1. Field of the Invention
The present invention relates to data integration using a weighting factor, and more particularly, to a data integration apparatus and method of combining results from various sensing devices using a weight factor and producing a final sensing result.
2. Description of the Related Art
In a conventional data integration method, a final sensing result is produced using only signals in each sensing device. The conventional data integration method is classified into a decision integration method using AND, OR, and Majority logics and a method using prior probabilities such as Bayesian detection and the Neyman-Pearson test.
In the method using prior probabilities, the existence of signals may not be known in an actual circumstance. Accordingly, the prior probabilities cannot be estimated and used so that the conventional decision integration method is generally used.
In the conventional decision integration method, advantages and disadvantages exist in each of its types. For example, in the decision integration method using AND logic, when some of the sensing devices cannot detect a signal due to an environmental factor such as fading, an error, which is a sensing result finally indicating that a signal does not exist, may be generated. However, the probability of false alarm is low.
In the decision integration method using OR logic, although a hidden terminal problem occurs, when some of the sensing devices determine that a signal exists, it is determined that a signal exists as a final sensing result. However, when a signal does not exist, the probability of false alarm increases due to a sensing error.
The decision integration method using Majority logic is between the former two methods. When the Majority logic is formed of nodes having a similar signal to noise ratio (SNR), an excellent performance is shown. However, when there is a difference of SNR in each sensing device, the performance of the Majority logic is lower than that of the OR logic.
As described above, the disadvantages above may be problems to actually realize the data integration method.