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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >A recursive multiple model approach to noise identification
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A recursive multiple model approach to noise identification

机译：递归多模型噪声识别方法

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摘要

Correct knowledge of noise statistics is essential for an estimator or controller to have reliable performance. In practice, however, the noise statistics are unknown or not known perfectly and thus need to be identified. Previous work on noise identification is limited to stationary noise and noise with slowly varying statistics only. An approach is presented here that is valid for nonstationary noise with rapidly or slowly varying statistics as well as stationary noise. This approach is based on the estimation with multiple hybrid system models. As one of the most cost-effective estimation schemes for hybrid system, the interacting multiple model (IMM) algorithm is used in this approach. The IMM algorithm has two desirable properties: it is recursive and has fixed computational requirements per cycle. The proposed approach is evaluated via a number of representative examples by both Monte Carlo simulations and a nonsimulation technique of performance prediction developed by the authors recently. The application of the proposed approach to failure detection is also illustrated.
机译：正确的噪声统计知识对于估算器或控制器具有可靠的性能至关重要。但是，实际上，噪声统计信息是未知的或不是完全已知的，因此需要进行识别。先前关于噪声识别的工作仅限于静态噪声和仅具有缓慢变化的统计信息的噪声。这里介绍一种方法，该方法对于统计量快速变化或缓慢变化的非平稳噪声以及平稳噪声均有效。该方法基于多个混合系统模型的估计。作为混合系统中最具成本效益的估计方案之一，这种方法使用了交互多模型（IMM）算法。 IMM算法具有两个理想的属性：它是递归的，并且每个周期具有固定的计算要求。通过蒙特卡罗模拟和作者最近开发的性能预测的非模拟技术，通过许多代表性示例对提出的方法进行了评估。还说明了所提出的方法在故障检测中的应用。

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