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This paper focuses on designing a particle filter for randomly delayedmeasurements with an unknown latency probability. A generalized measurementmodel is adopted which includes measurements that are delayed randomly by anarbitrary but fixed maximum number of the steps, along with random packetdrops. Recursion equation for importance weights is derived under the presenceof random delays. Offline and online algorithms for identification of theunknown latency parameter using the maximum likelihood criterion are proposed.Further, this work explores the conditions which ensure the convergence of theproposed particle filter. Finally, two numerical examples concerning problemsof non-stationary growth model and the bearing-only tracking are simulated toshow the effectiveness and superiority of the proposed filter.

Particle Filter for Randomly Delayed Measurements with Unknown Latency Probability