New Pre-Print (Inverse Filtering)

You can now find a pre-print of our latest work Inverse Filtering for Hidden Markov Models with Applications to Counter-Adversarial Autonomous Systems, in which we provide important extensions to the inverse filtering algorithms proposed in our earlier NeurIPS paper.

By observing, or intercepting, posterior distributions from a Bayesian filter, we seek to estimate i) the model of the dynamic system, ii) the accuracy of the sensors and iii) the measured observations.

We also discuss the design of counter-adversarial systems. As in our ICASSP’20 paper, an enemy:

1. measures our current state via a noisy sensor,
2. computes a posterior estimate (belief) and
3. takes an action that we can observe.

Based on observations of the enemy’s actions and knowledge of our own state sequence, we estimate the accuracy of the enemy’s sensors.

Have a look, and feel free to send me any comments you may have by email!