Half space object classification via incident angle based fusion of radar and infrared sensors

In this paper, we introduce an incident angle based fusion method for radar and infrared sensors to improve the recognition rate of complex targets under half space scenarios, e.g., vehicles on the ground in this paper. For radar sensors, convolutional operation is introduced into the autoencoder, a “winner-take-all (WTA)” convolutional autoencoder (CAE) is used to improve the recognition rate of the radar high resolution range profile (HRRP). Moreover, different from the free space, the HRRP in half space is more complex. In order to get closer to the real situation, the half space HRRP is simulated as the dataset. The recognition rate has a growth more than 7% compared with the traditional CAE or denoised sparse autoencoder (DSAE). For infrared sensor, a convolutional neural network (CNN) is used for infrared image recognition. Finally, we combine the two results with the Dempster-Shafer (D-S) evidence theory, and the discounting operation is introduced in the fusion to improve the recognition rate. The recognition rate after fusion has a growth more than 7% compared with a single sensor. After the discounting operation, the accuracy rate has been improved by 1.5%, which validates the effectiveness of the proposed method.