Beamforming with semi-coprime arrays

Adhikari, Kaushallya

doi:10.1121/1.5100281

Cited by 30 publications

(15 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, we choose the samples at (t k , t l ) = (n 1,k ∆ 1 , n 2,l ∆ 2 ) = for k = 1, 2..., M 1 and l = 1, 2..., M 2 . To find the (t k , t l ) values, it is only necessary to compute the samples of the integrand in (12). Denoting the integrand by g(t 1 , t 2 ), we can express its samples as…”

Section: Optimal Sampling Indices For Maximizing Detectionmentioning

confidence: 99%

“…S PARSE sampling of a signal in space or time domain has been a subject of much interest for decades. In particular, the array signal processing has seen much work in this area [1]- [12]. Most of the sparse designs in the literature have focused on eliminating aliasing artifacts due to undersampling and reducing the corresponding frequency response's peak side lobe height for a given main lobe width.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Sparse Sampling for Detection of a Known Signal in Nonwhite Gaussian Noise

Adhikari

Kay

2021

IEEE Signal Process. Lett.

Self Cite

View full text Add to dashboard Cite

We address the problem of sparse sampling pattern design to maximize detection for a deterministic signal in colored noise. We model a colored noise as a continuous-time autoregressive process, which is obtained by passing a white noise through a causal linear-time invariant filter. This noise modeling is crucial to the development of the optimal sampling pattern design for a given number of sensors. We obtain a closed form expression for the whitening filter and consequently, for the Kullback-Leibler divergence at the whitening filter output, which is the detection index. The optimum sampling pattern is obtained by evaluating the detection index at Nyquist sampling rate, rank ordering the samples, and selecting the maximum values. We present some examples to illustrate the proposed procedure. We also extend our method to two-dimensional sampling. The advantage of our approach is its low computational complexity for both one-dimensional and two-dimensional cases and that optimality is guaranteed.

show abstract

Section: Optimal Sampling Indices For Maximizing Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Sparse Sampling for Detection of a Known Signal in Nonwhite Gaussian Noise

Adhikari

Kay

2021

IEEE Signal Process. Lett.

Self Cite

View full text Add to dashboard Cite

show abstract

“…We also formulated the NADSB algorithm to find the optimal sensor locations for sparse arrays with a given aperture and number of sensors. We compared the optimal sparse arrays with standard sparse arrays such as coprime arrays, nested arrays, and semi-coprime arrays [6], [7]. Our specific contributions are:…”

Section: Introductionmentioning

confidence: 99%

Array Shading to Maximize Deflection Coefficient for Broadband Signal Detection With Conventional Beamforming

et al. 2021

Self Cite

View full text Add to dashboard Cite

This paper develops and applies a numerical optimization procedure to compute broadband noise-adaptive weights for delay and sum beamforming that are conditioned to maximize the deflection coefficient at the output of a square law detector for a given set of underwater pressure measurements. The resulting optimal weights mitigate the effects of noise and interferers and maximize signal detection. Comparison of the optimal weights with minimum variance distortionless response weights show that the presented algorithm provides higher attenuation of interferers. We also use the noise-adaptive algorithm to find the optimal sparse array geometry for a given number of sensors and aperture. Comparison of the resulting optimal array with coprime, nested, and semi-coprime arrays shows that the proposed sparse array suppresses interferers more than the other sparse arrays.

show abstract

“…Minimum redundant arrays [3] and nested arrays [4] are examples of fully augmentable sparse arrays. Semi-coprime arrays [5] are examples of partially augmentable sparse arrays.…”

Section: Introductionmentioning

confidence: 99%

Absolute Eigenvalues-Based Covariance Matrix Estimation for a Sparse Array

Adhikari¹

2021

Preprint

Self Cite

View full text Add to dashboard Cite

The ensemble covariance matrix of a wide sense stationary signal spatially sampled by a full linear array is positive semi-definite and Toeplitz. However, the direct augmented covariance matrix of an augmentable sparse array is Toeplitz but not positive semi-definite, resulting in negative eigenvalues that pose inherent challenges in its applications, including model order estimation and source localization. The positive eigenvalues-based covariance matrix for augmentable sparse arrays is robust but the matrix is unobtainable when all noise eigenvalues of the direct augmented matrix are negative, which is a possible case. To address this problem, we propose a robust covariance matrix for augmentable sparse arrays that leverages both positive and negative noise eigenvalues. The proposed covariance matrix estimate can be used in conjunction with subspace based algorithms and adaptive beamformers to yield accurate signal direction estimates.

show abstract

Beamforming with semi-coprime arrays

Cited by 30 publications

References 34 publications

Optimal Sparse Sampling for Detection of a Known Signal in Nonwhite Gaussian Noise

Optimal Sparse Sampling for Detection of a Known Signal in Nonwhite Gaussian Noise

Array Shading to Maximize Deflection Coefficient for Broadband Signal Detection With Conventional Beamforming

Absolute Eigenvalues-Based Covariance Matrix Estimation for a Sparse Array

Contact Info

Product

Resources

About