Yunfei Guo

Modeling of pH neutralization process using fuzzy recurrent neural network and DNA based NSGA-II

Abstract In this paper, the Takagi–Sugeno fuzzy recurrent neural network (T–S FRNN) is applied to model a pH neutralization process. Since the accuracy and complexity of the network are two contradictory criteria for the T–S FRNN model, a DNA based NSGA-II is proposed to optimize the parameters of the model. In the DNA based NSGA-II, each individual is encoded with one nucleotide base sequence, modified DNA based crossover and mutation operators are designed to improve the searching ability of the algorithm, and crowding tournament selection is applied based on the Pareto-optimal fitness and the crowding distance. The study on the performance of test functions shows that the DNA based NSGA-II outperforms NSGA-II in the quality of the obtained Pareto-optimal solution. To verify the effectiveness of the established T–S FRNN model for the pH neutralization process, it is compared with two T–S FRNN models optimized with other methods. Comparison results show that the model optimized by DNA based NSGA-II is more accurate and the complexity of the network is acceptable.

Penalty Dynamic Programming Algorithm for Dim Targets Detection in Sensor Systems

In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods. With this feedback, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation. Thus, the algorithm can be used in the multi-target situation with unknown target numbers. The efficiency and advantages of PDP-TBD compared with two existing methods are demonstrated by several simulations.

A joint tracking and classification algorithm with improved mutual feedback

For the joint tracking and classification (JTC) problem in FM-band passive air surveillance radar system, a particle filter approach with improved mutual feedback is presented. Delay and Doppler measurements are used to estimate dynamic state and recognize target class. The improved mutual feedback between tracker and classifier is realized by a classification probability dependent particle assignment technique, which utilizes feedback information completely and increases tracking performance of the higher probability target class. Simulation results show the efficiency of the proposed method.

Track-before-detect algorithm based on dynamic programming for multi-target tracking

A new dynamic programming based track before detect (DP-TBD) algorithm is presented to detect and track multi-target when SNR is low and target number is unknown. The technique combines the possible target state estimation using PDA with standard DP-TBD algorithm. And with the assumption that one measurement can originate from one target or clutter, the track separation phenomenon can be minimized and the target number can be obtained. Since the like-ratio function is not included in the merit function, the new method can be used without the priori information of the noise. Simulation results reveal that the new DP-TBD method has good performance when detecting target number and recovering target trajectories.

Low Altitude Target Tracking Algorithm with Acoustic Wireless Sensor Network

For the problem of low altitude target tracking with acoustic wireless network, the signal propagation time delay effect must be considered. The target has been far away from its emitting position when the signal is received by sensors. This effect leads to synchronous sensors in the measurement space sample asynchronously in the state space and the sample frequency becomes unknown and time varying. In this paper, a batch type distribution fusion algorithm is proposed which consists of two steps. First, a linear search method is used for estimating the time-varying state transition time which is the parameter of least square solution for the initial state. This initial state is used again to optimize the state transition time until the iteration termination condition is satisfied. Second, a time register procedure and a distribution fusion technique are presented to obtain the global track. Simulation results verify the efficiency of the proposed method.