A novel economic impact model is proposed by this paper to analyze the impact of economic downturn on the air quality in Wuhan during the epidemic period, and to explore the effective solutions to improve the urban air pollution. The Space Optimal Aggregation Model (SOAM) is used to evaluate the air quality of Wuhan from January to April in 2019 and 2020. The analysis results show that the air quality of Wuhan from January to April 2020 is better than that of the same period in 2019, and it shows a gradually better trend. This shows that although the measures of household isolation, shutdown and production stoppage adopted during the epidemic period in Wuhan caused economic downturn, it objectively improved the air quality of the city. In addition, the impact of economic factors on PM2.5, SO2 and NO2 is 19%, 12% and 49% respectively calculated by the SOMA. This shows that industrial adjustment and technology upgrading for enterprises that emit a large amount of NO2 can greatly improve the air pollution situation in Wuhan. The SOMA can be extended to any city to analyze the impact of the economy on the composition of air pollutants, and it has extremely important application value at the level of industrial adjustment and transformation policy formulation.

Random Matrix (RM) model is a typical extended objectmodeling method, and has been widely used in extended object tracking. However, the existing RM based filters usually under a assumption that the measurements obey Gaussian distribution, which may lead to the decrease of accuracy when the filter applied to Lidar system. This paper proposed a new observation model and used it to modified a RM smoother by considering the characteristics of Lidar data. Simulation results show that the proposed method achieved better performance compared with the original RM tracker in Lidar system.

The random matrix (RM) model is a typical extended object-modeling method that has been widely used in extended object tracking. However, existing RM-based filters usually assume that the measurements follow a Gaussian distribution, which may lead to a decrease in accuracy when the filter is applied to the lidar system. In this paper, a new observation model used to modify an RM smoother by considering the characteristics of 2D LiDAR data is proposed. Simulation results show that the proposed method achieves a better performance than the original RM tracker in a 2D lidar system.

The Gaussian inverse Wishart probability hypothesis density filter is a promising approach for tracking multiple extended targets. However, if targets are closely spaced and performing maneuvers, the performance of a Gaussian inverse Wishart probability hypothesis density filter will decline. The reason for this is that the measurement partitioning approaches fail to provide accurate partitions, which influences the component updating process directly. This paper presents a modified prediction partitioning algorithm for the Gaussian inverse Wishart probability hypothesis density filter in order to solve the partitioning problem of closely spaced targets. The inaccuracy of the target prediction information occurred by target maneuvers leads to the above problem, thus a modified prediction partitioning algorithm will label the components and corresponding measurements to improve the prediction accuracy. Simulation results show that the use of modified prediction partitioning can improve the performance of a Gaussian inverse Wishart probability hypothesis density filter by providing more accurate partition results when targets are closely spaced and performing maneuvers.