H∞ filtering for discrete-time systems with time-varying delay
Signal Processing
Hyperplane-based vector quantization for distributed estimation in wireless sensor networks
IEEE Transactions on Information Theory
Brief paper: An efficient sensor quantization algorithm for decentralized estimation fusion
Automatica (Journal of IFAC)
Lossless Linear Transformation of Sensor Data for Distributed Estimation Fusion
IEEE Transactions on Signal Processing
SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using the Sign of Innovations
IEEE Transactions on Signal Processing
Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case
IEEE Transactions on Signal Processing
Multi-sensor optimal information fusion Kalman filter
Automatica (Journal of IFAC)
Optimal linear estimation fusion .I. Unified fusion rules
IEEE Transactions on Information Theory
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This paper is concerned with the distributed H"~ fusion filtering problem (DHFFP) for a class of networked multi-sensor fusion systems with communication bandwidth constraints. Due to the limited bandwidth, only finite-level quantized sensor messages are sent to the fusion center, and multiple finite-level logarithmic quantizers are introduced to describe the above quantization strategy. In this sense, the DHFFP is inherent the co-design of the fusion parameters and quantization parameters. With the aid of the discrete-time bounded real lemma, the co-design problem is converted into a convex optimization problem over all the aforementioned parameters, which can be easily solved by standard software packages. It turns out that the performance of the designed distributed fusion filter is superior to that of each local quantized estimate. Finally, a numerical example is given to show the effectiveness of the proposed method.