Qinglan Chen

A novel estimator with moving ants

Abstract In the traditional ant colony optimization (ACO) algorithm, ants are utilized to solve various combinatorial optimization problems regardless of their individual velocities. In this work, however, a moving ant estimator (MAE) is developed on the premise that the issue of moving velocity of each ant is considered. The determination of the velocity of each ant depends directly on the normalized weights between the one-step prediction of the ant starting position and the selected one-step position. Besides this, the velocity of each ant is further adjusted locally based on its individual moving ability, which is defined in the corresponding “pheromone” update process just as the traditional ACO algorithm. To improve the accuracy of the proposed estimator, two improved versions are investigated. Numerical simulations show that the moving ant estimator, as well as its two improved versions, could estimate adaptively the state of maneuvering or non-maneuvering target. To verify the effectiveness of the MAE, the obtained results are compared with those from PF, IMMPF, etc.

Ants for track initiation of bearings-only tracking

Abstract A novel track initiation method using ants of different tasks, a kind of ant colony optimization (ACO) algorithm, is developed in this paper. For the proposed system of ants of different tasks, we assume that the number of tracks to be initiated equals the one of tasks, and moreover, ants of the same task search for a given track by collaboration, while ants of different tasks will compete with each other during the search process. In order to fulfill such behaviors, the pheromone model is established, and the corresponding objective function to be optimized is also presented. Numerical simulation results indicate that, for the case of bearings-only multi-sensor-multi-target tracking, the track initiation performance for the proposed system of ants of different tasks performs well compared to other track initiation methods.

Analysis and approximation of performance bound for two-observer bearings-only tracking

This paper presents the analytic recursive formulas of Cramer-Rao lower bound (CRLB) for the switching models system, in which the target moves either with a constant velocity or with a constant speed and a constant turn rate. For the case of two-observer bearings-only maneuvering target tracking, a reliable maneuver detection method is investigated and then utilized to approximate the theoretic CRLB. Finally, to demonstrate the agreement between the approximated CRLB using the proposed maneuver detection method and the theoretic one, a large number of Monte-Carlo runs under different maneuvering scenarios are conducted. Correctness of the analytic recursive formulas of CRLB and effectiveness of the proposed maneuver detection method are verified from these simulations.

A new track initiation method for multi-target tracking

This paper introduces a novel track initiation technique for the bearings-only two-sensor-multi-target tracking system. Observing that each target is of the characteristic of uniform rectilinear motion, a new objective functional derived from the thought of Hough transform is investigated. On the basis of it, the ant colony optimization (ACO) algorithm, a kind of heuristic optimization method, is utilized to initiate tracks of interest. Numerical simulation results are presented and show that the proposed ACO-based track initiation method performs well compared with other traditional ones such as rule-based, logic-based, and Hough-transform-based track initiation approaches.

Two Ant Decision Levels and Its Application to Multi-Cell Tracking

Inspired by ant’s stochastic behavior in searching of multiple food sources, a novel ant system with two ant decision levels are proposed to track multiple cells in biological field. In the ant individual level, ants within the same colony perform independently, and ant decision is determined in probability by both its intended motion model and likelihood function. In the ant cooperation level, each ant adjusts individual state within its influence region, while the global best template at current iteration is found among all ant colonies and further utilized to update ant model probability, influence region, and the probability of fulfilling task. Simulation results demonstrate that our algorithm could automatically track numerous cells and its performance is compared with the multi-Bernoulli filtering method.

A Hybrid ACO-ACM Based Approach for Multi-cell Image Segmentation

In this paper, a hybrid multi-cell image segmentation approach is proposed, based on the combination of active contour model (ACM) and ant colony optimization (ACO), for multi-cell image segmentation. This novel image segmentation algorithm integrates the characteristics of ACM model into the ACO with tractable and well defined energy and heuristic functions. Consequently, the problem of cell image segmentation is actually converted to search for the marks of cell contours by group of ants. Experiment results show that our proposed approach is more effective than several existing methods, and it is noted that our proposed approach is developed and implemented in LabVIEW as well with performance consistency.

Multi-cell Contour Estimate Based on Ant Pheromone Intensity Field

In this paper, we propose an ant pheromone based approach to accurately extract the contours of multiple small cells in low contrast bio-medical images. With the local information of intensity variation of each pixel, the initial distribution of ant colony is generated as ants’ starting positions. Following the heuristic information, such as the pixel grayscale variance, the ant inertial heading and the image intensity, ant’s searching behavior is modeled appropriately to make each of ants move along the edge of interested object as possible. Due to modeling an accurate depositing mechanism of pheromone, the corresponding ring pheromone field is formed and used to extract interested cells’ contours after simple morphological operations. Experiment results show that our algorithm could give an accurate contour estimate of each cell for several different image sequences.

Ocean buoy communication node selection strategy with intelligent ant behavior

In this paper, we propose a novel ant system algorithm for balancing node energy distribution with maximum the number of complete data transmission in ocean buoy communication sensor network. In our algorithm, a complete transmission process is regarded as an ant tour, and each ant stochastically select corresponding node based on such information as energy function, heuristic function, and pheromone amount. An appropriate objective function is carefully designed with the expectation of maximizing the number of complete transmission and uniform minimum energy distribution. Simulation results are presented to support obtained favorable performance of our algorithm.