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An improved multi-sensor orderly PHD convolution particle filtering algorithm is proposed to handle multi-sensor multi-target tracking problem under the condition of asynchronous sampling. Firstly, the sampling points in a fusion cycle are mapped to the timeline of the fusion center based on the framework of SMC-PHD. State equations within two adjacent intervals are fused to model the state of the sampling interval based on the sequence of measured value, and the particle weights are updated by convolution particle filter to avoid calculating likelihood function in multi-dimensional measurement space. Finally the state values of targets are extracted by CLEAN technology. Simulation results show CKUPC-PHD has higher robustness and stability in the context of multi-sensor multi-target tracking as compared to SMC-PHD.

With the continuous development of economy and society, the financial market has ushered in its own peak, which has pushed the financial industry as a whole into a new stage. In this environment, the financial investment activities of the public and enterprises have greatly increased and have an important role and impact on the optimal allocation of market funds. Due to the coexistence of risks and interests, the financial investment market will be impacted by high-risk events, which will lead to instability of order and certain economic losses. The operation risk of an enterprise is a huge challenge for its own development. A little carelessness may lead to a sharp decline in the status of the enterprise. Therefore, it is necessary to strengthen financial investment control and risk prediction, which has important practical significance for the benign development of enterprises, improving market competitiveness, and reducing negative impacts. Therefore, this paper adopts the concept of edge computing, and based on the R&D and operation experience of relevant venture capital systems, develops an intelligent system that can predict various risks in the whole process of financial investment. Firstly, the mobile edge computing framework is introduced, followed by the computing advantages. Then, the system architecture is designed around the edge computing, such as designing specific algorithms, so as to complete the overall design of the intelligent financial investment risk prediction system. Then, the results are systematically analyzed through experiments, which show that the system can well predict the financial investment risk. Finally, the strategy of optimizing the intelligent financial risk prediction system is proposed.

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