Beamforming Using Passive Nested Arrays of Sensors

A novel approach to beamforming using a new class of sensor arrays is proposed, which can increase the achievable degrees of freedom significantly beyond the conventional limits obtained from uniform linear arrays (ULA). This class of arrays is named as “nested arrays” since they are obtained by nesting two or more ULAs with increasing inter-sensor spacing. Using the second order statistics of the signal received by such an array in a novel way, it is possible to perform beamforming with O(N^2) degrees of freedom using only O(N) physical elements. This kind of beamforming will be shown to be essentially non linear in nature and theoretically, it is capable of nulling the effect of noise provided enough snapshots are available

Study of Enhanced MISC-Based Sparse Arrays with High uDOFs and Low Mutual Coupling

In this letter, inspired by the maximum inter-element spacing (IES) constraint (MISC) criterion, an enhanced MISC-based (EMISC) sparse array (SA) with high uniform degrees-of-freedom (uDOFs) and low mutual-coupling (MC) is proposed, analyzed and discussed in detail. For the EMISC SA, an IES set is first determined by the maximum IES and number of elements. Then, the EMISC SA is composed of seven uniform linear sub-arrays (ULSAs) derived from an IES set. An analysis of the uDOFs and weight function shows that, the proposed EMISC SA outperforms the IMISC SA in terms of uDOF and MC. Simulation results show a significant advantage of the EMISC SA over other existing SAs.Comment: 6 pages 4 figure

Maximizing the Number of Spatial Nulls with Minimum Sensors

In this paper, we attempt to unify two array processing frameworks viz, Acoustic Vector Sensor (AVS) and two level nested array to enhance the Degrees of Freedom (DoF) significantly beyond the limit that is attained by a Uniform Linear Hydrophone Array (ULA) with specified number of sensors. The major focus is to design a line array architecture which provides high resolution unambiguous bearing estimation with increased number of spatial nulls to mitigate the multiple interferences in a deep ocean scenario. AVS can provide more information about the propagating acoustic field intensity vector by simultaneously measuring the acoustic pressure along with tri-axial particle velocity components. In this work, we have developed Nested AVS array (NAVS) ocean data model to demonstrate the performance enhancement. Conventional and MVDR spatial filters are used as the response function to evaluate the performance of the proposed architecture. Simulation results show significant improvement in performance viz, increase of DoF, and localization of more number of acoustic sources and high resolution bearing estimation with reduced side lobe level

A novel array structure for directions-of-arrival estimation with increased degrees of freedom

A novel array structure for 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 O(N^2) degrees of freedom using only O(N) physical sensors. 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 sensors. A novel spatial smoothing based technique is also proposed to exploit the increased degrees of freedom offered by the array to perform DOA estimation of more sources than sensors, using only second order statistics of the received data. This method does not suffer from inherent weaknesses of techniques employing higher order statistics or quasi stationarity of sources. The validity of all the proposed methods is verified through numerical examples