Simultaneously estimating plasma parameters of the ionosphere presents a problem for the incoherent scatter radar (ISR) technique at altitudes between ~ 130 and ~ 300 km. Different mixtures of ion concentrations and temperatures generate almost identical backscattered signals, hindering the discrimination between plasma parameters. This temperature–ion composition ambiguity problem is commonly solved either by using models of ionospheric parameters or by the addition of parameters determined from the plasma line of the radar. Some studies demonstrated that it is also possible to unambiguously estimate ISR signals with very low signal fluctuation using the most frequently used non-linear least squares (NLLS) fitting algorithm. In a previous study, the unambiguous estimation performance of the particle swarm optimization (PSO) algorithm was evaluated, outperforming the standard NLLS algorithm fitting signals with very small fluctuations. Nevertheless, this study considered a confined search range of plasma parameters assuming a priori knowledge of the behavior of the ion composition and signals with very large SNR obtained at the Arecibo Observatory, which are not commonly feasible at other ISR facilities worldwide. In the present study, we conduct Monte Carlo simulations of PSO fittings to evaluate the performance of this algorithm at different signal fluctuation levels. We also determine the effect of adding different combinations of parameters known from the plasma line, different search ranges, and internal configurations of PSO parameters. Results suggest that similar performances are obtained by PSO and NLLS algorithms, but PSO has much larger computational requirements. The PSO algorithm obtains much lower convergences when no a priori information is provided. The a priori knowledge of N e and $${T}_{e}/{T}_{i}$$ T e / T i parameters shows better convergences and “correct” estimations. Also, results demonstrate that the addition of $${N}_{e}$$ N e and $${T}_{e}$$ T e parameters provides the most information to solve the ambiguity problem using both optimization algorithms.

中文翻译：

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从非相干散射雷达信号明确估计等离子体参数的粒子群优化算法的性能评估

同时估计电离层的等离子体参数给非相干散射雷达 (ISR) 技术在约 130 至约 300 公里的高度带来了一个问题。离子浓度和温度的不同混合物产生几乎相同的反向散射信号，阻碍了等离子体参数之间的区分。这种温度-离子成分模糊问题通常可以通过使用电离层参数模型或添加从雷达等离子体线确定的参数来解决。一些研究表明，使用最常用的非线性最小二乘 (NLLS) 拟合算法也可以明确估计具有非常低信号波动的 ISR 信号。在之前的一项研究中，评估了粒子群优化 (PSO) 算法的明确估计性能，其性能优于标准 NLLS 算法以非常小的波动拟合信号。然而，这项研究考虑了等离子体参数的有限搜索范围，假设对离子成分的行为和在阿雷西博天文台获得的具有非常大 SNR 的信号有先验知识，这在全球其他 ISR 设施中通常是不可行的。在本研究中，我们对 PSO 拟合进行蒙特卡罗模拟，以评估该算法在不同信号波动水平下的性能。我们还确定了添加从血浆线、不同搜索范围和 PSO 参数的内部配置中已知的参数的不同组合的影响。结果表明 PSO 和 NLLS 算法获得了相似的性能，但 PSO 具有更大的计算要求。当没有提供先验信息时，PSO 算法的收敛性要低得多。N e 和 $${T}_{e}/{T}_{i}$$ T e / T i 参数的先验知识显示出更好的收敛性和“正确”估计。此外，结果表明，添加 $${N}_{e}$$ N e 和 $${T}_{e}$$ T e 参数为使用两种优化算法解决歧义问题提供了最多的信息。