Safety of particle filters: Some first results on the time evolution of particle filter estimates

Particle filters (PFs) is a class of Monte Carlo algorithms that propagate over time a set of $N\in\mathbb{N}$ particles which can be used to estimate, in an online fashion, the sequence of filtering distributions $(\hat{\eta}_t)_{t\geq 1}$ defined by a state-space model. Despite the popularity of PFs, the time evolution of their estimates does not appear to have been previously studied in the literature. Denoting by $(\hat{\eta}_t^N)_{t\geq 1}$ the PF estimate of $(\hat{\eta}_t)_{t\geq 1}$ and letting $\kappa\in (0,1)$, we first show that for any number of particles $N$ it holds that, with probability one, we have $\|\hat{\eta}_t^N- \hat{\eta}_t\|\geq \kappa$ for infinitely many $t\geq 1$, with $\|\cdot\|$ a measure of distance between probability distributions. Considering a simple filtering problem we then provide reassuring results concerning the ability of PFs to estimate jointly a finite set $\{\hat{\eta}_t\}_{t=1}^T$ of filtering distributions by studying $\P(\sup_{t\in\{1,\dots,T\}}\|\hat{\eta}_t^{N}-\hat{\eta}_t\|\geq \kappa)$. Finally, on the same toy filtering problem, we prove that sequential quasi-Monte Carlo, a randomized quasi-Monte Carlo version of PF algorithms, offers greater safety guarantees than PFs in the sense that, for this algorithm, it holds that $\lim_{N\rightarrow\infty}\sup_{t\geq 1}\|\hat{\eta}_t^N-\hat{\eta}_t\|=0$ with probability one.