Efficient Multilateration Tracking with Concurrent Offset Estimation using Stochastic Filtering Techniques

Multilateration systems operate by deter- mining distances between a signal transmitter and a number of receivers. In aerial surveillance, radio sig- nals 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 dif- ference 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 ap- proach, using raw receiving times. Thus, estimating the signal's emission time is required, captured as a com- mon offset within an augmented version of the system state. This way, the multilateration problem is reduced to intersecting cones. Estimation of the aircraft's posi- tion based on a nonlinear measurement model and an underlying linear system model is achieved using a lin- ear regression Kalman filter [1, 2]. A decomposed com- putation of the filter step is introduced, allowing a more efficient calculation.