To establish situation awareness during air defence surveillance missions, track level data from sensors and additional data sources are combined to form the air picture for the region under surveillance. Typically, compilation of the air picture and the C4ISR activities that it supports, namely real-time surveillance, fighter control and situation and threat assessment at the tactical level, and mission planning and intelligence collection at the theatre level, are all performed manually by defence and intelligence operators. To assist operators with compilation of the air picture and its subsequent applications, it is desirable to introduce automation to the information processing required for these activities. To accomplish this requires the use of the contextual information in the surveillance region to extract descriptive (symbolic) information about the behaviour of each detected air target from the positional and kinematic data in its state estimate. Since much of the contextual information exists in the form of entities and regions that can be modeled geometrically, it is possible to perform the information extraction using geometric criteria. In this paper, this philosophy is followed to produce such a set of geometric criteria which can be used to extract information that can be conveniently represented as predicates. First the choice of the criteria is motivated by an examination of the nature of the information which is to be extracted, before describing the mathematical details required for determining that the criteria are met. Several examples are also given to illustrate the methodology for using the criteria. Finally, the future directions for the further development, test and evaluation of the methodology are briefly discussed.
Association of air targets with airlanes is a problem of interest in wide area surveillance because of its application to target identification, situation assessment and sensor registration. This problem has been previously considered for the single airlane scenario, under the assumption that the track state estimates are Gaussian distributed. In this paper, under the same assumptions, a recent solution based on statistical hypothesis tests is generalized and extended in three ways. First, the association test is generalized to the multiple airlane scenario. If the target can be associated with more than one airlane, the ambiguity is resolved by employing the test statistic of the association test as a discriminant. Secondly, a probabilistic state model based on airlane information is formulated for the corresponding airlane. Finally, the track data is fused with the associated airlane to improve target state estimates. Simulation results are presented for both unbiased and biased sensor measurements in terms of the probability of association for each airlane, and the root mean square error of fused and unfused target position and velocity estimates.
In many commercial and military activities such as manufacturing, robotics, surveillance, target tracking and military command and control, information may be gathered by a variety of sources. The types of sources which may be used cover a broad spectrum and the data collected may be either numerical or linguistic in nature. Data fusion is the process in which data from multiple sources are combined to provide enhanced information quality and availability over that which is available from any individual source. The question is how to assess these enhancements. Using the U.S. JDL Model, the process of data fusion can be divided into several distinct levels. The first three levels are object refinement, situation refinement and threat refinement. Finally, at the fourth level (process refinement) the performance of the system is monitored to enable product improvement and sensor suite management. This monitoring includes the use of measures of information from the realm of generalized information theory to assess the improvements or degradation due to the fusion processing. The premise is that decreased uncertainty equates to increased information. At each level, the uncertainty may be represented in different ways. In this paper we give an overview of the existing measures of uncertainty and information, and propose some new measures for the various levels of the data fusion process.