An important prerequisite for successful multisensor integration is that the data from the reporting sensors are transformed to a common reference frame free of systematic or registration bias errors. If not properly corrected, the registration errors can seriously degrade the global surveillance system performance. The relative sensor registration (or grid-locking) process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. In this paper we consider a multi-target scenario and we address here the problem of jointly estimating all registration errors involved in the grid-locking problem. An Expectation-Maximization (EM) estimator of all bias errors is derived and its statistical performance compared to the hybrid Cramér-Rao lower bound (HCRLB)
An Expectation-Maximization-Based Approach to the Relative Sensor Registration for Multi-Target Scenario
GINI, FULVIO;GRECO, MARIA;
2012-01-01
Abstract
An important prerequisite for successful multisensor integration is that the data from the reporting sensors are transformed to a common reference frame free of systematic or registration bias errors. If not properly corrected, the registration errors can seriously degrade the global surveillance system performance. The relative sensor registration (or grid-locking) process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. In this paper we consider a multi-target scenario and we address here the problem of jointly estimating all registration errors involved in the grid-locking problem. An Expectation-Maximization (EM) estimator of all bias errors is derived and its statistical performance compared to the hybrid Cramér-Rao lower bound (HCRLB)I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
