A plurality of sensors observe an object, and the raw sensor data is processed to produce evidence signals representative of characteristics which may be used to classify the object as to type. The evidence from the plurality of sensors is fused to generate fused or combined evidence. Thus, the fused evidence is equivalent to signals produced by a virtual sensor. The fused evidence is applied to a taxonomic classifier to determine the object type.

A plurality of sensors observe an object, and the raw sensor data is processed to produce evidence signals representative of characteristics which may be used to classify the object as to type. The sensor response characteristics from the plurality of sensors are fused to generate fused or combined sensor response characteristics. Thus, the fused or combined sensor response characteristics are equivalent to the sensor response characteristics of a virtual sensor. The evidence and fused sensor response characteristics are applied to a taxonomic classifier to determine the object type.

The invention relates to a method for an analysis tool for analysis of the sensor performance of a system of sensors, which method comprises analytical calculation of a sensor system's measurement characteristics at each point in a given geographical area. The method comprises obtaining performance parameters from N sensors that are in the system. The method is characterized in that a set of analytical performance parameters for the system is calculated by the performance parameters being fused irrespective of the different measurement characteristics of the sensors in the system with regard to the given performance parameters and in that the analytical parameters are used in the analysis of the performance of the sensor system. The invention also relates to a device for use of the method and to the use of the method and the device.

A multi-sensor data fusion system and method provides adaptive weighting of the contributions from a plurality of sensors in the system using an additive calculation of a sensor reliability function for each sensor. During a predetermined tracking period, data is received from each individual sensor in the system and a sensor reliability function is determined for each sensor based on the SNR (signal-to-noise ratio) for the received data from each sensor. Each sensor reliability function is individually weighted based on the SNR for each sensor and a comparison of predetermined sensor operation characteristics for each sensor and a best performing (most reliable) sensor. Additive calculations are performed on the sensor reliability functions to produce both an absolute and a relative reliability function which provide a confidence level for the multi-sensor system relating to the correct classification (recognition) of targets and decoys.

A method and system provide a multi-sensor data fusion system capable of adaptively weighting the contributions from each one of a plurality of sensors using a plurality of data fusion methods. During a predetermined tracking period, the system receives data from each individual sensor and each data fusion method is performed to determine a plurality of reliability functions for the system based on combining each sensor reliability function which are individually weighted based on the S/N (signal-to-noise) ratio for the received data from each sensor, and a comparison of predetermined sensor operation characteristics for each sensor and a best performing (most reliable) sensor. The system may dynamically select to use one or a predetermined combination of the generated reliability functions as the current (best) reliability function which provides a confidence level for the multi-sensor system relating to the correct classification (recognition) of targets and decoys.