · p. 22, Figure 17 · p. 23, Figure 18 · p. 24
  • RMSE and PSNR were nearly insensitive to ground-truth choice; MAE, ERQA, MS-SSIM, and FSIM changed slightly, while SSIM, DISTS, and PieAPP differed more noticeably.These shifts qualify individual metric values, while the comparative AVC result was preserved within the recorded data.Source: §6.7 · p. 22, Figure 19 · p. 25

6.8 Bicubic baseline comparison

AVC improved output quality across all nine metrics with both the proposed SR pipeline and bicubic interpolation. The bicubic comparison checks the direction of the AVC effect, not equivalence between the methods.

6.8 Bicubic baseline comparison

  • The bicubic baseline used one frame from each of the 62 bursts and compared the same with-AVC and without-AVC data series.Because bicubic interpolation does not fuse a burst, this removes burst size and other multi-frame fusion choices from the comparison.Source: §6.8 · p. 26
  • The baseline checks whether the AVC improvement’s direction persists across SR approaches, but does not establish bicubic equivalence or performance beyond this experiment.Source: §6.8 · p. 26
  • Both the proposed SR pipeline and bicubic interpolation improved with AVC across all nine metrics.Table 7 reports means ± standard deviations for the without-AVC and with-AVC scenarios.Source: Table 7 · p. 26, §6.8 · p. 26

7 Real-world Applicability and Limitations

The processing-time estimate suggests high along-track overlap under stated imaging and flight assumptions. However, the demonstrator was not exposed to aerodynamic loading or atmospheric effects, and real-stratosphere suitability requires further testing.

7 Real-world Applicability and Limitations

  • At 20 km altitude, a 16° camera field of view, and 10 m/s travel speed, the estimated 51-second processing interval leaves about 90% along-track overlap.A 32-image burst took 17 seconds on an office workstation and roughly triple that on a Jetson-class embedded computer.Source: §7 · p. 26
  • The platform had no aerodynamic loading or atmospheric effects, so its vibration spectra and image sequences are a pre-exposure reference for more complex aeroelastic conditions.Source: §7 · pp. 26–27
  • Real-stratosphere suitability remains unestablished; gust dynamics, temperature-dependent sensors, and optical disturbances may complicate reconstruction and motivate more robust control synthesis.Source: §7 · p. 27

8 Conclusion

The proposed distributed-camera system combines software post-processing with active vibration control as an alternative to heavy optical systems for payload-limited, flexible-wing platforms. Real-time two-camera tests showed vibration suppression and improved final image quality; atmospheric effects remain to be evaluated.

8 Conclusion

  • The controller suppressed vibrations and improved input images for super-resolution, significantly enhancing final image quality on the defined metrics.Validation used a dedicated real-time test bench with two lightweight cameras.Source: §8 · p. 27
  • The distributed-camera system combines software post-processing with active vibration control as an alternative to expensive or heavy optical systems for payload-limited, flexible-wing platforms.The concept was validated through real-time tests on a dedicated experimental test bench.Source: §8 · p. 27
  • Long-range haze and significant illumination variations remain unconsidered, so simulated atmospheric and environmental testing is needed to assess reliability and generalization.The architecture and super-resolution pipeline can support more than two sensors; future work includes diverse modalities such as multispectral cameras.Source: §8 · p. 27
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