Time-varying Filter Parameter Estimation Model for Millimeter Wave Radar Roadside Traffic Monitoring

With the development of autonomous driving and intelligent transportation， tracking algorithms have become a hot issue. This paper is mainly aimed at predicting the parameters of the Generalized Labeled Multi-Bernoulli Filter （GLMB） in the Millimeter wave radar roadside traffic scene to ensure the performance of the filter in the time-varying traffic monitoring. Parameter selection is one of the main factors that restrict the performance of the filter， and it is of great significance to master its characteristics. Traditional tracking filtering uses a fixed set of parameters in a specific scene. When the scene changes， the filter parameters cannot be tuned in time， resulting in reduced tracking performance. In response to this problem， this paper introduces the Long Short Term Memory network （LSTM） into the field of GLMB filter parameter estimation， and trains the neural network through data to enable it to estimate the filter parameters. Use Millimeter wave radar data to construct a data set to train the neural network. After the training is completed， the test data will be used to obtain the neural network's parameter prediction results. The verification results of radar measured data in different traffic scenarios show that compared with the artificially set fixed parameter method， this method can enable the filter to estimate and tune the parameters in time in time-varying traffic monitoring， and improve the GLMB filter target tracking Performance.