Research Of Networking Technology For Airborne Radar Track Fusion Optimization

If we combine both of them for networking integration,the detection accuracy and anti-stealth of the system can be greatly improved. After importing airborne radar to the common fixed base measurement model of radar network, In addition to the model of radar measurement errors inherent itself also introduce carrier’s location error and Eulor angle errors, In this case the measurement equation is no longer a linear equation and the former solving system error vector solution model fails, we need to find an improved estimation algorithm. Meanwhile, with the increase in the number of radar, networking system communication bandwidth resources becomes very short, and each node in the data transfer process and the fusion center will loss of information. According to those problems, some researches have carried out as follows:1. Considering with the Eulor angle bias which brought by the airborne sensor, an exact solution is provided for the airborne multisensor bias estimation problem based on extended kalman filer(EKF).By using EKF to linearize the non-linear measurement equation, the decoulpling between the target state estimation and the sensor bias estimation is achieved without ignoring or approximating the crosscovariance between the state estimate and the bias estimate, which brings out a new exact airborn sensor bias estimation registration algorithm namely EEX.2. For Network distribution, a coverage area of particle swarm algorithm for radar stations and distribution networks redundant constitute nonlinear equations, the establishment of a distribution station index and track the accuracy of measurement data fusion correspondence between the given station arrangement strategy to be adopted under different scenarios.3. To analyze the relationship between notes transmission rate,we take the example of exact algorithm track to track fusion operating for various information configurations.as the nodes increased fast in the fusion network,an investigation reveals that these is a constrain between the transmission rate means system bandwidth and the fusion accuracy.4. Due to the bandwidth or computational constrains, sometimes we encounter some sensors deliver a truncated local track covariance. This paper analyzed the performance of some popular track to track fusion algorithm under truncated sensor track covariance information and arised a simple alternative method to compensate this information loss.