TRANSIENT DETECTION WITH UNKNOWN STATISTICS VIA SOURCE CODING

Quickest detection problems are fairly common in surveillance applications, as framing surveillance alerts as a change in an observation sequence's statistics is often apt. In this work, we consider the scenario where an appropriate statistical description of our observations is not available, neither before nor after the transient we are trying to detect. In this vein, we explore the use of the database Lempel-Ziv, or LZ77, procedure, to detect this transient in the observation data. This algorithm is known to have phrase lengths that are asymptotically distributed as Gaussian random variables, which allows us to form a quickest detection problem around statistics of the coded output. This work specifies procedures to perform source-agnostic transient detection using Locally Optimal (LO) statistic to augment a Page CUSUM test. The work also shows an application to acoustic data.

Automated summary

This study explores the use of the Lempel-Ziv (LZ77) procedure to detect transients in surveillance data. The algorithm is characterized by phrase lengths that are asymptotically distributed as Gaussian random variables, allowing for the formulation of quickest detection problems based on statistics of the encoded output. The study specifies procedures for source-agnostic transient detection using locally optimal statistics to augment a Page CUSUM test, and demonstrates an application to acoustic data.