Assessment of Hierarchical Multi-Sensor Multi-Target Track Fusion in the Presence of Large Sensor Biases

Optimal Linear Filter Based on Feedback Structure for Sensing Network with Correlated Noises and Data Packet Dropout

“…The advantage of this method is that it no longer needs a fusion center that requires a large amount of memory space. Thus, in recent decades, various distributed and parallel versions and applications of the Kalman filter have been reported, such as in [ 7 , 8 , 9 ], to improve its accuracy. Hashmipour et al [ 7 ] described a parallel Kalman filtering structure for multisensory networks amenable to parallel processing.…”

“…Carlson [ 8 ] presented the famous federated square root filter, which assumes the initial estimation error cross covariance matrices among the local subsystems to be zero, i.e., the local estimation errors among the local subsystems are uncorrelated at the initial time, which does not accord with the general case. Ogle et al [ 9 ], in turn, described a multi-sensor optimal information fusion estimator in the maximum likelihood sense under the assumption of normal distributions.…”

“…Moreover, we use a filter with a feedback structure based on this, making it possible to reduce the covariance of each local tracking error while maintaining the optimality of the trajectory fusion. In summary, the main contributions of this paper are as follows: To address the unresolved issue of the impact of excessive covariance on sensor systems in [ 9 , 11 , 13 ], this paper proposes a matrix weight fusion method with a feedback structure. The method is not only able to deal with the problem of the inter-correlation of measurement and estimation noise in the process of multi-sensor data fusion, is can also achieve optimal estimation in a linear minimum variance sense.…”

“…To address the unresolved issue of the impact of excessive covariance on sensor systems in [ 9 , 11 , 13 ], this paper proposes a matrix weight fusion method with a feedback structure. The method is not only able to deal with the problem of the inter-correlation of measurement and estimation noise in the process of multi-sensor data fusion, is can also achieve optimal estimation in a linear minimum variance sense.…”

Study on Multi-Heterogeneous Sensor Data Fusion Method Based on Millimeter-Wave Radar and Camera

“…It involves techniques such as data detection, target recognition, comprehensive optimization, data association, and tracking processing to effectively coordinate and optimize the data information from different sensors. This allows for the integration of the local information collected by different sensors, while reducing the differences and minimizing redundant information between these data sources [3]. Ultimately, this approach reduces uncertainty and improves the reliability and robustness of the system.…”