54 research outputs found
Space-Time Transmit-Receive Design for Colocated MIMO Radar
This chapter deals with the design of multiple input multiple-output (MIMO) radar space-time transmit code (STTC) and space-time receive filter (STRF) to enhance moving targets detection in the presence of signal-dependent interferences, where we assume that some knowledge of target and clutter statistics are available for MIMO radar system according to a cognitive paradigm by using a site-specific (possible dynamic) environment database. Thus, an iterative sequential optimization algorithm with ensuring the convergence is proposed to maximize the signal to interference plus noise ratio (SINR) under the similarity and constant modulus constraints on the probing waveform. In particular, each iteration of the proposed algorithm requires to solve the hidden convex problems. The computational complexity is linear with the number of iterations and polynomial with the sizes of the STTW and the STRF. Finally, the gain and the computation time of the proposed algorithm also compared with the available methods are evaluated
A New Restriction on Low-Redundancy Restricted Array and Its Good Solutions
In array signal processing, a fundamental problem is to design a sensor array
with low-redundancy and reduced mutual coupling, which are the main features to
improve the performance of direction-of-arrival (DOA) estimation.
For a -sensor array with aperture , it is called low-redundancy (LR) if
the ratio is approaching the Leech's bound for ; and the mutual coupling is often
reduced by decreasing the numbers of sensor pairs with the first three smallest
inter-spacings, denoted as with . Many works have
been done to construct large LRAs, whose spacing structures all coincide with a
common pattern with the
restriction . Here denote the spacing between adjacent
sensors, and is the largest one. The objective of this paper is to find
some new arrays with lower redundancy ratio or lower mutual coupling compared
with known arrays. In order to do this, we give a new restriction for to be , and obtain 2 classes of -type arrays, 2 classes
of -type arrays, and 1 class of -type arrays for any .
Here the -Type means that . Notably, compared with
known arrays with the same type, one of our new -type array and the new
-type array all achieves the lowest mutual coupling, and their uDOFs are
at most 4 less for any ; compared with SNA and MISC arrays, the new
-type array has a significant reduction in both redundancy ratio and
mutual coupling.
We should emphasize that the new -type array in this paper is the first
class of arrays achieving and for any
Optimization and Noise Analysis of the Quantum Algorithm for Solving One-Dimensional Poisson Equation
Solving differential equations is one of the most promising applications of
quantum computing. Recently we proposed an efficient quantum algorithm for
solving one-dimensional Poisson equation avoiding the need to perform quantum
arithmetic or Hamiltonian simulation. In this letter, we further develop this
algorithm to make it closer to the real application on the noisy
intermediate-scale quantum (NISQ) devices. To this end, we first develop a new
way of performing the sine transformation, and based on it the algorithm is
optimized by reducing the depth of the circuit from n2 to n. Then, we analyze
the effect of common noise existing in the real quantum devices on our
algorithm using the IBM Qiskit toolkit. We find that the phase damping noise
has little effect on our algorithm, while the bit flip noise has the greatest
impact. In addition, threshold errors of the quantum gates are obtained to make
the fidelity of the circuit output being greater than 90%. The results of noise
analysis will provide a good guidance for the subsequent work of error
mitigation and error correction for our algorithm. The noise-analysis method
developed in this work can be used for other algorithms to be executed on the
NISQ devices.Comment: 20 pages, 9 figure
Quantum-inspired Complex Convolutional Neural Networks
Quantum-inspired neural network is one of the interesting researches at the
junction of the two fields of quantum computing and deep learning. Several
models of quantum-inspired neurons with real parameters have been proposed,
which are mainly used for three-layer feedforward neural networks. In this
work, we improve the quantum-inspired neurons by exploiting the complex-valued
weights which have richer representational capacity and better non-linearity.
We then extend the method of implementing the quantum-inspired neurons to the
convolutional operations, and naturally draw the models of quantum-inspired
convolutional neural networks (QICNNs) capable of processing high-dimensional
data. Five specific structures of QICNNs are discussed which are different in
the way of implementing the convolutional and fully connected layers. The
performance of classification accuracy of the five QICNNs are tested on the
MNIST and CIFAR-10 datasets. The results show that the QICNNs can perform
better in classification accuracy on MNIST dataset than the classical CNN. More
learning tasks that our QICNN can outperform the classical counterparts will be
found.Comment: 12pages, 6 figure
Black-Box Quantum State Preparation with Inverse Coefficients
Black-box quantum state preparation is a fundamental building block for many
higher-level quantum algorithms, which is applied to transduce the data from
computational basis into amplitude. Here we present a new algorithm for
performing black-box state preparation with inverse coefficients based on the
technique of inequality test. This algorithm can be used as a subroutine to
perform the controlled rotation stage of the Harrow-Hassidim-Lloyd (HHL)
algorithm and the associated matrix inversion algorithms with exceedingly low
cost. Furthermore, we extend this approach to address the general black-box
state preparation problem where the transduced coefficient is a general
non-linear function. The present algorithm greatly relieves the need to do
arithmetic and the error is only resulted from the truncated error of binary
string. It is expected that our algorithm will find wide usage both in the NISQ
and fault-tolerant quantum algorithms.Comment: 11 pages, 3 figure
Robust Design of Constant Modulus Sequence and Receiver Filter in the Presence of Signal-dependent Clutter
In this paper, we focus on the detection of a moving point-like target embedded in uncertain signal-dependent clutter and develop robust transmit-code and receive-filter designs in slow-time. First, based on the Worst-case Signal-to-Interference-plus-Noise Ratio (W-SINR) when the second-order clutter statistics are uncertain, we establish a high-dimensional transmit-receive optimization model that considers the constant modulus constraint with non-convexity. Next, we propose an Iterative Sequential Optimization (ISO) algorithm. At each iteration, it converts a high-dimensional optimization into multiple one-dimensional fractional programming problems that can be efficiently solved using Dinkelbach’s method. Finally, we use numerical examples to confirm that the ISO can resist the uncertain knowledge of signal-dependent clutter, which enables the radar system to adapt to complicated environments. Moreover, compared to Semi-Definite Relaxation (SDR)-related and randomization methods, the proposed algorithm is superior with respect to both optimized W-SINR and computational time
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