Centralized Fusion Estimators for Multisensor Systems With Random Sensor Delays, Multiple Packet Dropouts and Uncertain Observations

Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

“…In [158], by using the innovation analysis technique and augmentation approach, the optimal centralized fusion estimators (including filter, predictor and smoother) in the minimum variance sense have been designed for a class of linear discrete time-varying stochastic systems with random delays, packet dropouts and uncertain observations, where the stability of the developed estimation algorithms has been discussed and sufficient criterion has been given to verify the existence of the centralized fusion steady-state estimators. Recently, by employing similar technique as in [158], the optimal centralized and distributed fusion estimation problems have been addressed in [159] for linear discrete time-varying multi-sensor system with different packet dropout rates, and the centralized fusion estimators (including filter, predictor and smoother) in the linear minimum variance sense have been firstly designed and, subsequently, the distributed fusion estimation algorithm based on the scalar-weighted fusion mechanism has also been provided in order to decrease the computational cost and improve the reliability.…”

Multi‐UAVs Cooperative Localization Algorithms with Communication Constraints

“…Two methods are proposed to describe communication latency and packet loss. One is Markov chain in [7]; the other is random variable with Bernoulli distribution in [8][9][10].…”

Least‐Squares Filtering Algorithm in Sensor Networks with Noise Correlation and Multiple Random Failures in Transmission

“…However, usually the network characteristics may not be completely reliable and some anomalies (e.g., uncertain observations or missing measurements, random delays, and/or packet dropouts) may arise when the sensor measurements are transmitted to the fusion center. Ignoring these random phenomena in the derivation of the estimators may deteriorate their accuracy and performance and, for this reason, the design of new fusion estimation algorithms for linear systems featuring one of these uncertainties (see, e.g., [3][4][5] and references therein) or even several of them simultaneously (see, e.g., [6][7][8][9] and references therein) has become an active research topic. Specifically, multisensors subject to random packet dropouts are dealt with in [3], a packet-dropping network is considered in [4], and networked systems in the presence of stochastic sensor gain degradations are object of study in [5].…”

“…Specifically, multisensors subject to random packet dropouts are dealt with in [3], a packet-dropping network is considered in [4], and networked systems in the presence of stochastic sensor gain degradations are object of study in [5]. Networked multisensor systems with random transmission delays and packet dropouts are considered in [6][7][8][9], in addition to missing sensor measurements in [6] and uncertain observations in transmission in [8].…”

“…The most commonly used fusion methods are the centralized and the distributed ones. In the centralized fusion filtering algorithms (see, e.g., [8]), all the raw data from different sensors are sent to a single location where they are fused and processed to provide optimal estimators; hence, when there are no sensor errors and under perfect connections, centralized fusion estimators have the best accuracy. On the other hand, in the distributed fusion filtering algorithms (see, e.g., [13]), the filtering process is divided between some local filters working in parallel to obtain individual sensor-based estimates and one main filter where these local estimates are combined to yield an improved global signal estimate.…”