Data fusion method of radar and ADS-B based on track quality assessment

The data fusion of radar and automatic dependent surveillance-broadcast(ADS-B) is the effective method to surveille the 'black flights' and flying birds. However, the tracking performance of the two sensors has large differences and is easy to be fluctuated, which will bring a decline in fusion accuracy. A data fusion method of radar and ADS-B based on track quality assessment is proposed. Firstly, the effects of local track accuracy, data update times and sensor measurement errors on local track quality on corresponding assessment factors are analyzed and quantified. And then, these assessment factors are combined to calculate the quality weighting factors of the local track, and the asynchronous track fusion processing of radar and ADS-B is completed based on the distributed fusion structure. Finally, the feasibility and effectiveness of the proposed method is verified with simulation experiment and application. The results show that the proposed fusion method can effectively improve the fusion accuracy, and the tracking errors are better than the traditional algorithms when the sensor tracking performance fluctuates.