基于新息的网络化系统高效量化编码通信方法

A Communication-Efficient Approach for Networked Systems via Innovation-Based Quantization

摘要

针对网络化系统中远程状态估计面临的数据精度与通信带宽受限之间的问题，本文研究了远程状态估计中的高效数据传输问题。相较于原始测量数据，新息具有稳定且与系统状态无关的统计特性，使得在满足预期估计精度的同时可实现更高效的数据压缩，从而显著降低通信带宽占用。本文进一步提出一种基于新息的反馈量化方法，该方法在减少通信比特数的同时，保证重构误差可控且可调。进而围绕量化比特数分配问题构建优化模型，在估计精度与通信成本之间实现权衡。最后，自建实车平台实验验证了所提方法的有效性与优势。

Abstract

In large-scale distributed systems such as the Internet of Vehicles (IoV), the conflict between high-frequency data sampling and limited communication bandwidth presents a critical challenge for remote state estimation. Traditional quantization methods often suffer from saturation failures when system states diverge, as observations exceed the dynamic range of the quantizer. To address the issue, this paper proposes an Innovation-Based Encoding-Decoding Quantization (IEQ) method and a Reconstruction-Error-Based Feedback Quantization (REFQ) algorithm. Firstly, the IEQ method constructs a novel type of measurement innovation. Theoretical analysis proves that this innovation is statistically independent of system states and follows a stable distribution. This characteristic fundamentally eliminates quantizer saturation caused by large-amplitude observations. Secondly, to solve the problem of error accumulation in differential encoding, the REFQ algorithm introduces a feedback mechanism. The sender simulates the decoding process to calculate the reconstruction error and corrects the quantization input in real time. Theoretical analysis demonstrates that REFQ strictly constrains reconstruction errors within a single quantization step size. Finally, an optimization-based strategy is developed to allocate quantization bits based on system matrices. Crucially, it is further established that under this bit allocation scheme, the error covariance of the remote Kalman filter remains uniformly bounded, thereby providing a rigorous theoretical guarantee for the stability of the overall estimation system. Experiments are conducted on a self-built real-world vehicle platform to emulate an IoV remote state estimation scenario. The results show that the proposed method reduces bandwidth consumption by 4 times compared to raw data transmission while maintaining estimation accuracy. Moreover, the REFQ algorithm effectively suppresses error drift, keeping reconstruction errors within a stable bound regardless of time steps, whereas traditional methods show divergent errors.

关键词

量化 / 状态估计 / 信息物理系统 / 数据压缩 / 编码

Key words

quantization / state estimation / cyber-physical system / data compression / encoding

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基金资助

国家科技攻关计划(2023YFF1204805)

国家自然科学基金(62336005, 62503178)