## 1 Introduction
Numerous studies seeking to find more effective signal sources, except for the most-used GNSS signals received by the ground-based receivers, have been performed to raise the rank of the coefficient matrix to further improve the accuracy of CIT [4]. [PERSON] utilized diverse data sources including tri-frequency beacon signals to improve the accuracy of CIT [13]. [PERSON] proposed that the use of PolSAR signal data can better reconstruct the electron density, and this method improves resolution and does not require extra ground-based receiving stations, which significantly reduces the cost of ionospheric detection [14; 15]. [PERSON] investigated the use of shipborne AIS and airborne ADS-B data received by receivers loaded on GNSS to realize 2D CIT, which was a brand-new idea for effective signal sources [16; 17]. Global navigation satellite system (GNSS) signals implemented by low-earth-orbit (LEO) satellite constellation signals were applied for ionospheric tomography by [PERSON] to obtain more effective data for three-dimensional ionospheric electron density reconstruction [18]. Inspired by the above researchers' work on improving the rank of the coefficient matrix, we turn our attention to small-satellite constellations operating in low orbit. The number of global small-satellite launches has increased explosively in recent years, and small satellites have the advantage of being flexible and cost-effective [19]. Moreover, small satellites that can be operated as constellations contain more than one small satellite to achieve more powerful functionality and broader coverage. Considering these advantages, we try to utilize a small-satellite system carrying AIS receivers as the signal source for 3D CIT over ocean areas. As for the initial electron density applied in CIT, the electron density obtained using the international reference ionosphere (IRI) model [20] is the most used for the ART and MART iteration algorithms. During the iteration process, the initial electron density value in the voxels without any traversing signal rays will remain unchanged during the iteration process both in ART and MART. Thus, in the CIT process, the reconstructed electron density in voxels without signal rays depends totally on the initial value. The weighted international reference ionosphere 1995 (IRI-95) is used as the initial guess to solve the sparse problem in the CIT process [21]. [PERSON] applied the electron density calculated by the total-least-squares (TLS) algorithm as the input of the ART to achieve more accurate results compared to the reconstruction result obtained by only ART or only TLS [22]. Considering the advantage of the truncated singular value decomposition (TSVD) method for CIT--that no prior values are needed and whose result is an approximation of the real electron density--D. [PERSON] combined TSVD and MART to solve the ill-posed problem in 3D CIT. They utilized the TSVD result as the initial value for the ART iteration [23; 24]. [PERSON] used the total electron content (TEC) obtained by the spaceborne full-polarization synthetic aperture radar (PolSAR) to correct the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM), to improve the authenticity of the initial \(\mathrm{n_{e}}\) for MART [25]. With the develop of machine learning techniques, a neural network (NN) was applied by [PERSON] to obtain high-precision initial ionospheric electron density for computerized ionospheric tomography (CIT), and better ionospheric electron density was reconstructed using IRI-2020 as the initial guess than using CIT [26]. All these methods of optimizing the initial electron density improve the accuracy of CIT, and it can be inferred that the evaluation and optimization of initial electron density is necessary for CIT. An adjustment factor \(\delta\) is proposed in this paper to evaluate and optimize the initial \(\mathrm{n_{e}}\) obtained by the IRI-2020; this method is introduced and validated in Sections 2 and 4, along with comparisons of CIT at different times with different \(\delta\).