Airborne circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) systems are attractive tools for air-to-ground surveillance and monitoring, because of their advantages of short revisit time and large coverage. This paper proposes an accurate imaging and Doppler chirp rate estimation algorithm for the airborne CSSAR-GMTI system with high range resolution. A key step of this algorithm is to utilize an alternation strategy to implement the correction of the target's range cell migration and the estimation of the range equation's quadratic coefficient alternatively. This step enables the proposed algorithm to achieve an accurate imaging and Doppler chirp rate estimation of a ground moving target. In addition, this paper presents an investigation on the accuracy of the quadratic-approximated range equation for ground moving target imaging. Numerical experiments are conducted to validate the proposed algorithm. INDEX TERMSSynthetic aperture radar, ground moving target indication, ground moving target imaging, Doppler chirp rate estimation. YONGKANG LI (M'19) was born in Hunan, China, in 1988. He received the B.Eng. degree in electronic information engineering and the Ph.D. degree in signal and information processing from

Vehicular Ad-Hoc Networks (VANETs) have received a great attention recently due to their potential and various applications. However, the initial phase of the VANET has many research challenges that need to be addressed, such as the issues of security and privacy protection caused by the openness of wireless communication networks among the city-wide applied regions. Specially, anomaly detection for a VANET has become a challenging problem, due to the changes in the scenario of VANETs comparing with traditional wireless networks. Motivated by this issue, we focus on the problem of anomaly detection in VANETs, and propose an effective anomaly detection approach based on the convolutional neural network in this paper. The proposed approach takes into account the spatio-temporal and sparse features of VANET traffic, and it uses a convolutional neural network architecture and a loss function based on Mahalanobis distance to extract and estimate the traffic matrix. Then, reinforcement learning is used to implement anomaly detection. Furthermore, a comprehensive assessment is provided to validate the proposed approach, which illustrates the effectiveness of this approach.

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