Andong Sheng

An order insensitive sequential fast covariance intersection fusion algorithm

The proposed SFCI fusion algorithm can handle the problem of unknown cross-correlation in local estimation errors.The fusion accuracy of the proposed algorithm is not relevant to the fusion orders, i.e., all fusion nodes can acquire identical result.The fusion coefficients are straightforward to calculate, owing to not require optimizing nonlinear cost function.The proposed algorithm is computationally efficient and is therefore applicable for use in real-time fusion systems. In this article, data cross-correlation in distributed sensor system is investigated via an order insensitive sequential fast covariance intersection fusion algorithm. Among the existing approaches, the common drawbacks are that the fusion results are sensitive to fusion orders and the computational burden is tremendous due to the optimization of multi-dimensional nonlinear cost function. In order to overcome these drawbacks, a sequential fast covariance intersection (SFCI) algorithm is presented. The new fusion coefficients can be calculated straightforward by taking the reciprocal of the trace of the inverse variances as local fusion coefficients and using an iterative process for fusion step to revise the coefficient weight. Note that the proposed fusion algorithm is consistent, and its accuracy is unrelated to the fusion order of the distributed system. Finally, real radar data and simulation examples are provided to verify the effectiveness of the proposed algorithm.

Modeling analysis of ant system with multiple tasks and its application to spatially adjacent cell state estimate

The problem of multi-cell tracking plays an important role in studying dynamic cell cycle behaviors. In this paper, a novel ant system with multiple tasks is modeled for jointly estimating the number of cells and individual states in cell image sequences. In our ant system, in addition to pure cooperative mechanism used in traditional ant colony optimization algorithm, we model and investigate another two types of ant working modes, namely, dual competitive mode and interactive mode with cooperation and competition to evaluate the tracking performance on spatially adjacent cells. For adjacent ant colonies, dual competitive mode encourages ant colonies with different tasks to work independently, whereas the interactive mode introduces a trade-off between cooperation and competition. In simulations of real cell image sequences, the multi-tasking ant system integrated with interactive mode yielded better tracking results than systems adopting pure cooperation or dual competition alone, both of which cause tracking failures by under-estimating and over-estimating the number of cells, respectively. Furthermore, the results suggest that our algorithm can automatically and accurately track numerous cells in various scenarios, and is competitive with state-of-the-art multi-cell tracking methods.

Cell automatic tracking technique with particle filter

Cell motion analysis contributes to research the mechanism of the inflammatory process and to the development of anti-inflammatory drugs. To gain full dynamics of multiple cells, a hybrid cell detection algorithm is first designed, which is combined with several methods, such as threshold processing, distance transform, watershed negative transform, and shape and boundary constraint, to reduce over-segmentation and contour missing. By exploiting temporal information and prior knowledge, a particle-filter-based tracking technique is then proposed for image sequences to estimate individual state of multiple cells. Simulation results are presented to support obtained favorable performance of our algorithm.

Extended target tracking filter with intermittent observations

This study addresses the problem of tracking extended target with intermittent observations. Based on practical applications, two Bernoulli distributed random variables are employed to describe the intermittent phenomenon of the positional measurements and the measurements of target extent, respectively. First, a machine vision algorithm is developed to solve the target shape parameters. Then, four sub-filters are designed according to the received observations and the achieved target shape parameters. The output of the proposed tracking filer can be obtained by the weighted-confidence fusion of the sub-filters. Finally, the machine vision algorithm is evaluated by the virtual target images created in OpenGL (Open Graphics Library) and the real images of a moving ship. The performance of the designed tracking filter is compared with the traditional tracking filter. The experiment results show the effectiveness of the machine vision approach; also the Monte-Carlo runs demonstrate that the provided tracking filter outperforms the traditional one with respect to accuracy.

Cooperative Target Localization and Tracking with Incomplete Measurements

This study investigates a problem on target localization and tracking for two cases where either the slant range information of dual stations is lost or the slant range information of one station and the pitch angle information of the other one are missing. The models of cooperative localization with incomplete measurements are presented and the Kalman filtering algorithm is applied for target tracking. For improving tracking precision, a strategy of observers path planning based on the gradient of circular error probability (CEP) is integrated into the Kalman filtering algorithm. Several numerical examples are used to illustrate the tracking performance of the proposed algorithm with the corresponding root mean square error (RMSE) and Cramer-Rao lower bound (CRLB). The Monte Carlo simulation results validate the effectiveness of the presented algorithm.

A distributed fusion algorithm over asynchronous sensor networks

This paper presents a distributed asynchronous fusion algorithm over peer-to-peer asynchronous sensor networks. We propose two major steps to make an asynchronous fusion algorithm to work in a distributed way. First, we develop a local estimator in an optimal batch fashion. The second step is to present the fusion estimator which fuses local estimates in the neighborhood based on covariance intersection algorithm. The proposed algorithm improves local estimates and reduces the estimate disagreement. The effectiveness of the proposed distributed asynchronous fusion algorithm is validated by simulation results.

Quantized steady-state kalman filter in a wireless sensor network

This paper addresses the problem of state estimation in the wireless sensor network (WSN). Firstly, the quantized Kalman filter based on the quantized observations is presented. Focuses are on tradeoff between the communication energy and the estimation accuracy. A closed-form solution to the optimization problem for minimizing the energy consumption is given, where the total energy consumption is minimized subject to a constraint on the stead state error covariance. An illustrative numerical example is provided to demonstrate the usefulness and flexibility of the proposed approach.

Diffusion Kalman filtering with multi-channel decoupled event-triggered strategy and its application to the optic-electric sensor network

Abstract Recently the distributed estimation problem with communication constraints has been widely studied for sensor network application. Our work focus on the diffusion Kalman filter with communication constraints. To satisfy finite communication resources constraints, this paper presents a multi-channel decoupled event-triggered strategy which improves the utilization of the network communication resources. With this strategy, only some entries of sensors’ measurements are transmitted if their triggering criteria are satisfied. We apply this strategy to the step 1 of the diffusion Kalman filter and analyze its performance. The analysis shows that the multi-channel decoupled event-triggered diffusion Kalman filter is unbiased in mean sense and is convergent in mean-square sense. The theoretical steady-state mean-square deviation (MSD) and communication cost are also given in this article. Simulation results demonstrate a good match between the theory analysis and experiment. Finally this algorithm is applied to the optic-electric sensor network, and the results verify the effectiveness of the proposed strategy in terms of the communication resources utilization.

A Distributed sequential fusion algorithm over asynchronous sensor networks

A distributed sequential fusion algorithm for decentralized asynchronous sensor networks is presented in this paper. Our distributed asynchronous sequential fusion algorithm can be divided into a local estimator and a fusion estimator. The local estimator utilize the local measurements to generate a local fusion estimates. And the local fusion estimates are fused by the fusion estimator to improve the local estimation performance. The proposed algorithm is operational for sensor networks with arbitrarily sampling rates and initial sampling time instants. The effectiveness of the proposed algorithm is validated by simulation results.

A Novel Multiobject Tracking Approach in the Presence of Collision and Division

This paper aims to develop a general framework for accurately tracking and quantitatively characterizing multiple cells (objects) when collision and division between cells arise. Through introducing three types of interaction events among cells, namely, independence, collision, and division, the corresponding dynamic models are defined and an augmented interacting multiple model particle filter tracking algorithm is first proposed for spatially adjacent cells with varying size. In addition, to reduce the ambiguity of correspondence between frames, both the estimated cell dynamic parameters and cell size are further utilized to identify cells of interest. The experiments have been conducted on two real cell image sequences characterized with cells collision, division, or number variation, and the resulting dynamic parameters such as instant velocity, turn rate were obtained and analyzed.