GPS kriegt man genauer - aber (für sich genommen) nur mit stationärem Referenzsignal.
Beide Stationen (mobil + stationär) kann man per BT oder WiFi oder XBee miteinander kommunizieren lassen, und nur die Abweichung zum Referenzstandort wird berechnet.
Positionier-Genauigkeit: bis ca. +/- 2cm!
Auch andere Methoden sind denkbar, aber mit extrem hohem Rechenaufwand verbunden, und man braucht dazu am besten feste, bekannte, externe Referenzpunkte (Baken, Wände ect., wobei man durchaus auch GPS u/o Kompassdaten als "externe Referenzen", wenn auch stark verrauscht, behandeln kann):
Sensor-Fusioning mit GPS, Odometrie, Gyro (am besten ntl IMU) , 3D-Accelerometer und Kompass,
dazu stochastische Filter, entweder EKF (Extended Kalman Filter) oder SMC (Sequenzielle Monte Carlo Methoden = Partikelfilter).
Auszug:
Example application
As an example application, consider the problem of determining the precise location of a truck. The truck can be equipped with a GPS unit that provides an estimate of the position within a few meters. The GPS estimate is likely to be noisy; readings 'jump around' rapidly, though always remaining within a few meters of the real position. In addition, since the truck is expected to follow the laws of physics, its position can also be estimated by integrating its velocity over time, determined by keeping track of wheel revolutions and the angle of the steering wheel. This is a technique known as dead reckoning. Typically, dead reckoning will provide a very smooth estimate of the truck's position, but it will drift over time as small errors accumulate.
In this example, the Kalman filter can be thought of as operating in two distinct phases: predict and update. In the prediction phase, the truck's old position will be modified according to the physical laws of motion (the dynamic or "state transition" model) plus any changes produced by the accelerator pedal and steering wheel. Not only will a new position estimate be calculated, but a new covariance will be calculated as well. Perhaps the covariance is proportional to the speed of the truck because we are more uncertain about the accuracy of the dead reckoning estimate at high speeds but very certain about the position when moving slowly. Next, in the update phase, a measurement of the truck's position is taken from the GPS unit. Along with this measurement comes some amount of uncertainty, and its covariance relative to that of the prediction from the previous phase determines how much the new measurement will affect the updated prediction. Ideally, if the dead reckoning estimates tend to drift away from the real position, the GPS measurement should pull the position estimate back towards the real position but not disturb it to the point of becoming rapidly changing and noisy.
Kalman filter - Wikipedia (hier wird auch die Mathematik besser hergeleitet als auf der deutschen Wiki-Seite, wie ich finde).
Wie das professionelle Systeme machen, kann man hier im Anhang erkennen (2 unabhängige Extended Kalman Filter, je nach Beschaffenheit des Untergrunds):