{"id":991,"date":"2018-08-17T13:16:33","date_gmt":"2018-08-17T21:16:33","guid":{"rendered":"http:\/\/depts.washington.edu\/uwrainlab\/?page_id=991"},"modified":"2018-08-22T17:10:09","modified_gmt":"2018-08-23T01:10:09","slug":"large-scale-distributed-kalman-filtering-via-an-optimization-approach","status":"publish","type":"page","link":"https:\/\/depts.washington.edu\/uwrainlab\/large-scale-distributed-kalman-filtering-via-an-optimization-approach\/","title":{"rendered":"Large-scale distributed Kalman filtering via an optimization approach"},"content":{"rendered":"

M. Hudoba de Badyn, M. Mesbahi<\/strong><\/p>\n

Proc. of the 2017 IFAC World Congress<\/strong><\/p>\n

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Large-scale distributed systems such as sensor networks, often need to achieve filtering and consensus on an estimated parameter from high-dimensional measurements. Running a Kalman filter on every node in such a network is computationally intensive; in particular the matrix inversion in the Kalman gain update step is expensive. In this paper, we extend previous results in distributed Kalman filtering and large-scale machine learning to propose a gradient descent step for updating an estimate of the error covariance matrix; this is then embedded and analyzed in the context of distributed Kalman filtering. We provide properties of the resulting filters, in addition to a number of applications throughout the paper.<\/p>\n<\/div>\n<\/div>\n<\/div>\n