Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.最新文献_第3页

A Wideband Compressive Receiver for Real-time Signal Detection 用于实时信号检测的宽带压缩接收机

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706166

Cheol-Sun Park, Jun-Ho Choi, Daeyoung Kim, Joong-Soo Lim

引用次数: 4

Unitary Cyclic MUSIC for Direction Finding in GPS Receivers 用于GPS接收机测向的单循环MUSIC

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706093

M. Sahmoudi, M. Amin

引用次数: 10

Optimal Adaptive Array and Angle Tracking for Multiple Targets - A Re-examination of Optimal Array Processing 多目标最优自适应阵列与角度跟踪——对最优阵列处理的再考察

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706115

H. Gu

{"title":"Optimal Adaptive Array and Angle Tracking for Multiple Targets - A Re-examination of Optimal Array Processing","authors":"H. Gu","doi":"10.1109/SAM.2006.1706115","DOIUrl":"https://doi.org/10.1109/SAM.2006.1706115","url":null,"abstract":"The optimal array for detecting the signal from a desired direction but contaminated by receiver noise and strong interference from different sources with unknown arriving-angles is re-examined. The well-known optimal array is obtained by inverting the covariance matrix of interference and noise to maximize the signal to interference and noise ratio (SINR). In practice, the covariance matrix is unknown and has to be estimated by a sample covariance matrix. The optimal array is thus estimated by inverting the sample covariance matrix. This procedure has been employed in optimal array research without challenge. However, it is shown in this paper that the estimated optimal array fails to yield the highest SINR in the case of unknown arriving-angles. Instead the highest SINR can be achieved by optimally estimating the arriving-angles of interference followed by a constrained matched filter, which maximizes the signal to noise ratio subject to canceling the interference from the estimated arriving-angles. In order to reduce the computational burden, an angle-tracking system for multiple targets is adopted to achieve the optimal estimation of arriving-angles. The resulting system of angle-tracking adaptive array offers the highest SINR at a computational burden only on 2 the order of N middot M2 multiplications within a radar range-cellDeltatau, rather than N3 multiplications in the well-known but questionable estimated optimal array. Here N is the number of sensors in the array and M the number of interference sources. Typically, N = 1,000 in a planar radar array, M = 2-10 and Deltatau = 1 mus. Numerical simulations confirm the theoretical results","PeriodicalId":272327,"journal":{"name":"Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.","volume":"54 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":"121317800","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: 1

On the Threshold Region Mean-Squared Error Performance of Maximum-Likelihood Direction-of Arrival Estimation in the Presence of Signal Model Mismatch 信号模型不匹配情况下最大似然到达方向估计的阈值区域均方误差性能

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706135

C. Richmond

{"title":"On the Threshold Region Mean-Squared Error Performance of Maximum-Likelihood Direction-of Arrival Estimation in the Presence of Signal Model Mismatch","authors":"C. Richmond","doi":"10.1109/SAM.2006.1706135","DOIUrl":"https://doi.org/10.1109/SAM.2006.1706135","url":null,"abstract":"The mean squared error (MSE) performance prediction of maximum-likelihood (ML) direction-of-arrival (DOA) angle estimation has been studied extensively. Previous analyses consider Cramer-Rao Bounds, sensitivity/asymptotic [in signal-to-colored noise ratio (SNR)] local error performance prediction that includes the impact of finite samples effects and additive signal modeling errors (mismatch), and prediction of the low SNR threshold region performance of ML DOA (without mismatch). Analysis of the adaptive array ML DOA (without mismatch) scenario has also been considered. The goals of this present analysis include the following: (i) to extend prediction of the asymptotic and threshold region MSE performance of ML to include a general form of deterministic signal model mismatch, and (ii) to begin looking at the threshold region performance of ML DOA estimation from an information-theoretic perspective, (iii) to determine if the classic work of Huber on model misspecification, although primarily asymptotic in nature, provide new insights into this finite sample problem. This initial work will focus on the DOA estimation of a single deterministic planewave signal in known colored noise and brief consideration will be given to the more complex scenario of an adaptive array in which the colored noise covariance must be estimated","PeriodicalId":272327,"journal":{"name":"Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.","volume":"73 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":"121419663","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: 13

Design of Reduced-Rank MVDR Beamformers under Finite Sample-Support 有限样本支持下的降阶MVDR波束形成器设计

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706078

F. Rubio, X. Mestre

{"title":"Design of Reduced-Rank MVDR Beamformers under Finite Sample-Support","authors":"F. Rubio, X. Mestre","doi":"10.1109/SAM.2006.1706078","DOIUrl":"https://doi.org/10.1109/SAM.2006.1706078","url":null,"abstract":"The design of optimal reduced-rank minimum variance beamformers based on the Krylov-subspace spanned by the covariance matrix of the array observations is addressed. We concentrate on finite sample-support situations that naturally appear in practice when the number of antennas and the sample-size are comparable in magnitude. The design of the coefficients of the resulting polynomial expansion is approached by first approximating the signal-to-interference-plus-noise ratio (SINR) at the output of the antenna array in the small sample-size regime defined assuming that both the number of samples and the observation dimension grow together without bound at the same rate. Limiting SINR values in this double-limit context are very representative of the reality because, as it happens to be the case in realistic scenarios, both quantities are considered to be of the same order of magnitude. The building blocks of the (asymptotic) output SINR expression are spectral functions of the true covariance matrix that can be estimated using the statistical theory of large observations (or general statistical analysis) developed by Girko. This paper proposes a consistent estimator of the reduced-rank minimum variance beamformer that is consistent when both the number of antennas and the sample-support go to infinity with a constant ratio between them","PeriodicalId":272327,"journal":{"name":"Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.","volume":"63 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":"114206137","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: 1

Blind Detection, Separation, and Location of Dense Co-Channel Emitters using Multiplatform Spatial-Coherence Restoral 基于多平台空间相干恢复的密集同通道发射体盲检测、分离和定位

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706167

B. Agee

{"title":"Blind Detection, Separation, and Location of Dense Co-Channel Emitters using Multiplatform Spatial-Coherence Restoral","authors":"B. Agee","doi":"10.1109/SAM.2006.1706167","DOIUrl":"https://doi.org/10.1109/SAM.2006.1706167","url":null,"abstract":"The multiplatform spatial-coherence restoral (MP-SCORE) algorithm, introduced in 1988 to blindly separate spatially-coherent, temporally featureless signals based on their differing TDOA and FDOA at spatially separated antenna arrays, is extended to blind detection, separation, and location of dense co-channel emitters using wide-baseline collection networks. The original MP-SCORE algorithm is reviewed and related to the ML estimator of the location of a Gaussian-distributed source (or of the location and content of an unknown source) received in the presence of independent Gaussian-distributed interference with arbitrary spatial covariance at each array in the network. False-alarm and miss-rates for the corresponding MP-SCORE detector are also derived, and are shown to be a function of only the time-bandwidth product of the detector and the maximum source SENR attainable at each array in the network. Practical methods for detecting, separating, and locating multiple co-channel sources using MP-SCORE are then presented, and the end-to-end algorithm is demonstrated for a GPS C/A code jamming scenario in which two 8-sensor airborne receivers attempt to detect and locate 100 narrowband (2 MHz) noise-loaded emitters continuously transmitting over a wide (150times150 nmi) geographical region at the center of the GPS L1 frequency band. The simulated algorithm detects and geolocates 27 emitters to within 1/2 nmi over a single 26 ms snapshot, and detects and geolocates 38 emitters to within 445 feet over eleven 26 ms snapshots collected at 15 nmi intervals along the edge of the emission region","PeriodicalId":272327,"journal":{"name":"Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.","volume":"151 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":"116093675","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: 0

Sensor Scheduling using a 0-1 Mixed Integer Programming Framework 基于0-1混合整数规划框架的传感器调度

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706178

A. Chhetri, D. Morrell, A. Papandreou-Suppappola

引用次数: 19

Incorporating pulse-to-pulse motion effects into side-looking array radar models 将脉冲对脉冲运动效应整合到侧视阵列雷达模型中

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706200

J. W. Green, T. Hale, M. Temple, J. Buckreis

引用次数: 2

Sensor Management with Uncertain Performance Characteristics 具有不确定性能特性的传感器管理

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706175

M. Kolba, L. Collins

{"title":"Sensor Management with Uncertain Performance Characteristics","authors":"M. Kolba, L. Collins","doi":"10.1109/SAM.2006.1706175","DOIUrl":"https://doi.org/10.1109/SAM.2006.1706175","url":null,"abstract":"Previous work has presented an information-theoretic sensor management framework for the detection of static targets. This framework is based on the expected discrimination gain maximization technique of Kastella. The sensor manager searches for N targets within a grid of cells using M sensors, which may be thought of as a reconfigurable sensor array. Sensor probabilities of detection and false alarm are used in the mathematical structure of the sensor manager, and these probabilities have previously been assumed to be certain. Realistic problems, however, will inevitably involve uncertainty. This paper introduces uncertain sensor Pd and Pf values into the mathematical framework and allows for their incorporation into the sequential structure of the sensor manager. The performance of the presented sensor management technique is then compared to direct search, where the sensors sweep through the grid in a predefined sampling pattern. The sensor manager is found to be superior to direct search when the uncertainty present in the problem is properly modeled. When uncertainty is present but not modeled, the performance of the sensor manager is severely degraded. This result indicates that uncertainty modeling will be important and necessary for the successful application of the presented sensor manager to real-world problems. Additional simulations examine the robustness of the sensor manager to errors in the assumed densities for Pd and Pf, and the performance of the sensor manager is found to remain strong even when the assumed Pd and Pf densities differ from the true densities","PeriodicalId":272327,"journal":{"name":"Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.","volume":"65 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":"115828858","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

Performance analysis of Krylov space adaptive beamformers 克雷洛夫空间自适应波束形成器性能分析

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. Pub Date : 2006-07-12 DOI: 10.1109/SAM.2006.1706075

I. Kirsteins, H. Ge

引用次数: 20