{"title":"Beamforming Design for Minimizing the Signal Power Estimation Error","authors":"Esa Ollila;Xavier Mestre;Elias Raninen","doi":"10.1109/TSP.2026.3673368","DOIUrl":null,"url":null,"abstract":"We study the properties of beamformers in their ability to either maintain or estimate the true signal power of the signal of interest (SOI). Our focus is particularly on the Capon beamformer and the minimum mean squared error (MMSE) beamformer. The Capon beamformer, also known as the minimum power distortionless response (MPDR) or the minimum variance distortionless response (MVDR) beamformer, is a widely used method in array signal processing. A curious feature of both the Capon and the MMSE beamformers is their tendency to either overestimate or underestimate the signal power. That is, the power estimates of their output signals are not asymptotically unbiased (as the sample size approaches infinity). To address this issue, we propose to shrink the Capon beamformer by finding a scaling factor that minimizes the mean squared error (MSE) of the signal power estimate. The new beamformer, referred to as the Capon<inline-formula><tex-math>${}^{+}$</tex-math></inline-formula> beamformer, is evaluated against the Capon and MMSE beamformers in terms of signal power bias and MSE, and signal waveform MSE. The Capon<inline-formula><tex-math>${}^{+}$</tex-math></inline-formula> beamformer strikes a better balance between signal power and waveform estimation while also exhibiting minimal bias, which approaches zero as the sample size increases.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"1206-1217"},"PeriodicalIF":5.5000,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11432948","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11432948/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/3/12 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
We study the properties of beamformers in their ability to either maintain or estimate the true signal power of the signal of interest (SOI). Our focus is particularly on the Capon beamformer and the minimum mean squared error (MMSE) beamformer. The Capon beamformer, also known as the minimum power distortionless response (MPDR) or the minimum variance distortionless response (MVDR) beamformer, is a widely used method in array signal processing. A curious feature of both the Capon and the MMSE beamformers is their tendency to either overestimate or underestimate the signal power. That is, the power estimates of their output signals are not asymptotically unbiased (as the sample size approaches infinity). To address this issue, we propose to shrink the Capon beamformer by finding a scaling factor that minimizes the mean squared error (MSE) of the signal power estimate. The new beamformer, referred to as the Capon${}^{+}$ beamformer, is evaluated against the Capon and MMSE beamformers in terms of signal power bias and MSE, and signal waveform MSE. The Capon${}^{+}$ beamformer strikes a better balance between signal power and waveform estimation while also exhibiting minimal bias, which approaches zero as the sample size increases.
期刊介绍:
The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others, audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Examples of topics of interest include, but are not limited to, information processing and the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.