Multisensor target tracking performance with bias compensation

Abstract

In this paper, multisensor-multitarget tracking performance with bias estimation and compensation is investigated when only moving targets of opportunity are available. First, we discuss the tracking performance improvement with bias estimation and compensation for synchronous biased sensors, and then a novel bias estimation method is proposed for asynchronous sensors with time-varying biases. The performance analysis and simulations show that asynchronous sensors have a slightly degraded performance compared to the "equivalent" synchronous ones. The bias estimates as well as the corresponding Cramer-Rao Lower Bound (CRLB) on the covariance of the bias estimates, i.e., the quantification of the available information on the sensor biases in any scenario are also given. Tracking performance evaluations with different sources of biases -offset biases, scale biases and sensor location uncertainties, are also presented and we show that tracking performance is significantly improved with bias estimation and compensation compared with the target tracking using the original biased measurements. The performance is also close to the lower bound obtained in the absence of biases.

Joint registration and multi‐target tracking based on labelled random finite set and expectation maximisation

“…When the bias for sensor i is time‐varying, the bias vector can be modelled as a dynamic stochastic process as given in [24–26] where , and are the range and angle bias rates, respectively, and is the process noise with covariance .…”

Multi-target joint detection, tracking and classification based on random finite set for aerospace applications

“…The prerequisite of multi-sensor data fusion is sensor registration [33] [34], which is referred to a process to eliminate the effect of sensor system bias on the measurement. Since each sensor measures the targets in its own coordinate system, fusion may be performed unsuccessfully and ghost tracks may occur due to the sensor measurement biases, such as scaling and offset biases in the range and azimuth measurements of radar [35,36].Various solutions have been proposed, such as the maximum likelihood [37], the extended Kalman filter [38][39][40], and the pseudo-measurement method [41][42][43].…”

“…To solve this problem, various solutions have been proposed, such as the maximum likelihood [37], the extended Kalman filter [38][39][40], and the pseudo-measurement method [41][42][43]. These algorithms calculate the sensor biases and the target state estimates simultaneously with determinate relationship between measurements and targets.…”

A joint maximum likelihood algorithm for simultaneous track correlation and sensorbias estimation

“…However, many error registration methods assume that tracks of the sensors had already been correlated. For example, Zhou [12] and Dong [13] used a maximum likelihood algorithm to estimate biases in two-sensor two-dimension networking systems; Karniely [14] used a neural network to estimate the sensor bias; Okello [15] adopted the maximum likelihood method in a distributed sensor system; Lin [16] and Qi [17] studied the problem of dynamic error registration; Zheng [18] and Fortunati [19] applied the least squares (LS) estimation method to error registration; Lin [20] researched the problem of asynchronous sensor bias registration; Lin [21] studied error registration in the target tracking system; and Besada [22] provided several sensor bias registration models. All these registration and correlation algorithms cannot be directly used in the multi-target multi-sensor information fusion system.…”