This letter describe target classification from the synthesized active sonar returns from targets. A fractional Fourier transform is applied to the sonar returns to extract shape variation in the fractional Fourier domain depending on the highlight points and aspects of the target. With the proposed features, four different targets are classified using two neural network classifiers.

In multi-static sonar systems, the least square (LS) and maximum likelihood (ML) are the typical estimation criteria for target location estimation. The LS localizaiton has the advantage of low computational complexity. On the other hand, the performance of LS can be degraded severely when the target lies on or around the straight line between the source and receiver. We examine mathematically the reason for the performance degradation of LS. Then, we propose a location adaptive — least square (LA-LS) localization that removes the weakness of the LS localizaiton. LA-LS decides the receivers that produce abnormally large measurement errors with a proposed probabilistic measure. LA-LS achieves improved performance of the LS localization by ignoring the information from the selected receivers.

A computationally efficient time delay and Doppler estimation algorithm is proposed for active sonar with a Linear Frequency Modulated (LFM) signal. To reduce the computational burden of the conventional estimation algorithm, an algebraic equation is used which represents the relationship between the time delay and the Doppler in the cross-ambiguity function (CAF) of the LFM signal. The algebraic equation is derived based on the Fast Maximum Likelihood (FML) algorithm. The use of this algebraic relation enables the time delay and Doppler to be estimated with two 1-D searches instead of the conventional 2-D search.