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A novel direct passive localization technique through a single moving arrayis proposed in this paper using the sparse representation of the arraycovariance matrix in spatial domain. The measurement is constructed by stackingthe vectorized version of all the array covariance matrices at differentobserving positions. First, an on-grid compressive sensing (CS) based method isdeveloped, where the dictionary is composed of the steering vectors from thesearching grids to the observing positions. Convex optimization is applied tosolve the `1-norm minimization problem. Second, to get much finer targetpositions, we develop an on-grid CS based method, where themajorization-minimization technique replaces the atan-sum objective function ineach iteration by a quadratic convex function which can be easily minimized.The objective function,atan-sum, is more similar to `0-norm, and more sparsityencouraging than the log-sum function.This method also works more robustly atconditions of low SNR, and fewer observing positions are needed than in thetraditional ones. The simulation experiments verify the promises of theproposed algorithm.

Off-grid Multi-Source Passive Localization Using a Moving Array