Array design for MEM and MLM array processing

Lang, S.; Duckworth, Gregory L.; McClellan, J.H.

doi:10.1109/icassp.1981.1171338

Cited by 40 publications

(10 citation statements)

References 9 publications

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“…Designing arrays for high resolution array processing was also addressed in [33]. Drawing inspiration from the MIMO radar literature, we proposed a novel nested array structure which can realize significantly more degrees of freedom even in the passive sensing scenario.…”

Section: Discussionmentioning

confidence: 99%

Nested Arrays: A Novel Approach to Array Processing With Enhanced Degrees of Freedom

Pal

Vaidyanathan

2010

IEEE Trans. Signal Process.

1,620

1,476

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Abstract-A new array geometry, which is capable of significantly increasing the degrees of freedom of linear arrays, is proposed. This structure is obtained by systematically nesting two or more uniform linear arrays and can provide ( 2 ) degrees of freedom using only physical sensors when the second-order statistics of the received data is used. The concept of nesting is shown to be easily extensible to multiple stages and the structure of the optimally nested array is found analytically. It is possible to provide closed form expressions for the sensor locations and the exact degrees of freedom obtainable from the proposed array as a function of the total number of sensors. This cannot be done for existing classes of arrays like minimum redundancy arrays which have been used earlier for detecting more sources than the number of physical sensors. In minimum-input-minimum-output (MIMO) radar, the degrees of freedom are increased by constructing a longer virtual array through active sensing. The method proposed here, however, does not require active sensing and is capable of providing increased degrees of freedom in a completely passive setting. To utilize the degrees of freedom of the nested co-array, a novel spatial smoothing based approach to DOA estimation is also proposed, which does not require the inherent assumptions of the traditional techniques based on fourth-order cumulants or quasi stationary signals. As another potential application of the nested array, a new approach to beamforming based on a nonlinear preprocessing is also introduced, which can effectively utilize the degrees of freedom offered by the nested arrays. The usefulness of all the proposed methods is verified through extensive computer simulations.

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Section: Discussionmentioning

confidence: 99%

Nested Arrays: A Novel Approach to Array Processing With Enhanced Degrees of Freedom

Pal

Vaidyanathan

2010

IEEE Trans. Signal Process.

1,620

1,476

View full text Add to dashboard Cite

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“…Co-array Design A common approach to choose sensor locations for a variety of problems is to examine the co-arrays formed by the sensor locations [8,9]. Recently, Pal and Vaidyanathan [10] proposed an alternate method to find sensor positions with desired co-array properties through the use of nested uniform arrays.…”

Section: Relation To Prior Workmentioning

confidence: 99%

Optimal beam pattern design for very large sensor arrays with sparse sampling

Lai

Bălan

Claussen

et al. 2013

2013 Asilomar Conference on Signals, Systems and Computers

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The main goal of this work is to adaptively employ a large set of microphone sensors distributed in multiple dimensions to scan an acoustic field. Processing data from a large set of sensors will necessarily involve intelligent definition of suitable subsets of sensors active at various times. This paper presents a novel method for optimal beam pattern design for large scale sensor arrays using convex and non-convex optimization techniques to define optimal subsets of sensors capable to select a target location while suppressing a large number of interferences. The first of two optimization techniques we present, uses a LASSO-type approach to convexify the corresponding combinatorial optimization problem. The second approach employs simulated annealing to search for optimal solutions with a fixed size subset of active sensors. Our numerical simulations show that for scenarios of practical interest, the convex optimization solution is almost optimal.Index Terms-array processing, beam pattern design, sparse sampling, very large scale arrays, convex optimization INTRODUCTIONConsider a large scale sensor array having N sensors (think of N in the order 1000's) that monitors a surveillance area. Data could be collected from all sensors, processed locally, and further communicated to a central processing unit. For example, for N = 10000 sensors and a data sampling rate of 100000 samples per second, the bandwidth requirements for a system performing exhaustive sampling is 1Gsamples/sec. Alternatively, the central processing unit could implement a policy of sparse spatial sampling where only a subset of sensors is polled for data at any time. This may be desirable for practical reasons: cost (in power consumption, communication or processing bandwidth) is simply too high for an exhaustive strategy. This paper advocates the need for advanced, dynamic control and signal processing of what data should be manipulated in place of the full extent of the data, and it introduces novel strategies and formal analysis of what can be achieved when only a sparse subset of sensors is sampled simultaneously.Assume a surveillance area that consists of a set of point-like sources that we are ultimately interested to separate. Our task is to sample the spatial field using a maximum of D sensors of the very large array (D N ) and then to process the raw data in order to estimate each source (see also Figure 1 for a setup scenario). We will thus design and analyze sparse spatial sampling strategies with near-optimal performance. Specifically, we target designs that simultaneously maximize target location gain and minimize the largest

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“…If these are in fact known accurately then the specular direction can be expressed as a function of that of the direct: k2 = g(ki,R,h r) (7) The CRLB for this coupled method includes a term 0k2/ak1 which can be shown to very nearly equal -1 for a wide range of low-angle multipath environments (including that of the Lake Huron site). The improvement in the bound offered by this method as opposed to treating k2 independant of k1 is indicated in figs.…”

Section: Mfblp Algorithmmentioning

confidence: 99%

High-Resolution Techniques for Angle-of-Arrival Estimation

Kezys

Vertatschitsch

Greenlay

et al. 1986

MILCOM 1986 - IEEE Military Communications Conference: Communications-Computers: Teamed for the 90's

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The intent of this paper, in part, is to discuss the implementation of a sampled-aperture radar facility and to address two algorithms applicable to angle-of-arrival estimation in the presence of multipath: modified forward-backward linear prediction (MFBLP) and classical maximum liklihood (ML). In the MFBLP approach, the angle-of-arrival estimation is viewed as a wave-number spectrum estimation problem. Results are presented using both simulated and real *data.For the ML method, formulation of the liklihood function is developed under the provision that knowledge of the range is available and that the aperture has a multi-frequency capability. Bounds are computed, simulations of the estimation are performed, and comparisons are made with the MFBLP method.The remaining part of the paper deals with an extension of the maximum-liklihood estimation algorithm applied to non-uniform sampled-aperture arrays and the development of tight bounds on the estimation. INTRODUCTIONThe signal received at the ith element of a N element linear array, due to a single target in the presence of multipath can be modelled as:

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Array design for MEM and MLM array processing

Cited by 40 publications

References 9 publications

Nested Arrays: A Novel Approach to Array Processing With Enhanced Degrees of Freedom

Nested Arrays: A Novel Approach to Array Processing With Enhanced Degrees of Freedom

Optimal beam pattern design for very large sensor arrays with sparse sampling

High-Resolution Techniques for Angle-of-Arrival Estimation

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