Detection and diagnosis of radar modeling errors using covariance consistency

Often, detection-based tracking algorithms are developed without much regard for the effects of either the radar´s analog signal processing or its digital signal-processing algorithms. In this paper, we combine the effects of the radar´s signal processing and tracking algorithms to assess the combined effect on covariance consistency of various algorithms. To do this, we first define the terms detection, detection primitive, and measurement. Next, we provide a detailed dataflow diagram for the processing chain of an electronically-scanned radar so that we can examine the propagation of truth data through various coordinate frames relative to radar signal processing. We examine issues related to expressing truth data in different frames and different relationships among targets. We describe in detail many of the algorithms in the signal-processing chain of typical monopulse radar and finally analyze and demonstrate the covariance consistency of various algorithms in the radar processing chain. When properly applied, covariance consistency analysis can be used to detect and correct inconsistent algorithms, invalid assumptions, and coding errors. The techniques described in this paper provide insight in determining system covariance requirements and may be used to ensure that both the design and implementation of radar processing algorithms provide good covariance consistency. The example simulations provide a baseline for algorithm covariance consistency, examine some common approximations used to simplify radar simulations, and demonstrate the effect of implementation errors that actually occurred during model development.