Algorithms Design for Tracking Moving Objects with Colored Process Noise

Tracking of moving objects is often accompanied with colored process noise (CPN) in the object speed. To reduce tracking errors, the Kalman and unbiased finite impulse response filtering algorithms are modified in this paper assuming the Gauss-Markov noise nature. The state differencing approach employed to derive the algorithms, requires solving a nonsymmetric algebraic Riccati equation that allows modifying the system matrix for CPN. Based on a simulated tracking example, a higher accuracy of the modified KF and unbiased finite impulse response (UFIR) filter is demonstrated. Extensive investigations of the walking human trajectories are provided for colored speed noise.