CFAR assessment of covariance matrix estimators for non-Gaussian clutter
نویسندگان
چکیده
منابع مشابه
Performance analysis of two structured covariance matrix estimators in compound-Gaussian clutter
In this work we present a thorough performance analysis of two algorithms for estimating Toeplitz covariance matrices, the structured sample covariance matrix estimator (SCME) and the structured normalised SCME (NSCME), which are employed by adaptive radar detectors against Gaussian and compound-Gaussian clutter. Performance predictions are checked with real-life sea clutter data. ( 2000 Elsevi...
متن کاملMultistatic adaptive CFAR detection in non-Gaussian clutter
This work addresses the problem of target detection for multistatic radars. We propose an algorithm that is able to keep constant the false alarm rate, when the disturbance samples associated with each receiver-transmitter pair are distributed according to a compound Gaussian model. The performance of the proposed detection algorithm are analysed to assess the impact of clutter diversity on det...
متن کاملAnalysis of the Influence of Non- Stationarity of Sea Clutter on Covariance Matrix Estimation and Its Impact on Cfar Detection
In this report we describe our analysis of the influence of sea clutter non stationarity on the clutter covariance matrix estimation and its impact on the CFAR property of the normalized adaptive matched filter (NAMF). Three estimators have been considered in the analysis, i.e. the sample covariance matrix (SCM), the normalized sample covariance matrix (NSCM), and the fixed point (PF) estimator...
متن کاملOcean Clutter Modeling for Ship Detection
This work addresses the problem of covariance matrix estimation for ocean clutter modeling. For ship detection based on polarimetric synthetic aperture radar (PolSAR) imagery and constant false alarm rate (CFAR) detectors, accurate ocean clutter modeling is essential. The covariance matrix provides all the polarimetric information of the ocean clutter and its estimate is always involved in PolS...
متن کاملImproved Covariance Matrix Estimators for Weighted Analysis of Microarray Data
Empirical Bayes models have been shown to be powerful tools for identifying differentially expressed genes from gene expression microarray data. An example is the WAME model, where a global covariance matrix accounts for array-to-array correlations as well as differing variances between arrays. However, the existing method for estimating the covariance matrix is very computationally intensive a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Science China Information Sciences
سال: 2010
ISSN: 1674-733X,1869-1919
DOI: 10.1007/s11432-010-4080-z