Title :
Fuzzy logic applications to multisensor-multitarget correlation
Author :
Singh, Ram-Nandan P. ; Bailey, William H.
Author_Institution :
Aircraft Div, Naval A, Patuxent River, MD, USA
fDate :
7/1/1997 12:00:00 AM
Abstract :
A consistent tactical picture requires data fusion technology to combine and propagate information received from diverse objects and usually vague situations. The information may be contained in two types of data; numerical data received from sensor measurements, and linguistic data obtained from human operators and domain experts. In real world situations, the numerical data may be noisy, inconsistent, and incomplete, and the linguistic information is imprecise and vague. To deal with these two types of data simultaneously, fuzzy sets and fuzzy logic provide a methodology to obtain an approximate but consistent tactical picture in a timely manner for very complex or ill-defined engineering problems. A functional paradigm for fuzzy data fusion is presented. It consists of four basic elements: (1) fuzzification of crisp elements, (2) fuzzy knowledge base derived from numerical input/output relations and humans, (3) fuzzy inference mechanism based on a class of fuzzy logic, (4) defuzzification of fuzzy outputs into crisp outputs for use by a plant. For real-time practical systems, the on-line determination of a fuzzy membership function from a given set of crisp inputs is vital. To this end, a methodology for estimating an optimal membership function from crisp input data has been implemented. This is based on the possibility/probability consistency principle as proposed by L.A. Zadeh. A relationship between the fuzzy membership function and the confidence level of statistical input data has been developed and it serves as a design parameter for fuzzification. This technique has been applied to a two-dimensional multisensor-multitarget tracking system. Fuzzy system performance evaluations have been presented. With simulated data in the laboratory environment, the simulation has been performed to evaluate the Mission Avionics Sensor Synergism (MASS) Systems. These results show better performance for the data correlation function using the fuzzy logic techniques.
Keywords :
Gaussian distribution; correlation methods; fuzzy logic; fuzzy set theory; inference mechanisms; military computing; object detection; possibility theory; radar target recognition; radar tracking; sensor fusion; target tracking; uncertainty handling; Gaussian PDF; Mission Avionics Sensor Synergism Systems; confidence level; crisp elements; data correlation function; data fusion technology; defuzzification; functional paradigm; fuzzification; fuzzy data fusion; fuzzy inference mechanism; fuzzy knowledge base; fuzzy logic applications; fuzzy membership function; human operators; ill-defined engineering problems; linguistic data; multisensor-multitarget correlation; numerical data; on-line determination; optimal membership function; possibility/probability consistency principle; radar tracks; sensor measurements; tactical picture; two-dimensional tracking system; Aerospace electronics; Data engineering; Fuzzy logic; Fuzzy sets; Fuzzy systems; Humans; Inference mechanisms; Performance evaluation; Probability; Real time systems;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on