貝葉斯估計器先驗?zāi)Ｐ蛥?shù)的迭代感知方法
doi: 10.11999/JEIT141012
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2.
(空軍工程大學(xué)信息與導(dǎo)航學(xué)院 西安 710077) ②(94816部隊 莆田 351100)
基金項目:
國家自然科學(xué)基金(61273408, 61302153)和航空創(chuàng)新基金資助課題
Iterated Cognition Method for Prior Model Parameters of Bayesian Estimator
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1.
(School of Information and Navigation, Air Force Engineering University, Xi&rsquo
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2.
(School of Information and Navigation, Air Force Engineering University, Xi&rsquo
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摘要: 充分利用先驗信息是提高統(tǒng)計推斷性能的有效途徑之一。貝葉斯估計的先驗信息模型參數(shù)必須在設(shè)計階段確定下來，與待探測環(huán)境模型參數(shù)之間必然存在不一致性，從而有可能導(dǎo)致估計質(zhì)量的下降。該文首先給出了基于估計性能的先驗?zāi)Ｐ蛥?shù)感知的一般性框架?；谠摽蚣?，針對白高斯噪聲中直流信號的貝葉斯估計器，分析了先驗失配條件下的估計性能，給出了一種先驗?zāi)Ｐ蛥?shù)迭代感知的算法。利用計算機仿真分析了該估計器性能對先驗?zāi)Ｐ蛥?shù)的敏感性和穩(wěn)健性，分析了不同條件下的迭代感知過程。計算機仿真結(jié)果表明，該文給出的迭代感知方法建立了從估計性能到先驗?zāi)Ｐ蛥?shù)的反饋，通過估計器與待探測場景的多次交互，可以使得先驗?zāi)Ｐ团c當(dāng)前場景模型匹配。Abstract: Smart use of prior information is one of effective approaches to improve the performance of Bayesian estimator. At the design stage of Bayesian estimator, the prior model parameters must be specified, but these parameters may not be identical with parameters of environment at the applicant stage. The mismatched prior model can result to the performance degradation of Bayesian estimator. In this paper, a general framework of prior model parameters cognition based on the estimator performance is given at first. Base on the framework, for a Bayesian estimator of DC signal in WGN, the estimation performance is analyzed, and an iterated cognition method of prior model parameters is proposed. The computer simulation is used to analyze the sensitivity and robustness of the estimator under the mismatched prior model condition, and the iterated cognition procedure under different conditions. The computer simulation results show that, the feedback from the estimation performance to the prior model parameters is obtained with the cognitive method proposed in this paper, and the prior model can be matched with the current environment model after the repeated interactions between the estimator and environment.
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Key words:
- Radar signal processing /
- Bayesian estimator /
- Mismatched prior /
- Robustness /
- Sensitivity /
- Iterated cognitive method
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