2results about How to "Reduce verification costs" patented technology

This invention discloses a piecewise adaptive Kalman track tracking method and apparatus based on residual classification, relating to the field of radar track tracking technology. Addressing the problems of poor adaptability to non-stationary measurement noise environments, parameter dependence on human experience, and complex FPGA implementation in existing track tracking methods, this invention proposes introducing a residual classification decision and piecewise adaptive noise adjustment mechanism into the Kalman filter recursive framework. First, a residual vector is calculated based on the predicted state and measurement data. Then, a classification decision is made using the residual vector, the predicted covariance, and the measurement noise covariance of the previous cycle, outputting the measurement quality level. Next, the measurement noise covariance is updated piecewise adaptively based on the measurement quality level. Finally, the filter gain is calculated based on the updated measurement noise covariance and the predicted covariance to complete the correction and update of the state and covariance.

This invention discloses a simulation method for behavior analysis and intelligent risk early warning based on multimodal information fusion. First, a virtual 3D scene with precise physical properties and programmably controllable environmental parameters is constructed. Second, multimodal raw data streams are synchronously generated within the scene and processed using a physical degradation model based on set environmental parameters to generate data highly consistent with the real world. Third, the degraded data stream is fed into the intelligent system under test (SUT) in real time and automatically compared with the precise ground truth labels generated by the simulation, constructing a closed-loop simulation and performance evaluation system. This method can deeply verify the effectiveness of modules such as environmental perception, dynamic weight adjustment, and intelligent fusion decision-making within the SUT, and supports large-scale automated testing and parameter optimization, thereby systematically improving the system's environmental adaptability, recognition accuracy, and decision robustness under harsh conditions such as low light and high noise.