The intelligent transportation system (ITS) is inseparable from people’s lives, and the development of artificial intelligence has made intelligent video surveillance systems more widely used. In practical traffic scenarios, the detection and tracking of vehicle targets is an important core aspect of intelligent surveillance systems and has become a hot topic of research today. However, in practical applications, there is a wide variety of targets and often interference factors such as occlusion, while a single sensor is unable to collect a wealth of information. In this paper, we propose an improved data matching method to fuse the video information obtained from the camera with the millimetre-wave radar information for the alignment and correlation of multi-target data in the spatial dimension, in order to address the problem of poor recognition alignment caused by mutual occlusion between vehicles and external environmental disturbances in intelligent transportation systems. The spatio-temporal alignment of the two sensors is first performed to determine the conversion relationship between the radar and pixel coordinate systems, and the calibration on the timeline is performed by Lagrangian interpolation. An improved Hausdorff distance matching algorithm is proposed for the data dimension to calculate the similarity between the data collected by the two sensors, to determine whether they are state descriptions of the same target, and to match the data with high similarity to delineate the region of interest (ROI) for target vehicle detection.

The application scenario of radar and the imaging quality are closely related. In this paper, we propose a distributed MIMO radar array imaging technique for radar imaging resolution, which uses compressed sensing to reconstruct the radar echo signal for imaging, and can improve the imaging quality of MIMO radar. By simulating a single MIMO radar and a distributed MIMO radar model, the results show that the distributed MIMO radar model has higher imaging resolution than a single MIMO radar, especially with higher angular resolution.

At present, MIMO radar is more and more widely used in the field of radar imaging. With its unique multi transmit and multi receive system, after appropriate processing, the radar can be equivalent to a virtual array that can transmit and receive independently at the same time, which is equivalent to increasing the radar aperture and improving the resolution of radar imaging. However, in some cases, when the radar needs to provide high azimuth resolution images under certain conditions, the imaging range and resolution provided by a single radar may not be enough. The distributed multi MIMO radar imaging system model described in this paper is composed of multiple radars with the same system. Under a certain signal processing mode, through timing control, the static distributed multi MIMO radar can realize high-resolution object images in the imaging area at the same time. Under the above conditions, using certain data processing methods, the imaging system proposed in this paper can provide a larger imaging range and higher resolution, especially angular resolution, compared with a single radar.