This exercise is concerned with filtering in an environment with no landmarks. Consider a vacuum robot in an empty room, represented by an $n \times m$ rectangular grid. The robot’s location is hidden; the only evidence available to the observer is a noisy location sensor that gives an approximation to the robot’s location. If the robot is at location $(x, y)$ then with probability .1 the sensor gives the correct location, with probability .05 each it reports one of the 8 locations immediately surrounding $(x, y)$, with probability .025 each it reports one of the 16 locations that surround those 8, and with the remaining probability of .1 it reports “no reading.” The robot’s policy is to pick a direction and follow it with probability .7 on each step; the robot switches to a randomly selected new heading with probability .3 (or with probability 1 if it encounters a wall). Implement this as an HMM and do filtering to track the robot. How accurately can we track the robot’s path?

**Figure [switching-kf-figure]**A Bayesian network representation of a switching Kalman filter. The switching variable $S_t$ is a discrete state variable whose value determines the transition model for the continuous state variables $\textbf{X}_t$. For any discrete state $\textit{i}$, the transition model $\textbf{P}(\textbf{X}_{t+1}|\textbf{X}_t,S_t= i)$ is a linear Gaussian model, just as in a regular Kalman filter. The transition model for the discrete state, $\textbf{P}(S_{t+1}|S_t)$, can be thought of as a matrix, as in a hidden Markov model.

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