The utilization of Global Positioning System (GPS) for three-dimensional ionospheric electron density reconstruction, i.e., computerized ionospheric tomography (CIT), provides significant importance in investigating the internal structure, variations, and disturbances within the ionosphere. However, the ill-posed problem caused by insufficient observational data or uneven distribution will rely heavily on the selection of initial values, which are typically derived from empirical models with low precision. Aiming at this issue, this paper uses the TEC obtained by the spaceborne full polarization synthetic aperture radar (PolSAR) to correct the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM), thus improving the authenticity of the initial value. This study makes full use of the advantages of low frequency full PolSAR in ionospheric sounding, including high precision and resolution, as well as all-day and all-weather operation without a ground receiver. Therefore, the precision of GPS-based tomography can be enhanced, particularly for small-scale anomalies, and it is also simple and easy to achieve. Numerical and measured experiments using GPS, incoherent scatter radar, PolSAR, and E-CHAIM data in Alaska demonstrate that the reconstruction accuracy of the proposed CIT is significantly improved than that of the tomography results using only empirical model. In addition, the effects of PolSAR system errors and voxel size on CIT are analyzed to demonstrate the robustness of the method proposed in this paper.

Target parameter estimation is considered for the cooperative multiple‐input multiple‐output (MIMO) radar and MIMO communications system. To address the hardware limitation of the radar system, a joint transmitter selection and receiver placement (JTSRP) problem is formulated to minimise the estimation performance benchmarked by the Cramer–Rao bound (CRB), where the transmitters can be selected from a discrete set and the receivers can be deployed over a continuous region. To efficiently solve this mix‐integer non‐linear programming problem approximately, a genetic algorithm (GA)‐based method is proposed. It is shown that the result obtained by the proposed algorithm is close enough to the optimum solution of the JTSRP problem. Numerical examples are presented to analyse the performance of the cooperative system designed by the GA‐based JTSRP.

The use of low-frequency spaceborne fully-polarized synthetic aperture radar (SAR), such as the ALOS PALSAR, to detect the structure of ionospheric has been widely validated in recent years. In terms of total electron content (TEC) retrieval, SAR exhibits significant advantages over GPS with regard to both accuracy and resolution at high-latitude. However, TEC cannot reflect the vertical structure of the ionosphere and still has limitations. On this basis, this letter presents a study into the application of fully-polarimetric PALSAR in computerized ionospheric tomography (CIT), which extends the horizontal ionospheric structure to the vertical, i.e., the three-dimensional (3-D) ionospheric electron density (IED) distribution. The reconstruction results from three groups of PALSAR measured data in Alaska are compared with the results from the nearby incoherent scatter radar (ISR). The accuracy has improved by 28.9%, 33.2%, and 47.6% respectively compared to the ionosphere model. This shows that the PALSAR has the ability to not only determine the horizontal structure of the ionosphere but also accurately reconstruct the 3-D IED, making it a promising approach for ionosphere sounding at high-latitude.

In the theoretical calculation of atmospheric wind fields using the cross-correlation analysis method of Medium-Frequency radar, it is necessary to compute a series of correlation parameters from the received echo signals, such as autocorrelation and cross-correlation functions, within the main lobe range of the antenna array to retrieve atmospheric parameters. However, both theoretical analysis and practical applications have shown that the shape of correlation functions can be affected by atmospheric conditions and receiver noise, leading to significant biases in the estimated correlation parameters within the main lobe range. In this study, we theoretically analyze the influence of noise on the amplitude of autocorrelation and cross-correlation functions. We propose a noise reduction method based on the characteristics of correlation functions at the zero-delay point to calculate the noise factor and process the correlation functions within the main lobe range. Furthermore, we conduct simulation analysis to evaluate the performance of this noise reduction method and summarize the effects of the number of fitting points and fitting methods on the noise reduction performance.

全相关分析法(Full Correlation Analysis, FCA)是常用的中频 (Medium Frequency Radar, MF)雷达风场反演算法, 但其对噪声较为敏感, 对噪声的有效处理可以帮助提升FCA方法在MF雷达风场反演中的准确性. 目前, MF雷达广泛采用的多项式拟合降噪法在不同噪声环境下的处理能力不一致, 导致MF雷达所能观测到的有效风场数据变少, 制约了MF雷达在MLT区域风场观测的应用前景. 因此, 寻找一类对噪声强度不敏感的降噪方法是提高MF雷达大气风场反演有效性的一条新思路. 本文首次将天线贡献值参数引入MF雷达, 提出了以天线贡献值参数为评价标准的MF-AH算法,对MF雷达接收信号生成的相关函数进行有效降噪处理. 将该算法与现有成熟雷达的多项式拟合方法相比, 通过模型数据和实测数据验证, MF-AH算法在低信噪比条件下将纬向风速和经向风速的误差降低了约 20%. 同时, 该算法摒弃了以噪声为核心评价指标的限制, 显著提升了风场数据的有效性和丰富性.