Enhancing the performance of the Primary Surveillance Radar using Multilateration

One way to improve the measurements of the PSR (Primary Surveillance Radar) is to utilize the cinematic model of the aircraft (A/C) in a Kalman filter. Another newly developed method would be to implement multilateration using a large number of ground-based ADS-B (Automatic Dependent Surveillance-Broadcast) receivers. Originating in airport surveillance, multilateration grew to become the primary system for ATM (Air Traffic Management) in airspaces without PSR coverage. Given that each of the systems has its own advantages and limitations, we propose an evaluation of an alternative approach that uses data from multiple ADS-B receivers to implement a data fusion algorithm between PSR acquired position and MLAT (Multilateration) estimated position. Among the many ways to implement data fusion, have chosen to analyze two possible solutions: the direct fusion of the two available positions provided by the two systems using a traditional Kalman Filter and a linearization approach for the multilateration solution that does not require position computation. In both cases, these will improve the Kalman filter and lower the position estimation errors. The evaluation takes into consideration the possible sources of inaccuracies and provides sensibility analyses in regards to the number and positioning of ADS-B receivers involved in multilateration. This paper will conclude with a discussion of the computational power required for the two implementations.