However, disaster environments are often characterized by complex spatial structures, severe infrastructure damage, and harsh environmental conditions, particularly in sheltered spaces, which exhibit \"blind, narrow, chaotic, and dangerous\" characteristics [6]. These extreme conditions hinder the effective application of existing localization and navigation technologies, failing to meet the critical need for autonomous localization and orientation of rescuers [7]. Consequently, research into autonomous localization and orientation for rescuers in disaster environments is paramount. With the global expansion of urban underground space development, underground space disasters and accidents have become more frequent, posing severe threats to lives and property [8]. As leading countries in underground space development, China, the United States, and Russia face severe challenges from natural disasters and security incidents, with an urgent need for underground disaster rescue operations. Unlike surface rescue, disaster sites in urban underground spaces often exhibit \"gray box\" characteristics, where spatial structures undergo drastic changes and infrastructure collapses, rendering traditional rescue support methods, such as communication, lighting, and broadcasting, ineffective [9]. This results in extremely harsh rescue environments, increasing the difficulty of rescue efforts and significantly raising casualty risks [10]. Accident investigation reports indicate that, aside from unavoidable factors, rescuers in underground disaster spaces often experience limited cooperation due to unclear positions and statuses, which is a major cause of casualties [11]. Therefore, developing localization and orientation technology for rescuers in complex spatial disaster sites has become a central focus for both the government and academia. To address the need for localization and orientation in such complex spatial environments, numerous scholars worldwide have conducted extensive research. Currently, localization and orientation methods rely on various technologies, including global navigation satellite systems (GNSS) [12], WIFI [13], cellular networks [14], communication base stations [15], computer vision [16], inertial navigation systems (INS) [17], ultra-wideband (UWB) [18], laser [19], Bluetooth [20], and wireless local area networks (WLAN), among others. For instance, [PERSON] et al. [21] proposed a hybrid GPS-INS method that improves localization accuracy in signal-intermittent environments by integrating GPS data and inertial measurements. [PERSON] et al. [22] employed the WIFI signal strength for indoor localization by constructing a WIFI fingerprint database, thereby enhancing the system's robustness in complex indoor environments. [PERSON] et al. [23] developed a UWB high-precision localization system that performed excellently in open environments but experienced significant accuracy degradation in cluttered underground environments. Visual simultaneous localization and mapping (SLAM) technology has also been widely used for real-time location tracking, as demonstrated by [PERSON] method [24], which effectively captures environmental features, although it performs unstably under poor lighting or drastic environmental changes. Additionally, [PERSON] et al. [25] integrated low-energy Bluetooth (BLE) with mobile devices to implement a portable indoor localization solution, though its applicability is limited in disaster-stricken underground areas with damaged infrastructure. However, the aforementioned technologies often perform poorly in urban underground disaster scenarios. GNSS and cellular signals often fail in shielded underground spaces, and WIFI signals in disaster sites are unstable, prone to failure, and have limited coverage [19; 26; 27]. Technologies such as Bluetooth, UWB, and WLAN rely on pre-deployed infrastructure, but in emergency situations, these facilities may be severely damaged or cannot be rapidly deployed, making it difficult to meet practical demand [28; 29].