Radar Error Correlation Analysis Under the Simultaneous Observation of Double Targets

Radar error is a significant factor restricting the detection accuracy of radar. Improving the detection accuracy of radar is crucial to accurate target localization in an ocean environment. With a large amount of measured data, this paper presents the correlation analysis of radar errors, and gives the assumptions needed for radar error modeling. This provides theoretical support for calibrating the modeling method based on the radar error in the simultaneous observation of double targets. First, three error correction models were developed with constant, first-order, and second-order fitting methods, based on the origin and transfer of error in the radar system and the principles of error elimination and reduction in a cooperative platform. Subsequently, the advantages of using a cooperative platform including information sharing were used to detail the collection and preliminary processing of measured radar data as well as the establishment of datasets. Ultimately, the measured radar data were analyzed considering the random error of radar, the strategy for the selection of the optimal error correction model, and the detection error correlation of double targets. In particular, the second-order fitting model with the strongest azimuthal systematic error correlation calculated the root mean square error, which was 32.07% higher compared to the other two models. As revealed in the results of the error analysis, the random error of radar does not agree with the vector superposition characteristic, and it is strongly correlated with the system error of a single radar system that observes different targets within a short period. This provides a way for resolving some problems such as accurate target localization and time variance of error in the complex ocean environment.