Selecting the optimal satellite subset for positioning from all satellites in view can not only achieve positioning accuracy but also reduce the computational burden. In this paper, a navigation satellites selection method is proposed based on ant colony optimization with the improvement of polarized feedback (ACO-PF). Firstly, the satellite selection problem is described as a combinatorial optimization problem, and the noise weighted geometric dilution of precision (NWGDOP) is defined as the criterion for satellite selection. Then the ant colony optimization (ACO) is incorporated to solve the problem, and a polarized feedback mechanism is presented to improve the convergence speed of algorithm. Meanwhile, a perturbation operator is designed to improve the global searching ability of the algorithm. The numerical experimental results show that ACO-PF can select the superior satellites combination which provides highprecision positioning. And its convergence outperforms the related algorithms by up to 50%. Besides, the achieved NWGDOP of ACO-PF is usually 0.065 smaller than ACO. Therefore, the ACO-PF method can be considered as a promising candidate for satellite selecting in navigation applications.INDEX TERMS Ant colony optimization (ACO), noise weighted, geometric dilution of precision (GDOP), polarized feedback, perturbation operator

In various applications of satellite navigation and positioning, it is a key topic to select suitable satellites for positioning solutions to reduce the computational burden of the receiver in satellite selection system. Moreover, in order to reduce the processing burden of receivers, the satellite selection algorithm based on Gibbs sampler is proposed. First, the visible satellites are randomly sampled and divided into a group. The group is regarded as an initial combination selection scheme. Then, the geometric dilution of precision is chosen as an objective function to evaluate the scheme’s quality. In addition, the scheme is updated by the conditional probability distribution model of the Gibbs sampler algorithm, and it gradually approaches the global optimal solution of the satellite combination with better geometric distribution of the space satellite. Furthermore, an “adaptive perturbation” strategy is introduced to improve the global searching ability of the algorithm. Finally, the extensive experimental results demonstrate that when the number of selected satellite is more than 6, the time that the proposed algorithm with the improvement of “adaptive perturbation” takes to select satellite once is 43.7% of the time that the primitive Gibbs sampler algorithm takes. And its solutions are always 0.1 smaller than the related algorithms in geometric dilution of precision value. Therefore, the proposed algorithm can be considered as a promising candidate for satellite navigation application systems.

Distributed Active Sensing Networks (DASNs) is a new sensing paradigm, where active and passive sensors are distributed in a field, and collaboratively detect the objects. The detectability is the most important property of DASNs. ''Exposure'' is defined to quantify the dimension limitations in detectability. Thus, it is important to deploy the sensors with the minimum exposure to improve the detectability. To minimize exposure is NP-hard, thus it is necessary to solve it by heuristic algorithms. In this paper, we present a Discrete Particle Swarm Optimization (DPSO)-based solution to achieve the minimum exposure. Furthermore, a feedback-based adjustment on the inertia weight of DPSO is designed to improve the convergence speed and global searching ability of algorithm. By a large number of simulations, this improved DPSO (Faiw-DPSO) is proved to outperform greedy algorithm by up to 74% and perform better than other related algorithms. This algorithm is robust, efficient and self-adaptive in regular and irregular monitoring field. INDEX TERMS Distributed active sensing networks (DASNs), discrete particle swarm optimization (DPSO), exposure, feedback-based adjustment, inertia weight.

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