Abstract

This paper investigates the problem of simultaneously locating the moving target and refining erroneous antenna parameters with a calibration object in distributed multiple-input multiple-out (MIMO) radars. Albeit traditional methods achieve moving target localization with antenna parameter errors, they still have some disadvantages, such as the high computational complexity of joint localization or the high noise sensitivity of estimating the target parameters. To overcome the drawbacks of existing algorithms, we first formulate a new localization model that joint target and antenna localization with a calibration object and then present an algebraic solution. Unlike previous work, the proposed estimator has additional unknowns of antenna parameter errors and presents new matrix equations by incorporating the calibration measurements. The proposed method is shown theoretically to attain the Cramer–Rao lower bound (CRLB) accuracy both in theory and simulations under small measurement noise and antenna error levels. Furthermore, simulation results show that the proposed method significantly improves the localization performance compared with existing methods.

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