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In this paper, we present a location-penalized maximum likelihood (LPML) estimator for bearing only target localization. We develop a new penalized maximum likelihood cost function by transforming the variables of target position and bearings. The new penalized likelihood function can also be recognized as a posterior distribution under a Bayesian framework by penalizing a prior. We give analysis of the asymptotic properties and show that both traditional bearing maximum likelihood (TBML) and LPML estimators are asymptotically efficient estimators. To compare the performances of the TBML and LPML estimators, we analyze the Cramér-Rao lower bound (CRLB) of the two estimators and show that the bound of the LPML estimator is lower than that of the TBML estimator. Extensive simulations are performed. It is observed that the new LPML algorithm consistently outperforms other well-known algorithms. Field experiments are also conducted by applying this method to localize a vehicle using real-world data acquired by an acoustic array sensor network. The new LPML algorithm demonstrates superior performance in all the field experiments.