Airborne Fire Control Radar Target Information Uncertainty Research

In recent years, the concept of multi-source information fusion causes the attention of a number of areas, whether military or civilian areas, the application of data fusion is more and more widely, Multi-source information fusion technology has many advantages, and because of this, recognition system based on data fusion merges the information from multiple sensors in order to identify the enemy property of target, Compared with the traditional IFF system, the integrated target identification system can improve the system’s accuracy and reliability. Due to a variety of complex and changing environment, coupled with the error on the sensors itself, the airborne radar as one of the sensors provide not an exact value about the target’s characteristics, but random variables subject to some random distribution, namely, the target characteristics exist uncertainty, As the input data of fusion system, this uncertainty will impact on the judgment(can’t get the discrimination results or get the completely wrong results),to a war environment, this will certainly result in very serious losses. Therefore, the analysis of the uncertainty about target’s characteristics is very necessary. The details as below:1) Classify the target’s feature, clear the relationship of these features, give the characteristics can be used to distinguish the enemy property of target; Clear the concept of the uncertainty of the objective characteristics, mechanism and the method of mathematical description;Relying on specific research project this paper based on, analyze the uncertainties of information provided by the IFF and radar.2) To specific integration system, analyze the ways that IFF and radar get the information used to distinguish the enemy attributes;at the same time, establish the uncertainty vector model (the value of the feature, uncertainty, confidence).3) In the background of white Gaussian noise, analyze the features’uncertainties (random distribution) from both theoretical and MATLAB simulation, established the mathematical model of uncertainty; On the other hand, from a different perspective, analyze the factors that affect the size of the uncertainty to reduce the impact on the next link of integration system.4) Discuss the statistics and spectrum model of a variety of distributed clutter and simulation;Analyze the factors that affect on the clutter’s amplitude and spectrum; From the perspective of MATLAB simulation, analyze the uncertainty of features in the background of clutter, given the description and metric methods of the uncertainty subjected to unknown distribution.From different point of view, analyze qualitative the factors that affect the size of the uncertainty in the background of the cutter to reduce the impact on the next link of integration system.