In this paper, an optimization technique is developed for providing the globally optimal solution to the maximum likelihood (ML) method for the bearing-only geolocation problem. The ML formulation does not require the a priori knowledge of the distances between the sensor and the emitter. It is formulated as a non-concave fractional programming problem, and a branch and bound algorithm is developed for solving for the globally optimal solution. The algorithm has the property of global convergence and the advantage of computational efficiency. Computer simulations are used to demonstrate the performance of the proposed techniques and comparisons to other methods and the Cramer-Rao lower bounds (CRLBs) are also provided.


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    Title :

    A Globally Optimal Solution to Maximum Likelihood Bearing-Only Geolocation


    Contributors:


    Publication date :

    2017-09-01


    Size :

    214025 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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