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首页> 外文期刊>IEEE Transactions on Signal Processing >Bayesian detection and estimation of cisoids in colored noise
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Bayesian detection and estimation of cisoids in colored noise

机译：贝叶斯检测和彩色噪声中的类固醇估计

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摘要

The problem of estimating the number of cisoids in colored noise is addressed. It is assumed that the noise can be modeled by an autoregression whose order has also to be estimated. A new criterion is proposed for estimating the number of cisoids and the autoregressive model order, as well as a new algorithm for estimating the cisoidal frequencies. In the derivation, a Bayesian methodology and subspace decomposition are employed. The proposed criterion significantly outperforms the popular MDL and AIC as applied in a paper by Nagesha and Kay. In addition, an algorithm that reduces the computational complexity of the solution is developed, computer simulations that demonstrate the performance of the criterion are included.
机译：解决了估计有色噪声中的cisoids数量的问题。假设噪声可以通过自回归建模，其阶数也必须估算。提出了一个新的准则来估计类固醇的数量和自回归模型的阶数，以及一个新的算法来估计类固醇的频率。在推导中，采用贝叶斯方法和子空间分解。拟议的标准明显优于Nagesha和Kay在论文中应用的流行的MDL和AIC。另外，开发了一种减少解决方案计算复杂性的算法，并包括了证明该标准性能的计算机仿真。

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