Batch Fusion Estimater Based on Relative Measurements for Sensor Network with Arbitrary Time Lags

For the practical multisensor systems, the communication channels are often unreliable, especially in large, wireless, multi hop sensor networks. Thus, the communication delays often appear and produce random and unknown influence upon the performance of fusion estimators. Recently, data fusion for "Out-of-sequence" measurements (OOSM) attracts a mass of attentions gradually and some filters based on single sensor and multiple sensors have been reported. However, the existing methods for OOSM problem have several shortcomings, such as limited application for single sensor, bad real-time performance for the smooth property, low fusion estimate for neglecting the delayed information, and so forth. To tackle these problems, a novel batch fusion estimator based on the relative measurements for multisensor tracking systems with OOSM is proposed. The estimator is optimal in linear minimum mean square error (LMMSE). The paper also presents the suboptimal recursive form of the proposed estimator. Its principle is to establish the relative measurement equations of delayed observations to the next latest systemic state. Compared with some existingfusion methods for OOSM, the proposed method with arbitrary time delays has the best total performance. At the same time, algorithm analysis and computer simulation validate the advantages of the proposed fusion estimator.