## 1 Introduction  
With the intensification of global climate change, tropical cyclones (TCs) have become one of the significant challenges faced globally. TCs are intense cyclonic systems originating over tropical or subtropical waters, causing extensive loss of life and property. Therefore, accurately monitoring and forecasting the location and intensity of TCs are crucial for effectively reducing human casualties and economic losses. Particularly, precisely estimating the TC center is key [1; 2; 3]. Due to their broad coverage and high imaging rate, visible and infrared sensors have been widely used to monitor TCs [4]. These sensors have facilitated the development of various methods for TC-center estimation, such as the logarithmic-spiral curve-fitting algorithm [5], genetic algorithm [6], chaos immune evolutionary algorithm [7], ellipse curve model [8], brightness-temperature gradient-based algorithm [9], and weight slice method [10]. These methods are based on the morphological characteristics of TCs in visible and infrared imagery; however, visible and infrared sensors can only record the cloud-top manifestations of TCs. Since the TC center positions near the sea surface and at the cloud top are often uncoordinated [11; 12], it is necessary to estimate the TC center positions near the sea surface. In recent years, Synthetic Aperture Radar (SAR) has become a powerful tool for monitoring TCs with the advantages of all-time and all-weather application and high spatial resolution [13; 14]. SAR can provide critical information such as the surface wind field and TC center position [15]. This information is vital for assessing the TC intensity and potential destructiveness [16; 17]. Nonetheless, the long revisit cycles of SARs, such as the 24-day cycle of RADARSAT-2 and the 6-day cycle of Sentinel-1A/B, limit their capacity for timely monitoring TCs [12]. With advancements in sensor technology, SAR is evolving toward miniaturization. Compared to traditional large SAR satellite missions, miniaturized SARs are characterized by their lower weight and cost, and they are easier to form constellations [18]. This leads to shorter revisit cycles. Therefore, miniaturized SARs hold significant potential for timely monitoring TCs. Typically, the Normalized Radar Cross-Section (NRCS) values are lower at the center of a TC compared to its surrounding areas in SAR imagery. This discrepancy facilitates the TC-center estimation by analyzing NRCS values. However, some phenomena associated with TCs, such as rainbands, also exhibit lower NRCS values in SAR imagery. Hence, relying solely on NRCS values is not robust for TC-center estimation. In the literature, various methods for estimating TC centers from SAR images have been proposed, including wavelet analysis [19], watershed segmentation [20], morphological analysis [21], and a particle swarm optimization algorithm [22]. Nonetheless, most of these methods are semi-automatic, requiring manual intervention. Hereafter, [23] proposed a fully automatic approach that estimates the TC center position by calculating the convergence degree of lines perpendicular to the inflow-angle-adjusted TC wind directions. With the rise of artificial intelligence (AI), there has been related research in TC-center estimation. For instance, in [24], deep learning techniques were employed to achieve automatic TC-center estimation in Sentinel-1 SAR images. Due to the relatively recent launch of the HISEA series of miniaturized satellites, the number of TC images captured is limited. This scarcity of data makes it difficult to provide a sufficient training dataset for AI-based TC-center estimation methods. Therefore, this study applied the algorithm proposed by [23] on three wide-swath miniaturized SAR images. The objective is to investigate the potential of miniaturized SAR in estimating TC centers and to discuss the robustness of this automatic TC-center estimation algorithm. The algorithm is organized into three stages: Stage 1, Stage 2, and an optional stage, detailed in Section 3 and [23].