Comparison of several maneuvering target tracking models

Abstract

The tracking of maneuvering targets is complicated by the fact that acceleration is not directly observable or measurable.Additionally, acceleration can be induced by a variety of sources including human input, autonomous guidance, or atmospheric disturbances. The approaches to tracking maneuvering targets can be divided into two categories both of which assume that the maneuver input command is unknown. One approach is to model the maneuver as a random process. The other approach assumes that the maneuver is not random and that it is either detected or estimated in real time. The random process models generally assume one of two statistical properties, either white noise or an autocorrelated noise. The multiple-model approach is generally used with the white noise model while a zero-mean, exponentially correlated acceleration approach is used with the autocorrelated noise model. The nonrandom approach uses maneuver detection to correct the state estimate or a variable dimension filter to augment the state estimate with an extra state component during a detected maneuver.Another issue with the tracking of maneuvering target is whether to perform the Kalman filter in Polar or Cartesian coordinates. This paper will examine and compare several exponentially correlated acceleration approaches in both Polar and Cartesian coordinates for accuracy and computational complexity. They include the Singer model in both Polar and Cartesian coordinates, the Singer model in Polar coordinates converted to Cartesian coordinates, Helferty's third order rational approximation of the Singer model and the Bar-Shalom and Fortmann model. This paper shows that these models all provide very accurate position estimates with only minor differences in velocity estimates and compares the computational complexity ofthe models.

Extended input estimation method for tracking non‐linear manoeuvring targets with multiplicative noises

“…Tracking manoeuvring targets is an important problem in guidance systems complicated by the fact that the accelerations acting on the target are not measurable with radar [6].…”

“…In spite of the recent advances in sensor technology, there are no devices that can detect the manned manoeuvres of a tracked target in the surveillance and guidance systems, thus tracking problem becomes more complicated [6]. The key problem in this kind of approach is input detection and estimation.…”

Set value‐based dynamic model development for non‐linear manoeuvring target tracking problem in the presence of unknown but bounded disturbances

“…radar and electro‐optical trackers) cannot measure the target acceleration directly. Owing to the lack of target manoeuvre measurement, the MTT problem becomes more complicated [1].…”

“…In a stochastic approach, the target acceleration is considered a random process. Stochastic acceleration models can be categorised into three groups: white noise, Markov, and semi‐Markov jump processes [1]. The earliest and simplest models developed for the MTT problem are the constant velocity and constant acceleration (CA) models [8, 9].…”

Modified input estimation technique for tracking manoeuvring targets

“…Target tracking is the determination of the present and often future states (position and velocity) of a moving target from noisy measurements of its present states. Tracking manoeuvring targets is an important problem complicated by the fact that radar cannot directly measure target accelerations [1].…”