Comprehensive Summary and Overview of Passive Localization Systems and Methods

Passive localization technology is a technique that determines target positions by receiving and analyzing reflected or radiated signals from targets without relying on their active signal transmission. It has broad applications in military reconnaissance, electronic warfare, and wireless communications. The core of passive localization lies in utilizing different signal characteristics and measurement parameters combined with corresponding localization algorithms to achieve accurate target position estimation.

Current mainstream passive localization systems include the following:

Time Difference of Arrival (TDOA) Based Localization This method calculates target position by measuring time differences of signal arrival at multiple receiving stations. Its advantage lies in not requiring time synchronization, but it is limited by time measurement accuracy and multipath effects. Code implementation typically involves cross-correlation algorithms to calculate time differences and hyperbolic positioning algorithms for coordinate estimation.

Frequency Difference of Arrival (FDOA) Based Localization Utilizes Doppler frequency shift differences of target signals between different receiving stations for localization, suitable for ranging and velocity measurement of high-speed moving targets. However, this method requires high precision in relative motion states of receiving stations. Implementation often involves phase difference calculation and Kalman filtering for motion parameter estimation.

Angle of Arrival (AOA) Based Localization Uses array antennas or directional antennas to measure signal incident angles, then calculates target position through geometric intersection. Advantages include no time synchronization requirement, but accuracy is limited by angle measurement errors and baseline length. Common implementations use MUSIC or ESPRIT algorithms for direction finding and triangulation methods for position calculation.

Hybrid Localization Methods Combines two or more systems from TDOA, FDOA, and AOA to improve localization accuracy and robustness. For example, TDOA+AOA combination can reduce dependence on time precision while enhancing localization accuracy. Implementation typically involves data fusion algorithms like least squares estimation or extended Kalman filters.

In comparative analysis, TDOA is suitable for static or low-speed targets, FDOA fits high-speed moving targets, while AOA performs well in short-range localization. Hybrid methods can integrate advantages of different systems to adapt to more complex application scenarios.

Final simulation experiments demonstrate that selecting appropriate localization systems and optimizing algorithm parameters (such as receiver station layout and signal processing methods) can significantly improve localization accuracy. Through source code implementation and data analysis, the applicability and limitations of different methods in various environments have been validated, with MATLAB or Python commonly used for algorithm prototyping and performance evaluation.