被動定位僅依賴訊號接收端進行定位，無需主動通訊，在具敵情威脅或通訊受限的環境下尤具優勢，近年廣泛應用於電子作戰與災害救援等任務中。低軌道衛星具備高覆蓋率、高重訪率與低通訊延遲等優勢，已成為電子偵察與頻譜監測等應用的重要平台，而立方衛星則因其低成本、模組化與部署彈性，更適合快速建構具擴展性與任務彈性的被動式定位系統。
本研究以雙星衛星任務架構為基礎，探討利用時間延遲差與都卜勒頻率差進行靜態發射源之被動定位。本文比較兩種定位架構之表現，分別為以交互模糊函數配合擴展卡爾曼濾波器和粒子濾波器實現之間接定位法，與以粒子濾波器實現之直接定位法，並進一步提出一種改良式粒子濾波策略，以解決直接定位方法在非凸目標空間中易受局部極值干擾而誤判問題，提升其收斂準確性與穩定性。
本論文建構之模擬平台整合了真實軌道元素與通訊鏈路模型，並透過多組蒙地卡羅實驗，分析不同積分時間、訊號頻寬、訊雜比及幾何配置條件下之定位誤差分布。藉此驗證並比較兩種定位方法在各種情境下的效能表現與適用性。

Passive localization techniques rely solely on signal reception without requiring active transmissions, offering clear advantages in contested or communication-constrained environments. These methods have been widely adopted in applications such as electronic warfare, spectrum monitoring, and disaster response. Satellites in low Earth orbit (LEO) offer several advantages, including wide coverage, high revisit frequency, and low communication latency, making them ideal platforms for space-based localization systems. CubeSats, due to their low cost, modular structure, and flexible deployment, are particularly well-suited for rapidly constructing scalable and mission-adaptive passive localization architectures.
This research is based on a dual-satellite mission architecture and investigates the use of time difference of arrival (TDOA) and frequency difference of arrival (FDOA) for passive localization of a stationary emitter. Two positioning approaches are evaluated: an indirect method utilizing the cross-ambiguity function (CAF) combined with an extended Kalman filter (EKF) or particle filter (PF), and a direct positioning determination (DPD) approach implemented solely via particle filtering. To address the issue of local extrema in non-convex search spaces, an improved particle filtering strategy is proposed to enhance the convergence accuracy and robustness of DPD.
A simulation platform integrating real orbital elements and a detailed communication link model is developed to support this study. Extensive Monte Carlo experiments are conducted to analyze localization error distributions under various integration times, signal bandwidths, signal-to-noise ratios (SNR), and geometric conditions. The results validate and compare the performance and applicability of both positioning approaches under diverse operational scenarios.

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