.png){kind=icon}
.png){kind=icon}
Return
Parametric Chunk Quantization Algorithm for Fast Passive Emitter Localization
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2026.3666895.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Passive emitter localization using airborne platforms presents a challenging grid-search problem, complicated by complex platform motion and unknown carrier frequency offsets. Conventional methods often address motion compensation and frequency offset correction as separate, inefficient, and potentially error-prone steps. This paper introduces the Parametric Chunk Quantization (PCQ) algorithm, a unified framework that accelerates the grid search while jointly compensating for both factors. Inspired by product quantization, PCQ divides the received signal and candidate phase histories into chunks, which are then parametrically approximated as linear frequency modulated (LFM) components. By leveraging a precomputed lookup table of inner products between these LFM surrogates, PCQ dramatically reduces the computational cost of the grid search. Simulations using real-world UAV trajectory data demonstrate that PCQ achieves significant acceleration over conventional methods while maintaining competitive localization accuracy. The proposed technique offers a generalizable approach for accelerating parameter estimation in problems involving piecewise-LFM signals.
Keywords:
Emitter localization
grid search optimization
parametric quantization
synthetic aperture techniques
Journal
I
IF:
3.9
Papers:
600
Citations:
0