This paper deals with the problem of censoring outliers in a class of complex multivariate elliptically contoured distributed radar data, which is a vital issue in radar signal processing applications, such as adaptive radar detection and space-time adaptive processing. The maximum likelihood (ML) estimate of the outlier subset is derived resorting to the generalized likelihood function (GLF) criterion. Since the ML estimate involves the solution of a combinatorial problem, a reduced complexity but approximate ML (AML) procedure is also considered. At the analysis stage, the performance of the AML method is evaluated in the presence of both simulated and real radar data, also in comparison with the conventional generalized inner product (GIP) and the reiterative censored GIP (RCGIP) algorithms. The results highlight that the AML technique achieves a satisfactory performance level and can outperform both GIP and RCGIP in some situations.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Censoring Outliers in Radar Data: An Approximate ML Approach and its Analysis


    Contributors:


    Publication date :

    2019-04-01


    Size :

    4676693 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Censoring distributed nonlinear state estimates in radar networks

    Conte, Armond S. / Niu, Ruixin | SPIE | 2016


    Detecting Outliers in Cell Phone Data

    Horn, Christopher / Klampfl, Stefan / Cik, Michael et al. | Transportation Research Record | 2014


    A neural approach to zoom-lens camera calibration from data with outliers

    Ahmed, M. / Farag, A. | British Library Online Contents | 2002


    Data Censoring in Renewable Energy Enabled Wireless Sensor Networks

    Yang, Miao / Yang, Liu / Zhu, Zhenghang et al. | IEEE | 2019


    SENSOR FUSION WITH CENSORING LIMITS

    Allik, Bethany L. | TIBKAT | 2020