Research on Data Fusion Optimizing Filtering Algorithm for SOPLAT System

Abstract

Most currently existing algorithms used in SOPLAT (single observer passive location and tracking) system usually select single parameter measurement set which concludes only a little measured information, which means that they do not consider and make full use of spatial and temporal information as well as frequency range information which can be measured synchronously by modern electronic detecting equipment. In order to overcome this drawback, a new fusion optimization filtering algorithm for SOPLAT system is presented, this algorithm can fully utilize the redundant information coming from many sensors, which includes TOA, DOA and Doppler frequency measurements，etc. The proposed method improves further the location & tracking accuracy, reliability and defensive operational capability of the system by using data fusion technology. As a demonstration to evaluate the method described in this paper, two simulation examples are presented which indicate the effectiveness and feasibility of this filtering algorithm, and the results show that this method has better accuracy and convergence rate than conventional algorithms. key word: passive location; data fusion; time and direction of arrival; nonlinear system, optimization

Applications of artificial neural networks for calculation of the Erlang B formula and its inverses

“…Blocking probability versus traffic intensity, calculated using the Erlang B formula. As commented in, 18 if the traffic intensity is a = 100 Erl, then a S overflows +∞ for S greater than 154. In the case of Erlang B formula calculation in MATLAB, if the largest finite floating point number is exceeded, the resulting probability becomes NaN (not a number).…”

“…Calculations of the blocking probability values by straightforward application of the Erlang B formula may face issues for a larger number of channels and a larger amount of traffic. 18 Namely, the calculations in the frequently used program packages (like MATLAB software environment 34 ) are performed in double-precision arithmetic conforming to the IEEE standard 754. 35 The largest finite floating-point number in IEEE double precision is 1023 , which is 1.7977 × 10 308 .…”

“…Moreover, even the calculation of the Erlang functions for larger amounts of traffic and/or large channel number become computationally extensive and certain solutions of less computationally demanding approximations of Erlang formulas have been derived. 14,[18][19][20] The Erlang formulas look-up tables refer only to tabulated values of the input variables, whereas the approximations may not be accurate enough. Artificial neural networks can provide a fast and accurate solution for calculating the Erlang formulas and their inverses.…”