Linear Fusion Estimation for Range-Only Target Tracking With Nonlinear Transformation

This article is concerned with the multisensor fusion estimation for target tracking with range-only wireless sensor networks. By employing a nonlinear transformation and a measurement fusion, the nonlinear distance measurements are transformed into a linear measurement with respect to the position of the target, which avoids the instability problem of nonlinear filtering. However, after the transformation, the new measurement noises are no longer Gaussian and cross uncorrelated. Taking the unmodeled disturbances into account, as well as the new noise properties, an adaptive factor is introduced by hypothesis test based on the posterior residual to improve the estimation performance, where only the root of a quadratic equation is required to be solved. Finally, both simulations and experiments of a target tracking example are presented to show the effectiveness of the proposed methods.