Kalman filtering with intermittent observations

By Bruno Sinopoli, Luca Schenato, Massimo Franceschetti, Kameshwar Poolla, Michael I. Jordan, S. Sastry. IEEE Transactions on Automatic Control, 2004

2,543 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/28e51cf4bb4c4491c43c04d8307694db549f477a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Distributing the Kalman Filter for Large-Scale Systems (2007, methodology): “over arbitrary communication networks is discussed in [7], using consensus protocols in [8]. References [ 9 ], [4]”
Wireless Network Design for Control Systems: A Survey (2017, methodology): “Kalman filter is one of the most popular approaches to obtain the estimated plant states for NCS [65].”
Hedge Fund Leverage (2011, positive): “This extreme form of measurement error follows Sinopoli et al. (2004) and others and effectively eliminates observations which are observed from the set of measurement equations.”
Stochastic Event-Triggered Sensor Schedule for Remote State Estimation (2013, positive): “Furthermore, the posterior estimate no longer equals to the prior estimate like (13) in [26] but a scaled prior estimate with a coefficient depending on the modified Kalman gain.”
Event-Triggered Feedback in Control, Estimation, and Optimization (2010, background): “Early work on this problem focused on characterizing the impact that intermittently received observations had on the performance of the estimator [68, 47].”
Recent Advances on Recursive Filtering and Sliding Mode Design for Networked Nonlinear Stochastic Systems: A Survey (2013, neutral): “[16] B.”
SINR-Based DoS Attack on Remote State Estimation: A Game-Theoretic Approach (2017, background): “whereP is the steady-state error covariance given by the unique positive semidefinite solution of g̃ ◦ h(X) = X (see [15]).”
Distributed Kalman filtering for sensor networks (2007, background): “Moreover, estimation issues in wireless networks with packet-loss have been the center of much attention lately [19], [7], [18].”
On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage (2006, methodology): “For the convergence, proof is similar to the one of Theorem 1 in (Sinopoli et al. 2004).”
Fusion of PMU and SCADA Data for dynamic state estimation of power system (2015, background): “Alternately we can find these covariances empirically [23].”
Event-based synchronisation of linear discrete-time dynamical networks (2015, methodology): “[46] Let ε > 0, R ∈ Rm×m and R 0.”
Kalman Filtering Over a Packet-Dropping Network: A Probabilistic Perspective (2010, background): “Following the spirit of [8], Liu and Goldsmith [10] extended the idea to the case where there are multiple sensors and the packets arriving from different sensors are dropped independently.”
Distributed Collaborative Control for Industrial Automation With Wireless Sensor and Actuator Networks (2010, methodology): “In [31], Kalman-filtering-based estimation over a lossy wireless channel is analyzed, and the bounds of packet loss rate for estimation convergency are obtained.”
Optimal LQG control across packet-dropping links (2007, neutral): “The nominal system is shown in Figure 1, where the links”
Secure control against replay attacks (2009, background): “study the impact of random packet drops on controller and estimator performance [6] [7].”
Distributed containment control of multi‐agent systems with general linear dynamics in the presence of multiple leaders (2013, methodology): “The following lemma shows the existence of solutions for the MARE.”