Dynamic Online Processor-Native Inference for State Estimation
Sensor-rich data-driven applications increasingly use Bayesian approaches to infer latent states of dynamic systems from noisy sensor measurements and physical models. Yet the computation of the likelihood remains an essential bottleneck for accurate posteriors and performant inference. This paper presents a Bayesian filtering technique that uses processor-native uncertainty tracking for both uncertainty propagation and inference. The technique implements deterministic hierarchical importance restructuring through a native operation, giving deterministic latency and bounded memory use for arbitrary models written as program code. Benchmarks across three nonlinear state-space systems compa...
| Authors | Orestis Kaparounakis |
|---|---|
| Primary category | eess.SP |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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