Abstract
In this paper, we address the problem of obtaining optimal deceptive signaling strategies between two agents, a sender and a receiver, over an ideal channel. Different from classical (cooperative) communication settings, here, the agents select their strategies under two different cost measures. For the case when these costs are quadratic, we analyze the Stackelberg equilibrium, where the sender leads the game by committing his/her strategies beforehand. This is an infinite-dimensional optimization problem, where the sender needs to anticipate the receiver’s reaction while selecting his/her policy within the general class of stochastic kernels. The specific model we adopt for the underlying information of interest is a discrete-time Markov process generated by a vector-valued linear dynamical system, and at each instant, the information is a realization of a square integrable multivariate random vector. Over both finite and infinite horizons, we show the optimality of memoryless, “linear” signaling rules when the receiver uses a Kalman filter to estimate its information of interest. We develop algorithms that deliver the optimal signaling strategies. Numerical analysis shows that the performance of the sender degrades slightly when the receiver uses the best nonlinear estimator even when the information of interest is a Rademacher random variable rather than Gaussian.
M.O. Sayin—This research was supported in part by the U.S. Office of Naval Research (ONR) MURI grant N00014-16-1-2710, and in part by the U.S. Army Research Labs (ARL) under IoBT Grant 479432-239012-191100.
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Notes
- 1.
For notational simplicity, we consider time-invariant matrices A and B; however, the results could be extended to the time-variant case rather straight-forwardly.
- 2.
We use the pronouns “he” and “she” while referring to \(\mathcal {P}_{S}\) and \(\mathcal {P}_{R}\), respectively, only for clear referral.
- 3.
We use the terms “strategy”, “signaling/decision rule”, and “policy” interchangeably.
- 4.
Note that \(\mathcal {M}_{m_a}(\pmb {c})\) is a closed subspace of \(\mathcal {H}^{(m_a)}\), and we have \(\mathcal {H}^{(m_a)} = \mathcal {M}_{m_a}(\pmb {c}) \oplus \mathcal {M}_{m_a}(\pmb {c})^{\perp }\).
- 5.
With a slight abuse of notation, we define \(\mathcal {E}(\pmb {a}\,|\,\pmb {b},\pmb {c}) := \mathcal {E}\left( \pmb {a}\,|\,\begin{bmatrix} \pmb {b}'&\pmb {c}'\end{bmatrix}'\right) \).
- 6.
Note that \(\varSigma _{z,k}\) satisfies (22).
- 7.
We say that two random vectors \(\pmb {a},\pmb {b}\) are uncorrelated if \(\mathbb {E}\{\pmb {a}\pmb {b}' \} = \mathbb {E}\{\pmb {a}\}\mathbb {E}\{\pmb {b}\}'\). We also emphasize that uncorrelatedness is sufficient since the signal (28) is linear in \(\pmb {z}_k\) and \(\pmb {n}_k\).
- 8.
We say that a point in a convex set is an extreme point if it cannot be expressed as a convex combination of any other two points in that set.
- 9.
Note that \(\{\bar{S}_k-F_k\} \in \mathcal {S}\), which ensures that its \(\mathcal {S}\)-norm is bounded.
- 10.
Henceforth, we omit the subscript for notational simplicity.
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A Proof of Lemma 1
A Proof of Lemma 1
In the following, we show each property one by one: Property (i) follows since \(\mathcal {M}_{m_a}(\pmb {b},\pmb {c}) = \mathcal {M}_{m_a}(\pmb {\tilde{b}}^{\perp },\pmb {c})\). Property (ii) follows since
Property (iii) follows since, by Property (i), we have
By taking a closer look at the right-hand-side, we obtain
Since \(\mathbb {E}\{\pmb {\tilde{b}}^{\perp }\pmb {c}' \} = O_{m_a\times m_c}\), it is equivalent to
which follows since the pseudo inverse of a matrix is a weak inverse for the multiplicative semi-group, i.e., \(M^{\dagger }MM^{\dagger } = M^{\dagger }\). Property (iv) follows since \(\mathrm {cov}\{\mathcal {E}(\pmb {a}\,|\,\pmb {b},\pmb {c})\}\) is equal to \(\mathrm {cov}\{\mathcal {E}(\pmb {a}\,|\,\pmb {\tilde{b}}^{\perp },\pmb {c})\}\). By taking a closer look at the right-hand-side, we obtain
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Sayin, M.O., Başar, T. (2019). On the Optimality of Linear Signaling to Deceive Kalman Filters over Finite/Infinite Horizons. In: Alpcan, T., Vorobeychik, Y., Baras, J., Dán, G. (eds) Decision and Game Theory for Security. GameSec 2019. Lecture Notes in Computer Science(), vol 11836. Springer, Cham. https://doi.org/10.1007/978-3-030-32430-8_27
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