In this paper, we propose an improved method for estimating position of a target in Multistatic Radar system. The least squares estimator improves localization accuracy in over-determined system under additive Gaussian noise model. In Multistatic radar systems, multiple low cost receivers are used to improve position estimate of target as compared to monostatic radar. However, receivers which are distant to the target experience large error in Time of Arrival (ToA) measurements. This impacts the performance of multistatic radar which utilizes Time Difference of Arrival (TDoA) based multilateration techniques. A more practical case of target Radar Cross Section (RCS) using Swerling-2 model can cause significant variation in Signal to Noise Ratio (SNR) at each receiver based on target aspect angle. The proposed estimator utilizes range difference measurements of receivers which are nearest to the target position within surveillance area. The number of TDoA measurements to be used for estimating target position is determined through analysis. In addition, Two-step weighted least square estimator is built over proposed method to further improve the accuracy of target location. In first step, weights are assigned based on proximity of receivers to target. In second step, the target position estimate from first step is used in error minimizer function. Performance comparison is made via computer simulations for proposed method, Two-step weighted least square estimator and Spherical Interpolation(SI) method. Two different types of geometry of receiver placement with fixed number of receivers and different noise variations are considered for simulation. Also, different factors affecting the performance of localization accuracy are investigated like number of TDoA measurements used, choice of reference receiver and its impact on Cramer-Rao Lower Bound(CRLB). It has been found that the proposed method performs better than SI under different noise conditions.


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    Titel :

    Improved Target Localization in Multistatic Radar Using Least Squares Method


    Beteiligte:
    Mamgain, Reena (Autor:in) / Bapat, Jyotsna (Autor:in)


    Erscheinungsdatum :

    05.03.2022


    Format / Umfang :

    522201 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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