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In this paper, the observability in BOT problem is analyzed and defined in form of leg by leg, the basic relationship between observability and the number of particles is opened out, and a fast particle filter with variable number of particles which calculated by this relationship is proposed to adopt the observability in BOT to save the computation resource. The simulation results show that the definition of observability accords with the reality and the proposed method decreases the computation time while remaining the tracking quality.

It is difficult to detect and recognize underwater targets of interest due to oceanic ambient noise and signal distortion resulting from the multipath effect. An ideal method to overcome this problem is to accurately estimate original source signals from this kind of complex signal. The number of unknown sources is estimated by principal component analysis using information theoretic criterion. The presented idea shows that the source signals of interest could be reconstructed by a blind source separation technique from the simulated mixed complex underwater signals. The multipath effect in underwater acoustic signal transmission is approximately considered as a convolutive mixture. Experiments based on simulated data achieved satisfactory results.

The change of background's chaos feature when there are moving targets was analysed, and a new algorithm based on Lyapunov exponents of image block for moving targets detection on the sea surface, which is used for ship targets detection on the sea surface, was proposed. The new method defines the term of image gray level distance, extends the calculation method of Lyapunov exponents from single dimension signal to image signal based on the improved Wolf method, uses the change of the largest gray level distance Lyapunov exponents to detect ship targetssubmerged in chaos background. Experimental results show that the sea surface background has chaos feature. The proposed algorithm can detect ship targets effectively and can get better result than statistical analysis.