11 accepted papers
One-bit data with non-zero quantization thresholds suffice to recover the covariance of the original Gaussian data. State-of-the-art recovery methods require computing the one-bit correlation from the estimated covariance. However, this step exhibits trade-offs between the computational complexity a…
Sparse arrays with fourth-order cumulant processing can identify up to $\mathcal{O}\left( {{N^4}} \right)$ source directions with only N physical sensors. To achieve this property, the fourth-order difference co-array is preferable to own a contiguous segment with no holes. However, existing sparse…
Linear sparse arrays, where the sensors are placed with non-uniform spacing, identify more source direction-of-arrivals (DOAs) than sensors. In contrast, uniform linear arrays (ULAs) can only find fewer source DOAs than sensors. To resolve these DOAs in practice, the sources have to be enumerated fi…
One-bit quantization has received attention due to its simplicity, low cost, and ability to recover the autocorrelation of the unquantized data. In the past, the autocorrelation estimation from one-bit data can be done in two separate stages. One is the power estimation by using one-bit quantizers o…
One-bit quantization has attracted attention in massive MIMO, radar, and array processing, due to its simplicity, low cost, and capability of parameter estimation. Specifically, the shape of the covariance of the unquantized data can be estimated from the arcsine law and onebit data, if the unquanti…
In array processing, minimum redundancy arrays (MRA) can identify up to O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) uncorrelated sources (the O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/x…
Sparse arrays can identify O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) uncorrelated sources using N physical sensors. This property is because the difference coarray, defined as the differences between sensor locations, has uniform lin…
One-bit quantization has become an important topic in massive MIMO systems, as it offers low cost and low complexity in the implementation. Techniques to achieve high performance in spite of the coarse quantizers have recently been advanced. In the context of array processing and direction-of-arriva…
In array processing, mutual coupling between sensors has an adverse effect on the estimation of parameters (e.g., DOA). Sparse arrays, such as nested arrays, coprime arrays, and minimum redundancy arrays (MRAs), have reduced mutual coupling compared to uniform linear arrays (ULAs). With N denoting t…
Coprime DFT filter banks (coprime DFTFB) achieve the effect of an MN-DFTFB by using two DFTFBs of size only M and N, where M and N are coprime integers. However, coprime DFTFBs need to be designed properly, to avoid unwanted bumps in stopbands or unsatisfactory total spectrum coverage, quantified by…
This paper extends the use of coprime arrays and samplers for the case of moving sources. Space-time adaptive processing (STAP) plays an important role in estimating direction-of-arrivals (DOAs) and radial velocities of emitting sources. However, the detection performance is fundamentally limited by…