A method of real time magnetic localization comprising: providing an artificial neural network field model that is calibrated and optimized for a predetermined magnet; receiving signals from one or more magnetic sensors; and solving the location of the magnet using the model based on the signals.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of real time magnetic localization comprising: providing an artificial neural network field model that is calibrated and optimized for a predetermined magnet; predicting magnetic fields from the magnet using the model; receiving signals from one or more magnetic sensors, the signals comprising magnetic fields from the magnet as measured by the one or more magnetic sensors; and solving the location of the magnet using the model based on the signals; wherein solving the location of the magnet using the model based on the signals comprises minimizing a cost function comprising differences between the signals and the predicted magnetic fields. 2. The method of claim 1 wherein the location of the magnet is substantially accurately solvable when the one or more magnetic sensors are substantially adjacent the magnet. 3. The method of claim 2 wherein the location accuracy is better than 1 mT (RMSE) and/or a peak absolute error is less than 40 mT when the magnet is within 25 mm distance of a sensor. 4. The method of claim 1 wherein the magnet is part of an in vivo device or a medical instrument configured for insertion into a patient, and the one or more magnetic sensors are provided as a magnetic sensing unit located on or about a patient. 5. The method of claim 1 wherein the model is based on a back propagation neural network field model trained using a Levenberg-Marquardt supervised learning algorithm. 6. The method of claim 1 further comprising selecting an order of the model and/or a number of nodes to reduce the error below a predetermined threshold. 7. The method of claim 6 wherein the order is a single hidden layer and the number of nodes is 5 to 20 hidden nodes. 8. The method of claim 1 further comprising providing a plurality of weighting coefficients for the model, wherein the weighting coefficients are substantially pre-optimized for the predetermined magnet. 9. The method of claim 1 wherein the magnet is passive and untethered. 10. The method of claim 1, wherein the one or more magnetic sensors are part of an in vivo device or a medical instrument configured for insertion into a patient, and a plurality of different magnets are provided on or about a patient. 11. A system for real time magnetic localization of an in vivo device or a medical instrument configured for insertion into a patient comprising: a magnet, one or more magnetic sensors configured to provide signals comprising magnetic fields from the magnet, a processor including data storage with software, the software when executed in the processor configured to solve the location of the magnet relative to the one or more magnetic sensors, or the location of the one or more magnetic sensors relative to the magnet using a neural network field model, based on the signals, by minimizing a cost function comprising differences between the signals and predicted magnetic fields from the model; and a display or indicator to show the real time location of the in vivo device or medical instrument within the patient. 12. The system of claim 11 wherein the magnet is passive and untethered. 13. The system of claim 12 wherein the magnet is an axially magnetized Neodymium annular cylinder permanent magnet. 14. The system of claim 11 wherein the model includes weighting coefficients, an order and/or the number of nodes for the magnet and/or a desired error threshold. 15. The system of claim 11 wherein the one or more magnetic sensors are included in a magnetic sensing unit configured to be located in, adjacent to or on the patient, and the processor is an embedded computer. 16. The method of claim 1 wherein the predicted magnetic fields are in cylindrical coordinates and the signals from the one or more magnetic sensors are in Cartesian coordinates, and wherein the method further comprises converting the signals from the Cartesian coordinates to the cylindrical coordinates prior to minimizing the cost function. 17. The system of claim 11 wherein the predicted magnetic fields are in cylindrical coordinates and the signals provided by the one or more magnetic sensors are in Cartesian coordinates, and wherein the signals are converted from the Cartesian coordinates to the cylindrical coordinates prior to minimizing the cost function.