Chaff is composed of small and thin pieces of glass fibres plated with aluminium or silver and is dispersed in the air like a cloud from air fighters or warships. The length of a chaff fibre is generally much smaller than the radar resolution and its orientation is random in chaff clouds. Therefore, chaff clouds can be viewed as anisotropic needle‐like particles. For such kind of target, according to the polarimetric decomposition theorem, identifying the dominant physical scattering mechanism is the polarimetric scattering parameter, which is a roll‐invariant parameter. As a result, this parameter is introduced to investigate the scattering characteristics of chaff clouds here. Theoretical analysis shows that this parameter of chaff clouds with three typical orientation distributions varies between and . Moreover, when the azimuth angles of chaff clouds meet the uniform distribution, the correlation of chaff clouds between the co‐ and cross‐polarised channels vanishes. To this end, the polarisation scattering characteristics of chaff clouds mentioned above are validated by numerical simulation, and a study is also carried out to compare the chaff clouds with other targets, which confirms the feasibility and real‐time of chaff jamming recognition by support vector machine classification method.

Ship recognition from chaff cloud jamming is challenging since they have similar dimensions and radar cross sections. In this paper, a polarimetric recognition technique with sophisticated polarimetric decomposition is proposed. To this end, a seven-component model-based decomposition is first put forward by integrating three sophisticated scattering models, thus the dominant and local scattering of ships can be characterized accurately. According to the derived scattering contributions, a robust discrimination feature is then designed based on the concept of contrast and suppression. Coupled with the average scattering angle estimated from eigen-based decomposition, the constructed feature vector is inputted into the support vector machine and the recognition is finally fulfilled. The proposed method is tested on simulated and real polarimetric radar data and the results demonstrate that the proposed method achieves the highest recognition rate of over 98%, which outperforms the state-of-the-art methods.

A representative general polarimetric model-based decomposition framework was proposed by Chen et al., which implements a simultaneous full-parameter inversion by using complete polarimetric information and solves several limitations in previous decomposition methods. However, there are still shortcomings in Chen’s work. Firstly, only the real part of the parameter $\beta$ in the generalized surface scattering model is considered. Secondly, inappropriate initial input values may lead to local optima in the nonlinear least squares optimization algorithm. Thirdly, the volume scattering component is underestimated in the volume scattering-dominated scene, but overestimated in buildings with large orientation (LOB) areas. Finally, nonlinear optimization is time-consuming computationally. To overcome those issues, an improved generalized polarimetric model-based decomposition method is proposed in this paper. The imaginary part of the parameter $\beta$ is incorporated into the decomposition framework of the proposed method. Ingeniously utilizing the internal relationship in the generic equations composed of coherent matrix elements, the model parameters can be inversed by simplifying the nonlinear equations to linear equations. Therefore, compared with Chen’s method, the proposed method does not rely on the initial input values, and improves the computational efficiency. In addition, a hierarchical decomposition scheme is presented to solve the problem of underestimation or overestimation of volume scattering component mentioned above. The performance and advantages of this method are evaluated with L-band and C-band polarimetric synthetic aperture radar (PolSAR) data sets. Comparison studies are carried out with other model-based decomposition methods, demonstrating that the proposed method can further improve decomposition performance, especially in LOB areas.

Limited by multiple factors, such as land use planning, landscape coordination and environmental standards, the ventilation conditions of the urban highway tunnels are insufficient, and therefore the joint control of ventilation and purification becomes a necessary solution. In this study, the effect of number, location of decentralized purification equipment, and the tunnel ventilation rate on pollution in the tunnel are studied using one-dimensional calculation. The results show that the arrangement of purification equipment can reduce the design ventilation rate to a certain extent. When purification equipment is arranged, the maximum NO2 concentration can be reduced by up to 30.9%, and the average NO2 concentration can be reduced by up to 18.2% compared with that without purification. The purification equipment arranged in the rear section of the tunnel is conducive to reducing the concentration of pollutants at the exit, while the arrangement in the middle section of the tunnel is conducive to reducing the average concentration of pollutants in the tunnel.