Jin Xue-bo

Centralized fusion estimation for uncertain multisensor system based on LMI method

This paper gives the models of uncertain multisensor system based on norm-bounded parameter uncertain model method and convex bounded uncertain model method, respectively. The corresponding centralized robust fusion estimations are developed, too. Simulation compares different fusion estimation methods detailedly in frequency and time fields and concludes that each of two filters has its strong point at restraining disturbance attenuation within some frequency range. The filter based on former model can obtain less estimation covariance, but may encounter numerical value problem and lead to a ‘bad’ solution. The filter based on latter model will become complicated when the number of sensors increasing. The results of this paper develop the theory basis of state fusion estimation for the uncertain multisensor system in practical.

Tracking in Distributed Multisensor System Update with Out-of-Sequence Information

In multisensor target tracking systems measurements from the same target can arrive out of sequence. How to fuse out-of-sequence information is a very important problem for distributed multisensor target tracking systems. Local sensors send their track to a fusion center and then the fusing center uses these track data from one or more sensors to update. In this paper, we obtain the out-of-sequence track in fusion center for distributed multisensor system based on the assumption that each sensor transmits to the central tracker only the last updated state for each track and the corresponding covariance matrix. Then we present numerical results using simulated data for a scenario where a global tracker processes track data from two local trackers. The results show the distributed fusion here is perfectly compared with the out-of-sequence measurement method.

Demo system of amplitude modulation based on Java

Demonstration system of amplitude modulation based on JAVA is developed. First, this paper analyzes the principle of amplitude modulation and demodulation. Then, The Architecture of demo system is designed. According to function requirement, we adopt object-oriented method to design and implement the demo system. Finally, we use the button to debug the demo system. The results indicate that the signal wave can change with pressing the button. The demo system has an attractive interface, and is convenient to operate. It also has a good interactivity. This demo system has already been used in educational practice and got a good effect.

No-Model Tracking by Data-Driven Method Using BP Networks

In the model-driven tracking approach, the tracking performance is mainly based on the system model. But the accurate model is difficult to obtain, while data-driven tracking approach can’t depend on the accuracy of system model and it can tracking by the measurement data. This paper use BP net to develop a data-driven tracking. The BP net is trained by system measurement and prediction get by Kalman filter and the tracking as the output data. The simulation has shown that the data-driven tracking by BP net can obtain good performance even when the moving target is different.

Distributed optimal out-of-sequence tracks fusion estimation based on pseudo-measurement

In practice, the fusion center of distributed multisensor system has to deal with out-of-sequence local estimated data. By considering the relation between local fusion estimation and fusion center, the estimation from local fusion nodes is regarded as a pseudo-measurement in this paper. Then the distributed estimation algorithm is turned to be two-level centralized fusion estimation and the new optimal distributed fusion estimation algorithm is obtained with Kalman filtering form, which in general only centralized estimation method has. Then, this paper develops optimal one-step-lag OOST(out-of-sequence tracks) method for distributed multisensor system by combining pseudo-measurement distributed fusion estimation algorithm and optimal one-step-lag OOSM — A1. Simulations show the developed algorithm has the excellent estimation performance.

Distributed Fusion Estimation Based on Pseudo-measurement for Multi-sensor System

By considering the relation between local fusion estimation and fusion center, in this paper the estimation from local fusion nodes is regarded as a pseudo-measurement. Then the distributed estimation algorithm is turned to be two-level centralized fusion estimation and the new optimal distributed fusion estimation algorithm is obtained with Kalman filtering form, which in general only centralized estimation method has. Simulations show the developed algorithm has the excellent estimation performance. By the developed algorithm, the distributed multisensor system can be unified with centralized system and make it possible that applying the abundant research result of centralized system to distributed multisensor system.