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Consensus+Innovations Distributed Kalman Filter with Optimized Gains
Information Theory · Submitted 19 May 2016
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Through numerical evaluations, we show that, the distributed estimator with optimal gains converges faster and with approximately 3dB better mean-squared error performance than previous distributed estimators.
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The abstract, sentence by sentence
In this paper, we address the distributed filtering and prediction of time-varying random fields represented by linear time-invariant (LTI) dynamical systems. The field is observed by a sparsely connected network of agents/sensors collaborating among themselves. We develop a Kalman filter type consensus+innovations distributed linear estimator of the dynamic field termed as Consensus+Innovations Kalman Filter. We analyze the convergence properties of this distributed estimator. We prove that the mean-squared error of the estimator asymptotically converges if the degree of instability of the field dynamics is within a pre-specified threshold defined as tracking capacity of the estimator. The tracking capacity is a function of the local observation models and the agent communication network. We design the optimal consensus and innovation gain matrices yielding distributed estimates with minimized mean-squared error. Through numerical evaluations, we show that, the distributed estimator with optimal gains converges faster and with approximately 3dB better mean-squared error performance than previous distributed estimators.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In this paper, we address the distributed filtering and prediction of time-varying random fields represented by linear time-invariant (LTI) dynamical systems. | background 0.88 | 0.23 | 0.03 | asserted 0.90 |
| s1 | The field is observed by a sparsely connected network of agents/sensors collaborating among themselves. | background 0.97 | 0.12 | 0.02 | asserted 0.99 |
| s2 | We develop a Kalman filter type consensus+innovations distributed linear estimator of the dynamic field termed as Consensus+Innovations Kalman Filter. | contribution 0.86 | 0.96 | 0.09 | asserted 1.00 |
| s3 | We analyze the convergence properties of this distributed estimator. | contribution 0.91 | 0.95 | 0.04 | asserted 0.69 |
| s4 | We prove that the mean-squared error of the estimator asymptotically converges if the degree of instability of the field dynamics is within a pre-specified threshold defined as tracking capacity of the estimator. | result 0.96 | 0.94 | 0.39 | asserted 1.00 |
| s5 | The tracking capacity is a function of the local observation models and the agent communication network. | result 0.63 | 0.69 | 0.05 | asserted 1.00 |
| s6 | We design the optimal consensus and innovation gain matrices yielding distributed estimates with minimized mean-squared error. | contribution 0.56 | 0.96 | 0.15 | asserted 0.99 |
| s7 | Through numerical evaluations, we show that, the distributed estimator with optimal gains converges faster and with approximately 3dB better mean-squared error performance than previous distributed estimators. | result 1.00 | 0.96 | 0.52 | asserted 1.00 |
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The paper
Original abstract
In this paper, we address the distributed filtering and prediction of time-varying random fields represented by linear time-invariant (LTI) dynamical systems. The field is observed by a sparsely connected network of agents/sensors collaborating among themselves. We develop a Kalman filter type consensus+innovations distributed linear estimator of the dynamic field termed as Consensus+Innovations Kalman Filter. We analyze the convergence properties of this distributed estimator. We prove that the mean-squared error of the estimator asymptotically converges if the degree of instability of the field dynamics is within a pre-specified threshold defined as tracking capacity of the estimator. The tracking capacity is a function of the local observation models and the agent communication network. We design the optimal consensus and innovation gain matrices yielding distributed estimates with minimized mean-squared error. Through numerical evaluations, we show that, the distributed estimator with optimal gains converges faster and with approximately 3dB better mean-squared error performance than previous distributed estimators.
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This approach is extended to estimating time-varying random states in [ref] .
Networked estimation under information constraints · Introduction · 0.96
Cites as background 1 paper
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In contrast, in single time-scale approaches [ref] , the agents collaborate with their neighbors only once in between each dynamics/observation evolution.
Networked estimation under information constraints · Introduction · 0.83
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