Track fusion in the presence of sensor biases

Abstract

A computationally effective approach is developed in this study to deal with the problem of track fusion in the presence of sensor biases. Aiming at the case that sensor biases are implicitly included in the local estimates, a pseudo-measurement equation is derived based on the Taylor series expansion firstly, which reveals the relationship explicitly between local estimates and the sensor biases; and then, the bias estimates can be obtained in the rule of recursive least squares; finally, based on the derived pseudo-measurement equation, the sensor biases can be removed from the original local estimates and track fusion can be carried out directly and easily. Monte Carlo simulations demonstrate the efficiency and effectiveness of the proposed approach compared with the competing algorithms.

Joint track-to-track association and sensor registration at the track level

“…In our previous work [16], we derived a pseudo-measurement equation of sensor biases that reveals the relationship explicitly between local estimates and sensor biases. If two local estimates…”

“…The third one is to present efficient methods to solve the resulting complex optimization model. To overcome these difficulties, we constructed a novel pseudo-measurement equation in [16], which establishes a link between local estimates and sensor biases. Moreover, the formulation of the proposed pseudo-measurement equation requires far less information than that required in [14,15].…”

Anti‐bias track‐to‐track association algorithm based on distance detection

“…Owing to the mutual prerequisite relationships between track‐to‐track association and sensor registration, dealing with both simultaneously has become a hot research topic in recent years [17–22]. In [19], a method using pseudo‐measurements to carry out registration at the track level and applying the expectation maximum algorithm to perform data association, registration, and fusion simultaneously is proposed.…”

“…In [21], sensor registration and track‐to‐track fusion are treated as joint problems and solutions for both measurements and state estimates are provided. The literature [22] constructs pseudo‐measurement equation, which separates the state estimation vectors into real state vectors, sensor bias vectors, and random error vectors, thus sensor biases are estimated based on recursive least squares. However, the algorithm needs an iterative calculation and long running time, and is only available to constant sensor biases condition.…”

“…To solve the problem mentioned above, this paper proposed a novel association algorithm with significant improvement of association accuracy and complex condition adaptability. In [22], the pseudo‐measurement equation is present to decompose the estimated state distance vectors into the real state distance vectors, the sensor bias vectors and the random error vectors. The estimated state distance vectors between homologous tracks are affected by the random errors, fluctuates around the sensor bias vectors, and conflicts with the distance vectors between the heterologous tracks.…”