Proceedings Article | 27 April 2018
KEYWORDS: Error analysis, Complex systems, Sensors, Stochastic processes, Monte Carlo methods, Statistical analysis, Linear filtering, Nonlinear filtering, Solids, Systems modeling
In the field of sensing, a typically unavoidable nuisance is the inherent bias of a sensor due to imperfections in timing, calibration, and other sources. The errors incurred by the bias ripple through higher-level processes such as tracking and sensor fusion, causing varying effects to each operation. In many different applications, such as track-to-track correlation, the overall effect of the biases on state estimation is modeled as a constant, translational shift in the position dimension of the track states. This assumption can be appropriate when the required precision of the track states is not stringent. However, in general, sensor bias can not only affect position estimates but also positional derivatives, i.e., velocity, acceleration, in a manner that can change dramatically depending on sensor-target geometry; for situations where high state estimation accuracy is required, these consequences become apparent and need to be handled. The contribution from measurement bias to state estimation error depends on many different aspects, e.g., measurement uncertainty, dynamic model uncertainty, sensor-target geometry. The focus of this work is the quantification of the relative significance of measurement error and measurement bias in the resultant state estimation error. In short, using the results in this work, it is straightforward to: (i) determine regimes where measurement bias becomes a predominant factor, (ii) bound the impact of the sensor bias on the outputted tracking information, (iii) analyze the dependence of the tracking error on sensor-target geometry, all of which can be of great impact when designing a tracking system architecture.
