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A type of collective detection scheme with improved pigeon-inspired optimization

机译:一种改进的鸽子启发式优化的集体检测方案

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Purpose - With increasing demand of localization service in challenging environments where Global Navigation Satellite Systems (GNSS) signals are considerably weak, a powerful approach, the collective detection (CD), has been developed. However, traditional CD techniques are computationally intense due to the large clock bias search space. Therefore, the purpose of this paper is to develop a new scheme of CD with less computational burden, in order to accelerate the detection and location process. Design/methodology/approach - This paper proposes a new scheme of CD. It reformulates the problem of GNSS signal detection as an optimization problem, and solves it with the aid of an improved Pigeon-Inspired Optimization (PIO). With the improved PIO algorithm adopted, the positioning algorithm arrives to evaluate only a part of the points in the search space, avoiding the problems of grid-search method which is universally adopted. Findings - Faced with the complex optimization problem, the improved PIO algorithm proves to have good performance. In the acquisition of simulated and real signals, the proposed scheme of CD with the improved PIO algorithm also have better efficiency, precision and stability than traditional CD algorithm. Besides, the improved PIO algorithm also proves to be a better candidate to be integrated into the proposed scheme than particle swarm optimization, differential evolution and PIO. Originality/value - The novelty associated with this paper is the proposition of the new scheme of CD and the improvement of PIO algorithm. Thus, this paper introduces another possibility to ameliorate the traditional CD.
机译:目的-随着在全球导航卫星系统(GNSS)信号非常弱的挑战性环境中对本地化服务的需求不断增长,已经开发了一种功能强大的方法,即集体检测(CD)。然而,由于大的时钟偏差搜索空间,传统的CD技术在计算上非常密集。因此,本文的目的是开发一种计算量较小的新CD方案,以加速检测和定位过程。设计/方法/方法-本文提出了CD的新方案。它将GNSS信号检测问题重新定义为优化问题,并借助改进的“鸽子灵感优化”(PIO)解决了该问题。通过采用改进的PIO算法,该定位算法可以只对搜索空间中的部分点进行评估,从而避免了普遍采用的网格搜索方法的问题。发现-面对复杂的优化问题,改进的PIO算法被证明具有良好的性能。在模拟和真实信号的采集中,所提出的带有改进的PIO算法的CD方案也比传统的CD算法具有更好的效率,精度和稳定性。此外,与粒子群优化,差分进化和PIO相比,改进的PIO算法也被证明是一种更好的候选方案。原创性/价值-与本文相关的新颖性是CD新方案的提出和PIO算法的改进。因此,本文介绍了另一种改善传统CD的可能性。

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