Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.最新文献_第10页

Dynamical Systems for Principal Singular Subspace Analysis 主奇异子空间分析的动力系统

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1677175

M. Hasan

引用次数: 5

Forward-Looking Planar Array 3D-STAP using Space Time Illumination Patterns (STIP) 使用时空照明模式(STIP)的前视平面阵列3D-STAP

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706205

P. Corbell, M. Temple, T. D. Hale

{"title":"Forward-Looking Planar Array 3D-STAP using Space Time Illumination Patterns (STIP)","authors":"P. Corbell, M. Temple, T. D. Hale","doi":"10.1109/SAM.2006.1706205","DOIUrl":"https://doi.org/10.1109/SAM.2006.1706205","url":null,"abstract":"Close-in sensing is needed for urban warfare operations, where ground moving target indication (GMTI) could be provided via forward or rear-facing multi-function array radars mounted on small highly-maneuverable airborne platforms. However, airborne radar arrays oriented any direction other than side-looking cause an elevation dependent angle-Doppler relationship in the clutter returns. This non-stationarity is acute in close-in sensing geometries where elevation diversity exists over the scene of interest. However, planar arrays have an inherent advantage over linear arrays due to their ability to observe clutter statistics as a function of elevation. This paper demonstrates the utility of elevation diversity by synthesizing a single 3D-STAP filter that exhibits an elevation dependent azimuth-Doppler response which is tailored to null the clutter \"bowl\" which characterizes the forward-looking clutter spectrum. Such a capability is particularly exploitable on transmit, where all elevation angles are simultaneously illuminated. To demonstrate potential benefits, this paper proposes the use of recently developed space time illumination patterns (STIP) from a planar AESA to invoke elevation diverse space-time illumination in a forward-looking clutter scenario. It is shown that 3D-STIP (azimuth-elevation-Doppler) facilitates elevation specific space-time beamforming which removes the clutter energy from a given Doppler frequency across all ranges, potentially simplifying processing on receive. Simulations using synthesized training data and clairvoyant covariance knowledge are conducted to demonstrate proof-of-concept","PeriodicalId":272327,"journal":{"name":"Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116020953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Performance Analysis of Linear Predictive Super-Resolution Processing for Antenna Arrays 天线阵列线性预测超分辨率处理性能分析

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706112

Honglei Chen, D. Kasilingam

{"title":"Performance Analysis of Linear Predictive Super-Resolution Processing for Antenna Arrays","authors":"Honglei Chen, D. Kasilingam","doi":"10.1109/SAM.2006.1706112","DOIUrl":"https://doi.org/10.1109/SAM.2006.1706112","url":null,"abstract":"Many applications in array processing require a high resolution beamforming method. For example, spatial division multiple access (SDMA) has been proposed to achieve higher system capacity in wireless systems. Implicit in SDMA is the ability to adaptively generate narrow antenna beam patterns, which ensures the physical separation between users and minimizes multiple user interference. However, antenna theory requires large antenna apertures to generate narrow beam patterns. This paper discusses an adaptive algorithm for extrapolating measurements from a small antenna array to a much larger virtual array. The technique utilizes linear prediction (LP) methods to perform the extrapolation. A least mean square (LMS) based algorithm was used to estimate the LP coefficients. Since the algorithm is linear, it serves both as a direction-of-arrival (DoA) estimator and a matched filter for retrieving the transmitted signal. Other popular high-resolution algorithms such as MUSIC are inherently nonlinear and thus cannot be used on their own for retrieving signal information. The performance of this algorithm is studied with respect to interference suppression, noise and spatial resolution. It is shown that the HR filter representing the LP process has poles corresponding to the DoA of the signal. The resulting LP coefficients can eliminate the interference between signals from different transmitters. However, no improvement in noise performance is seen because the receiver noise couples into the extrapolation process when two transmitters are closely located. The extrapolation algorithm is compared with the LCMV algorithm and found to produce similar results","PeriodicalId":272327,"journal":{"name":"Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116313604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

MUSIC using Off-Origin Hessians of the Second Characteristic Function MUSIC使用Off-Origin Hessians的第二特征函数

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706116

A. Yeredor

{"title":"MUSIC using Off-Origin Hessians of the Second Characteristic Function","authors":"A. Yeredor","doi":"10.1109/SAM.2006.1706116","DOIUrl":"https://doi.org/10.1109/SAM.2006.1706116","url":null,"abstract":"In its classical form, the multiple signal classification (MUSIC) algorithm applies eigen-decomposition to the estimated correlation matrix of the noisy sensors' outputs in order to estimate the directions of arrival (DOAs) of several sources. When the noise is spatially white, consistent estimates of the DOAs are obtained. However, when the noise is spatially correlated (and / or has different variances at different sensors), the correlation-based MUSIC cannot produce consistent DOA estimates, unless the noise covariance is known in advance. To mitigate this drawback, the use of certain matrices of higher order statistics (HOS), mainly fourth-order cumulants, has been proposed, which can still lead to consistent DOA estimates when the noise is not spatially white, as long as it has a Gaussian distribution. However, estimates of the required HOS often introduce larger variances; Moreover, if the sources happen to have null cumulants of the chosen order, they remain unaccounted for in the algorithm. In this paper we propose a new target-matrix to substitute HOS as the input to MUSIC. Our target matrix is based on subtracting the observations' correlation matrix from Hessians of their second characteristic function, evaluated at several \"processing points\". These Hessians are related to the array steering-vectors (hence to the DOAs) in the same way as the correlation matrix, and can be easily estimated from the data. The subtraction of the correlation matrices eliminates (asymptotically) the effect of any additive independent Gaussian noise, hence maintaining consistency. We demonstrate the attainable improvement in comparative simulation","PeriodicalId":272327,"journal":{"name":"Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122950015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Multiple Window Bispectrum Estimator 多窗口双谱估计

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706179

Huixia He, D. Thomson

引用次数: 1

On Proposal Functions for Cost-Reference Particle Filtering 代价-参考粒子滤波的建议函数

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706181

M. Bugallo, M. Vemula, P. Djuric

引用次数: 2

Semi-Blind Source Separation for Memoryless Volterra Channels in UWB and its Uniqueness 超宽带无记忆Volterra信道的半盲源分离及其独特性

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706197

N. Petrochilos, K. Witrisal

引用次数: 11

Performance Analysis of Quantifying Fluorescence of Target-Captured Microparticles from Microscopy Images 从显微镜图像中定量捕获目标微粒的荧光性能分析

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706139

P. Sarder, A. Nehorai

引用次数: 1

Cramer-Rao Lower Bound for Prior-Subspace Estimation 先验子空间估计的Cramer-Rao下界

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706162

Remy Boyer, G. Bouleux

引用次数: 5

VisiBuilding: Sensing through Walls 视觉建筑:透过墙壁感知

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706221

E. Baranoski

引用次数: 19