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A nonzero prearray perturbation changes the posterior but keeps its factor product PSD

Example

Use a scalar state and observation with P−=R=H=1. The exact measurement prearray is Z=[1101]. For the perturbation calculation take E=[01/1000],Z^=Z+E. This is a mathematical backward-perturbation example; it does not assert that a particular floating-point QR produces this exact E. The perturbed prearray has the measurement form with LR=L−=1 and observation coefficient H^=11/10, so the block identity applies to it once its innovation is shown positive below.

Facts & Assumptions

Given: The scalar prearray and the stated nonzero matrix perturbation.

[F1]

A factor perturbation E changes the Gram matrix by at most 2∥Z∥F∥E∥F+∥E∥F2. A factor perturbation gives an explicit Gram-matrix error

[F2]

When the innovation block is positive, the lower-right Schur complement of the block QR Gram matrix is represented as L22L22T. The block QR measurement update gives the canonical Kalman update

Proof

Given: The matrices in the Example.

1.1F2givenalgebra

The exact Gram matrix is ZZT=[2111]. Its innovation variance is 2, its cross covariance is 1, and its exact posterior covariance is 1−1/2=1/2.

2.1F1givenstep 1.1algebra

The perturbed array and its Gram matrix are Z^=[111/1001],Z^Z^T=[221/10011/1011/101]. Here ∥E∥F=1/10, ∥Z∥F=3, and the Gram difference is [21/1001/101/100], whose Frobenius norm is 641/100; it is bounded by [F1]. The perturbed innovation remains positive, and its Schur complement is 1−(11/10)2221/100=1−121221=100221>0. This differs from 1/2 by −21/442.

3.1F2step 2.1algebra∎

By [F2], the perturbed Schur complement is exactly the represented factor product L22L22T, so it is positive semidefinite; here it is the positive scalar 100/221. The nonzero prearray perturbation changes the posterior covariance, while the factor representation retains positive semidefiniteness.

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