Mismatching Judgment Using PDAF in ICCP Algorithm

Abstract

B. Kamgar-Parsi applies iterative closest contour point (ICCP) algorithm into underwater gravity matching, in which simplex algorithm is used to estimate the optimal trace of vehicle. However, simplex algorithm is usually convergent to the local optimization so that there is mismatching or even diverging. In this paper, the rule of mismatching judgment for ICCP is established to reduce mismatching probability. At present, the well used mismatching judgment rule, M/N method, has several shortcomings, including matching several times before location, much large matching error, and difficult to decide parameters. In this paper, the rule of mismatching judgment for ICCP is established by probability data association filter (PDAF). Simulation shows that PDAF improves the convergence and precision compared with the ICCP algorithm without mismatching judgment, and its mismatching probability decreases 35 percent compared with M/N method. The PDAF method for mismatching judgment increases the ICCPpsilas precision and stabilization.