SEQCUP MATLAB Script

- SEQCUP for Trilateration for Field Application: seqcup_trilateration.m

- SEQCUP - Simulation for Levelling: seqcup_level_simulation_example.m
- SEQCUP: seqcup_level_simulation_example2
% This script performs a reproducible Monte Carlo experiment to analyse the
% stepwise behaviour of the SEQCUP procedure on a synthetic levelling network.
% Using a fixed random seed (rng(42)), it generates two-epoch observations with
% Gaussian noise, randomly selects displaced points and displacement magnitudes,
% and applies the SEQCUP sequential testing scheme expansion tests up to q_max).
%The script classifies each run into correct
% identification, over-identification, under-identification, overlap, or no-solution
% and computes the conditional stepwise false-alarm rate when the current null
% model coincides with the true displaced configuration.

- seqcup_GNSS_baseline_simulation_example.m
- Monte Carlo calibration of the critical value for the initial SEQCUP stage q=1
% under the pure null model, using Q_delta in local ENU with 3D correlation per baseline.
% - Post-selection comparisons use a SINGLE ΔSSE statistic: ΔSSE = SSE0 - SSE_A
% between the current null hypothesis and a higher-dimensional alternative.
% - Everything is "by POINT": each candidate adds/removes a full 3D block (E,N,U) of a station.
% - Full covariance propagation in the local ENU system (ECEF→ENU for both coordinates and covariance matrices).
% - For each displacement magnitude in M_list, the script runs mc Monte Carlo realizations
% and stores empirical identification probabilities and the conditional stepwise false-alarm rate.
% - Random seed is fixed with rng(42) for reproducibility of all Monte Carlo experiments.

- seqcup_structured_full.m: pre-screening tool designed to evaluate the geometric separability and structural admissibility of candidate displacement hypotheses in geodetic networks.

- Dataset for trilateration: Rofatto, Vinicius; Matsuoka, Marcelo; Klein, Ivandro (2025), “Dataset Field Trilateration: SEQCUP performance”, Mendeley Data, V2, doi: 10.17632/msg783rh2y.2