Compared with the conventional two-step (including parameter estimation and position solution) localization mode

direct position determination (DPD) algorithm has more advantages

such as higher position estimation accuracy

strong resolution capability

etc. However

the performance of DPD algorithm is affected by the array modeling errors. In this paper

the localization deviation for multiple signal classification (MUSIC)-based DPD algorithm is derived quantitatively by using first-order Taylor expansion in the presence of array modeling errors. The result demonstrates that there is a nonlinear relationship between the true location of the radiation source and biased position estimation obtained from MUSIC-DPD

but this relationship cannot be accurately expressed in practice. To solve this problem

we propose the use of a multilayer perceptron (MLP) neural network (NN) for localization deviation correction of DPD. This method can effectively correct the localization deviation of DPD caused by array modeling errors through adaptive learning of NN.