Multisensor information fusion technology is an advanced processing method that different from classical data technology. Capability and operational accuracy, in order to make effective comprehensive evaluation of environmental quality, the monitoring system built by NB-IoT sensor used conduct multisensor research on collected factors such as temperature, humidity, formaldehyde, PM2.5, TVOC. At...

The focus of this paper is the problem of recursive estimation for uncertain multisensor linear discrete-time systems. We herein propose a new suboptimal filtering algorithm. The basis of the proposed algorithm is the fusion formula for an arbitrary number of local Kalman filters. The proposed suboptimal filter fuses each local Kalman filter by weighted sum with scalar weights. This filter can ...

We present and compare methods for pre-detection (feature-level) and post-detection (decision-level) fusion of multisensor data. This study emphasises methods suitable for data that are non-commensurate and sampled at noncoincident points. Decision-level fusion is most convenient for such data, but this approach is sub-optimal in principle, since targets not detected by all sensors will not ach...

A peer-to-peer collaboration framework for multisensor data fusion in resource-rich radar networks is presented. In the multi-sensor data fusion, data needs to be combined in such a manner that the real-time requirement of the sensor application is met. In addition, the desired accuracy in the result of the multi-sensor fusion has to be obtained by selecting a proper set of data from multiple r...

An algorithm for multiresolutional multisensor target identiication is presented. It uses the computationally eecient scale sequential approach to hypothesis testing during identiication and implements the inverse discrete wavelet transform to fuse data from diierent multires-olution sensors. We show that fusion leads to an increase in the probability of correct identiication without a signiica...

A multisensor fusion Student's $t$ filter is proposed for time-series recursive estimation in the presence of heavy-tailed process and measurement noises. It extends single-sensor Kalman to setup based on suboptimal arithmetic average (...

A new data fusion method called joint probability density matrix (JPDM) is proposed, which can associate and fuse measurements from spatially distributed heterogeneous sensors to identify the real target in a surveillance region. Using the probabilistic grids representation, we numerically combine the uncertainty regions of all the measurements in a general framework. The NP-hard multisensor da...