Explore MATLAB source code curated for "定位结果" with clean implementations, documentation, and examples.
Implementation of Bayesian inference-based data fusion to process results from CHAN and Taylor algorithms, achieving improved localization accuracy and reliability through probabilistic integration.
Utilizes Bayesian inference to process the outputs of CHAN and Taylor algorithms, achieving superior localization results through probabilistic data fusion. Validated through comprehensive testing with implementation involving Gaussian likelihood modeling and posterior probability optimization.
This MATLAB-based license plate localization algorithm employs row scanning methodology, delivering highly accurate positioning results with robust image preprocessing and edge detection capabilities.
A MATLAB-based GPS software receiver implementation covering the entire processing chain from digital IF signals to final positioning results. Contains valuable algorithmic references that are highly beneficial for GPS learning and research.
TDOA Positioning using Chan's Algorithm - A comprehensive implementation guide featuring multiple Weighted Least Squares (WLS) computations for scenarios where mobile stations are relatively close to base stations. Input parameters include R(N-1×1): TDOA measurement values and Pbs(N×2): Base station coordinates (X in first column, Y in second column) with reference base station coordinates in the first row. Output parameters comprise POS_ref(2×1): Initial WLS estimation result serving as reference for final positioning decision, along with multiple positioning solutions POS1(2×1), POS2(2×1), POS3(2×1), and POS4(2×1).