This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber
seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor
has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber
seismic sensor network for target detection and classification. However, ground target recognition based on
seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and
complicated real life application environment. To solve these dificulties, we study robust feature extraction
and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used.
An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets
comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation
result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S
evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance
of the system. A field experiment is organized to test the performance of fiber sensor network and gather real
signal of targets for classification testing.