Sinusoidal Parameter Estimation from Signed Measurements via Majorization-Minimization Based RELAX
원문 초록
We consider the problem of sinusoidal parameter estimation using signed observations obtained via one-bit sampling with fixed as well as time-varying thresholds. In a previous paper, a relaxation-based algorithm, referred to as 1bRELAX, has been proposed to iteratively maximize the likelihood function. However, the exhaustive search procedure used in each iteration of 1bRELAX is time-consuming. In this paper, we present a majorization-minimization (MM) based 1bRELAX algorithm, referred to as 1bMMRELAX, to enhance the computational efficiency of 1bRELAX. Using the MM technique, 1bMMRELAX maximizes the likelihood function iteratively using simple FFT operations instead of the more computationally intensive search used by 1bRELAX. Both simulated and experimental results are presented to show that 1bMMRELAX can significantly reduce the computational cost of 1bRELAX while maintaining its excellent estimation accuracy.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
From Roots to Rewards: Dynamic Tree Reasoning with RL
Analytic estimation of parameters of stochastic volatility diffusion models with exponential-affine characteristic function for currency option pricing
DCR: Quantifying Data Contamination in LLMs Evaluation
U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
A Simple Approximate Bayesian Inference Neural Surrogate for Stochastic Petri Net Models
$I^{2}$-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene Forecasting
토론
이 논문과 연결된 포럼 글이 아직 없습니다.