Abstract: The carrier-free UWB fuze is featured by high distance resolution, strong anti-interference capability, and rich information about target structure. Also, it is not easily affected by light and climate conditions. When striking ground targets, different terrain will affect the blast height of the fuze, which in turn affects the damage effect. A terrain identification system based on carrier-free UWB fuze is thus proposed, which requires rich experimental data for accurate target identification. The acquisition of terrain echoes is time-consuming and costly, and the number of acquired echoes is often limited, which may affect the recognition accuracy. To expand the data set, an improved conditional generation adversarial network is proposed, replacing the fully connected layers of the generator and discriminator with one-dimensional convolution, adding batch normalization to achieve signal generation while reducing pattern collapse, and enhancing the sequence generation effect under small sample conditions. In addition, the wavelet energy features of the expanded echo signals are used as input features, and the particle swarm optimized BP (PSO-BP) neural network is used to achieve intelligent terrain classification. Experimental results show that the PSO-BP neural network trained on the expanded training set has improved the accuracy by more than 4% compared with training on the original training set.

Key words: carrier-freeUWB, terrainrecognition, waveletenergyfeatures, conditionalgenerativeadversarialnets, bpneuralnetwork, particleswarmoptimization