Academic and research departmentsFaculty of Engineering and Physical Sciences, Centre for Vision, Speech and Signal Processing (CVSSP), Department of Electrical and Electronic Engineering.
arrival (DoA) estimation by joint optimisation of array and
spatial sparsity. Two-step iterative process is proposed. In the
first step, the wideband signal is reshaped and used as the input
to derive the weight coefficients using a sparse array optimisation
method. The weights are then used to scale the observed signal
model for which a compressive sensing based spatial sparsity
optimisation method is used for DoA estimation. Simulations are
provided to demonstrate the performance of the proposed method
for both stationary and moving sources.
spacing in uniform linear arrays (ULAs), will, to a certain
extent, affect the estimation result for source localisation. To
address the problem, sparse arrays such as coprime array and
nested array are considered to achieve less mutual coupling and
more uniform degrees-of-freedom (DoFs) than ULAs. However,
there are holes in coprime arrays leading to a decrease of
uniform DoFs and in a nested array, some sensors may still be
located so closely that the influence of mutual coupling between
sensors remains significant. This paper proposes a new Loosely
Distributed Nested Array (LoDiNA), which is designed in a
three-level nested configuration and the three layers are linked
end-to-end with a longer inter-element separation. It is proved
that LoDiNA can generate a higher number of uniform DoFs
with greater robustness against mutual coupling interference and
simpler configurations, as compared to existing nested arrays.
The feasibility of the proposed LoDiNA structure is demonstrated
for Direction-of-Arrival (DoA) estimation for multiple stationary
sources with noise.