Skip to content

NOTES

SSI — Stochastic Subspace Identification: Cross-Order Voting for Best Damping

Build block Toeplitz matrices, perform SVD, use shift invariance to extract system matrices, scan across multiple model orders with voting. Best damping accuracy when data is sufficient.

Intuition

Core Idea: Big Matrix from Correlation

Like ERA, SSI builds a large matrix and does SVD, but SSI uses a Toeplitz matrix (symmetric block matrix from cross-correlation).

The key innovation is cross-order voting:

Order 2 → n candidate modes
Order 4 → n candidate modes
Order 6 → ...
Order 16 → ...

All candidates → cluster → stable modes are real

This avoids manual model order selection—modes that keep appearing across orders are the real physical ones.

Pairwise Variant (SSI-PW)

Process 2 channels at a time, build 2×2 block Toeplitz, run SSI on each pair, then vote across all results. More robust for weak modes.

SSI vs ERA

ERA SSI
Matrix type Hankel Toeplitz
SVD Once Multiple (cross-order)
Damping High Highest (with enough data)
Data needed Moderate More (≥ 60s recommended)

Design Deep Dive

1. Toeplitz vs Hankel

ERA (Hankel) SSI (Toeplitz)
Structure rows=past, cols=future rows=future, cols=past
Post-SVD Truncate → balanced realization → A Truncate → observability → shift invariance → A
Noise type Impulsive Stationary random
Damping High Highest (with sufficient data)

Toeplitz columns are cross-correlation functions themselves, closer to "statistical averages" and more natural for stationary random excitation. Hankel processes a single realization and is more sensitive to transient components.

2. Cross-Order Voting Design

SSI's key advantage: no manual model order selection. All orders n=2,4,...,16 pool candidates → 10% frequency tolerance clustering → majority vote.

Why SSI-PW (pairwise) is more robust: global SSI builds nch×blockrows Toeplitz (60×60 for 5ch). SSI-PW processes 2ch at a time (24×24). Smaller matrices are more stable; weak modes aren't drowned by strong modes on other channels.

3. SSI vs ERA Selection

Condition Recommended Why
Short data (< 30s) ERA Hankel works better with limited data
Abundant data (> 60s) SSI Toeplitz statistical averaging wins
Damping critical SSI Cross-order voting + averaging
Frequency critical ERA Direct output from balanced realization
## When to Use SSI
  • Plenty of data (≥ 60s, recommended 120s)
  • Damping accuracy is critical
  • Need automatic model order selection