PASS: A Trustworthy Precision Approach Simulation System for CAT III Integrity Algorithm Development

Lang Zhu; Xin Zhang; Yulong Sun; Nan Liu; Jiahe Shen; Xiaodong Zhang; Xingqun Zhan

cs.ROOct 16, 2026

PASS: A Trustworthy Precision Approach Simulation System for CAT III Integrity Algorithm Development

Authors: Lang Zhu, Xin Zhang, Yulong Sun, Nan Liu, Jiahe Shen, Xiaodong Zhang, Xingqun Zhan

Organizations: Shanghai Jiao Tong University, Shanghai, China · Flight Automatic Control Research Institute, Xi'an, China

Abstract

For fixed-wing aircraft, reliable localization during approach and landing is critical to flight safety. Visual--inertial navigation is promising, but aircraft navigation also requires integrity: trustworthy outputs and timely warnings when they should not be used. Real approach data are costly to collect, while many visual errors and failures required for integrity algorithm development are difficult to reproduce safely. We present PASS, a trustworthy Precision Approach Simulation System for CAT III integrity algorithm development. In a unified temporal and geometric framework, PASS integrates configurable airport environments, prescribed trajectories, inertial simulation, calibrated stereo images, and continuous runway-relative ground truth. It supports navigation and integrity research through controllable sensor errors and environmental variations, computable visual projection errors, deterministic runway geometry priors, and condition-labeled data for statistical error modeling. We further propose Sim-to-Real Scene Trustworthiness (SRST), a measure based on DINOv2 feature distributions and UMAP, to quantify sim-to-real closeness. Experiments with representative stereo--inertial estimators demonstrate the usability of the generated sequences and quantify localization errors across algorithms and environments. PASS provides a controllable foundation for developing and evaluating visual--inertial navigation and integrity algorithms for fixed-wing precision approaches up to CAT III requirements.

Figures & tables

Resource	Type	Fixed-wing runway approach	Continuous stereo–IMU	Runway-relative ground truth	Configurable errors or conditions	Integrity support information	Sim-to-real credibility assessment
LARD v1 Ducoffe et al. (2023)	Dataset	Yes	No	No	No	No	Partial
LARD v2 Bougacha et al. (2026)	Dataset	Yes	No	No	No	No	Partial
BARS Chen et al. (2023)	Dataset	Yes	No	No	No	No	No
RLD/VALNet Wang et al. (2024)	Dataset	Yes	No	No	No	No	No
UnrealCV Qiu and Yuille (2016)	Simulation framework	No	No	No	Partial	No	No
AirSim Shah et al. (2017)	Simulation framework	No	Supported	No	Supported	No	No

Table 1: Comparison of representative runway-oriented datasets and synthetic simulation resources for fixed-wing precision approach navigation and integrity research.

Figure 1: Overall architecture and information flow of PASS . The Approach Scenario Module defines the runway geometry, operating conditions, and controllable disturbances required for downstream integrity analysis. The Trajectory and IMU Module generates the prescribed aircraft states, runway-relative ground truth, and simulated inertial measurements. The Stereo Imaging Module replays the prescribed motion and records synchronized stereo images. A shared time base and coordinate transformation chain maintain temporal and geometric consistency across the three modules.

Module	Main inputs	Main outputs
Approach Scenario Module	• Airport Geometry • Approach Definition • Environment Settings	• Approach conditions • Environment config
Trajectory and IMU Module	• Upstream geometry • IMU error parameters	• Continuous trajectory • Simulated IMU data
Stereo Imaging Module	• Upstream states • Camera calibration	• Altimeter Measurements • Stereo Camera Images

Table 2: Inputs and outputs of the three modules in PASS shown in Fig. 1 .

Figure 2: Coordinate framework of PASS . The transformation chain connects the geodetic and local navigation frames to the runway, aircraft body/IMU, stereo camera, and image frames, providing a common geometric basis for multimodal data generation.

Error component	Parameter(s)	Modeled effect
Sampling configuration	$\Delta t_{\mathrm{imu}},f_{\mathrm{imu}}$	Integration interval and measurement rate
Constant bias	$e_{b},d_{b}$	Constant gyroscope and accelerometer offsets
Random walk	$w_{eb},w_{db}$	Uncorrelated stochastic error accumulation
Correlated bias	$R_{0G},R_{0A},\tau_{G},\tau_{A}$	First-order Gauss–Markov bias process
Scale-factor error	$\delta K_{G,ii},\delta K_{A,ii}$	Axis-dependent multiplicative error
Axis misalignment	$\delta K_{G,ij},\delta K_{A,ij}$	Off-diagonal cross-axis coupling

Table 3: Parameterization of the PSINS-based IMU simulation model.

Panel of Figure 3 — Figure 3: Virtual stereo camera calibration: (a) relative configurations of the checkerboard and camera used for multi-view calibration and (b) distribution of the resulting reprojection errors.

Parameter	Left camera	Right camera
Image resolution (pixels)	$1920\times 1080$
$f_{x}$ (pixels)	$960.6297$	$959.6249$
$f_{y}$ (pixels)	$960.4265$	$959.4180$
$c_{x}$ (pixels)	$962.9833$	$961.6595$
$c_{y}$ (pixels)	$540.6769$	$540.3199$
$k_{1}$	$1.796\times 10^{-4}$	$1.462\times 10^{-3}$

Table 4: Calibrated parameters of the stereo camera system used in PASS . The distortion coefficients follow the order

[k_{1},k_{2},p_{1},p_{2}]

Panel of Figure 4 — Figure 4: Controlled environmental variations represented in PASS : (a) illumination conditions at different times of day and (b) representative weather conditions. The prescribed aircraft trajectory and runway reference geometry remain unchanged across these variations.

Figure 5: Dynamic-object-induced visual perturbation in the approach view. The birds introduce localized motion and partial occlusion of runway features without modifying the prescribed aircraft trajectory or reference geometry.

Figure 6: Runway geometry and runway-centered reference frame used by PASS . The runway origin, dimensions, corner coordinates, principal line features, and runway heading $\psi_{\mathrm{rwy}}$ define deterministic geometric references for runway feature projection and error evaluation.

Category	ICAO	Runway	Length (m)	Width (m)
Urban Airport	KLAX	L6–R24	3381.7560	60.9600
Urban Airport	ZSPD	R34–L16	3799.9416	60.0456
Desert Airport	OMAA	R31–L13	4099.8600	60.0456
Mountainous Airport	LOWI	26–08	2000.0976	45.1104
Coastal Airport	LFMN	R22–L40	2627.9856	45.1104
Coastal Airport	LGMK	34–16	1901.9520	29.8704

Table 5: Airport categories included in PASS dataset.

Panel of Figure 7 — Figure 7: Representative real and simulated approach images from coastal, desert, mountain, and urban airport environments.

Figure 8: Human-inspired decision process of SRST for unpaired real and simulated approach images. The 4c formulation requires the assigned category to prevail over at least two of three alternatives, whereas the 3c formulation merges coastal and urban scenes and requires the assigned category to prevail over both remaining alternatives.

Airport	Estimator	$15$ s	$20$ s	$25$ s	$30$ s
ZSPD	VINS-Fusion	3.66	7.48	12.65	19.95
	ORB-SLAM3	0.78	1.32	2.02	3.02
KSMO	VINS-Fusion	12.45	20.18	30.58	40.51
	ORB-SLAM3	7.50	10.66	14.15	18.51 †
LGMK	VINS-Fusion	9.64	13.87	18.40	23.74
	ORB-SLAM3	1.40	2.41	3.81	5.48

Table 6: Mean terminal position error [m] for the stereo–inertial terminal approach experiments. Values are in metres and are averaged over the repetitions that reached the planned final frame. Each condition contains three runs unless marked otherwise.

Figure 9: Evolution of the runway-relative position error over the $30$ s terminal approach windows. Each panel shows three independent executions under an identical input and configuration. The cross denotes the last valid output of the interrupted KSMO ORB-SLAM3 run and does not represent the planned terminal error. The vertical axes use a logarithmic scale.

	Middle approach		Low approach
Category	Real	Simulated	Real	Simulated
Coastal	89	41	62	60
Desert	27	61	34	40
Mountainous	56	59	52	42
Urban	104	79	45	22

Table 7: Real and simulated image counts by approach stage and airport scene category.

Figure 10: Joint two-dimensional UMAP embeddings of 469 real and 404 simulated approach images, fitted separately for the middle and low stages of approach. Colors indicate airport scene categories; circles and triangles denote real and simulated images, respectively. Coordinates are comparable within, but not between, the two panels.

UMAP Test	Overall, $n/404$	Middle coastal, $n/41$	Middle urban, $n/79$
Test 1	364 (90.1%)	20 (48.8%)	60 (75.9%)
Test 2	383 (94.8%)	20 (48.8%)	79 (100.0%)
Test 3	365 (90.3%)	38 (92.7%)	43 (54.4%)
Test 4	368 (91.1%)	20 (48.8%)	64 (81.0%)
Test 5	366 (90.6%)	20 (48.8%)	62 (78.5%)

Table 8: SRST results for five selected UMAP realizations. The six unlisted stage-by-category groups each had a 100% image-level concordance rate in all five realizations.

UMAP test	Overall, $n/307$	Middle urban/coastal, $n/63$	Low urban/coastal, $n/42$
Test 1	307 (100.0%)	63 (100.0%)	42 (100.0%)
Test 2	306 (99.7%)	63 (100.0%)	41 (97.6%)
Test 3	305 (99.3%)	63 (100.0%)	40 (95.2%)
Test 4	304 (99.0%)	63 (100.0%)	39 (92.9%)
Test 5	303 (98.7%)	63 (100.0%)	38 (90.5%)
Mean of 50 tests	98.1%

Table 9: Three-category SRST results. The five illustrated realizations were selected for their high overall scores and do not estimate performance across random initializations. The final row reports the mean across all 50 realizations. The four unlisted stage-by-category groups passed completely in the five illustrated realizations.

Figure 11: Joint two-dimensional UMAP embeddings for one illustrative three-category realization with an overall SRST score of 98.7%. Middle and low approach are shown separately. Colors indicate the merged urban/coastal, desert, and mountainous scene categories; circles and triangles denote real and simulated images, respectively.

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Vision-based navigation complements Global Navigation Satellite Systems, but certification demands integrity guarantees that account for faulty measurements. Previous work presented a probabilistic computer vision pipeline for runway-based pose estimation with fault detection inspired by Receiver Autonomous Integrity Monitoring. This work extends that framework by deriving protection levels, which provide probabilistic bounds on pose error that remain valid under undetected faults. We present an algorithm for computing protection levels for the nonlinear Perspective-

n

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