Abstract:

In the real-world positioning environment， observation stations are often set on mobile platforms， leading to measurement noise errors in the motion state information of the target to be measared as detected by the observation stations. These errors can affect the information received by the observation stations， thereby causing significant deviations in the estimation of the target source's position. To address this issue， a time-difference frequency-difference （TDOA/FDOA） multi-aircraft passive localization algorithm is proposed， which fusion the attention mechanism （AM）， convolutional neural network （CNN）， and bidirectional long short-term memory （BiLSTM） netwrok under observation station parameter errors. Meanwhile， which integrates an improved two-step weighted least squares method and a CNN-BiLSTM-Attention model. By correcting measurement values and improving the issue of the estimation performance degradation of the two-step method in the presence of observation noise， the accuracy of localization is improved. The results of the simulation comparisons show that the algorithm proposed in this paper demonstrates good performance in the presence of observation noise.

Key words: passive localization, time-difference frequency-difference, bidirectional long short-term memory network, attention mechanism, two-step weighted least squares