Efficient multilateration tracking with concurrent offset estimation using stochastic filtering techniques

Multilateration systems operate by determining distances between a signal transmitter and a number of receivers. In aerial surveillance, radio signals are emitted as Secondary Surveillance Radar (SSR) by the aircraft, representing the signal transmitter. A number of base stations (sensors) receive the signals at different times. Most common approaches use time difference of arrival (TDOA) measurements, calculated by subtracting receiving times of one receiver from another. As TDOAs require intersecting hyperboloids, which is considered a hard task, this paper follows a different approach, using raw receiving times. Thus, estimating the signal's emission time is required, captured as a common offset within an augmented version of the system state. This way, the multilateration problem is reduced to intersecting cones. Estimation of the aircraft's position based on a nonlinear measurement model and an underlying linear system model is achieved using a linear regression Kalman filter [1, 2]. A decomposed computation of the filter step is introduced, allowing a more efficient calculation.