Target type probability combining algorithms for multi-sensor tracking

被引:7
作者
Wigren, T [1 ]
机构
[1] SAABTech Syst AB, Sensor Data Proc Grp, SE-17588 Jarfalla, Sweden
来源
SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION X | 2001年 / 4380卷
关键词
Bayesian estimation; cross computation; deghosting; information fusion; multi-sensor tracking; target type estimation; track correlation; triangulation;
D O I
10.1117/12.436991
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Algorithms for the handing of target type information in an operational multi-sensor tracking system are presented. The paper discusses recursive target type estimation, computation of crosses from passive data (strobe track triangulation), as well as the computation of the quality of the crosses for deghosting purposes. The focus is on Bayesian algorithms that operate in the discrete target type probability space, and on the approximations introduced for computational complexity reduction. The centralized algorithms are able to fuse discrete data from a variety of sensors and information sources, including IFF equipment ESM's, IRST's as well as flight envelopes estimated from track data. All algorithms are asynchronous and can be tuned to handle clutter, erroneous associations as well as missed and erroneous detections. A key to obtain this ability is the inclusion of data forgetting by a procedure for propagation of target type probability states between measurement time instances. Other important properties of the algorithms are their abilities to handle ambiguous data and scenarios. The above aspects are illustrated in a simulations study. The simulation setup includes 46 air targets of 6 different types that are tracked by 5 airborne sensor platforms using ESM's and IRST's as data sources.
引用
收藏
页码:46 / 62
页数:17
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