A nonlinear tracking algorithm with range-rate measurements based on unbiased measurement conversion

TL;DR

This paper introduces a nonlinear tracking algorithm that effectively utilizes range-rate measurements by converting them into pseudo measurements, improving accuracy and consistency over traditional methods.

Contribution

The paper extends the 3D CMKF-U to incorporate range-rate measurements using a novel pseudo measurement approach and sequential filtering for enhanced tracking performance.

Findings

Outperforms traditional CMKF-D in simulations

Provides better measurement consistency than previous methods

Reduces approximation errors with sequential filtering

Abstract

The three-dimensional CMKF-U with only position measurements is extended to solve the nonlinear tracking problem with range-rate measurements in this paper. A pseudo measurement is constructed by the product of range and range-rate measurements to reduce the high nonlinearity of the range-rate measurements with respect to the target state; then the mean and covariance of the converted measurement errors are derived by the measurement conditioned method, showing better consistency than the transitional nested conditioning method; finally, the sequential filter was used to process the converted position and range-rate measurements sequentially to reduce the approximation error in the second-order EKF. Monte Carlo simulations show that the performance of the new tracking algorithm is better than the traditional one based on CMKF-D.