Zishu He

Synthesis of Sparse Planar Arrays Using Modified Real Genetic Algorithm

In array design, the positions of sparse array elements is an important concern for optimal performance in terms of its ability to achieve minimum peak sidelobe level (SLL). This paper proposes a modified real genetic algorithm (MGA) based on resetting of chromosome for the element position optimization of sparse planar arrays with rectangular boundary. And here the multiple optimization constraints include the number of elements, the aperture and the minimum element spacing. By simplifying the space between the elements from the actual distance to Chebychev distance, the MGA searches a smaller solution space by means of indirect description of individual, and it can avoid infeasible solution during the optimization process by two novel genetic operators. Finally, the simulation results confirming the great efficiency and the robustness of the proposed method are shown in this paper

An algorithm for precisely estimating loop-delay in digital predistortion system

In digital predistortion (DPD) design, loop-delay estimation for synchronization is an important work. In this paper, an algorithm for precisely estimating loop-delay in DPD system is proposed. The whole algorithm consists of integer loop-delay estimation and fractional loop-delay estimation. Amplitude-difference correlation function is used to estimate the integer loop-delay estimation. The fractional loop-delay estimation gets the estimate through recursive least squares (RLS) algorithm based on interpolation filter structure. Finally, a series of simulations prove the validity of the algorithm and the effect on the DPD system.

Design of 2-Dimension Sparse Arrays using an Improved Genetic Algorithm

For the element position synthesis of 2-dimension sparse rectangular arrays with the design constraints of the number of elements, the aperture and the minimum element spacing, an improved genetic algorithm (IGA) based on chromosome element resetting is presented in this paper. Compared with the synthesis method of thinned arrays, this new approach could exploit more degree of freedom of elements to control the characters of the sparse arrays. When the aperture, the number of elements and the minimum element spacing are fixed identical, the new approach can make the sparse plane array produce lower peak sidelobe level (PSLL). The simulated results show that the fitness function value of thinned plane arrays with 108 elements mentioned by Haupt can reduce 5.626 dB when the minimum element spacing of half wavelength was introduced into the optimization. And as well, the great efficiency and the robustness of IGA are shown in this paper

Adaptive direct position determination of emitters based on time differences of arrival

In passive localization systems based on the time difference of arrival, conventional methods usually take two steps for localization. In the first step the time differences of arrival (TDOA) are measured, which are used for localization in the second step. This two-step approach is suboptimal since each measurement of time difference is performed independently, although all measurements correspond to a common emitter position. In this paper, an Adaptive Direct Position Determination (ADPD) approach is proposed, where the position is determined directly from the observed signals in a single step. Monte Carlo simulations validate that the accuracy of proposed ADPD method outperforms the two-step method under low signal-to-noise ratios (SNRs), while both methods are close to the Cramer-Rao lower bound (CRLB) under high SNRs.

Design of Linear Sparse Antenna Arrays for Performance Improvement

An effective method for optimizing the performance of a fixed current distribution, uniformly spaced antenna array has been to adjust its element positions to provide performance improvement. Additionally, with a goal to reduce the peak sidelobe level (PSLL) of the array pattern, this method based on a modified real genetic algorithms (MGA) provides practical advantages such as taking the design constraints of the number of elements, the aperture and the minimum element spacing into account. The new method advanced in this paper reduces the size of the searching area of GA by means of indirect description of individual and avoids infeasible solution during the optimization process by designing the new genetic operators. The elementary steps of MGA are presented in this paper, and the simulated results confirming the great efficiency and the robustness of this algorithm are also provided here

Approximate maximum likelihood time differences estimation in the presence of frequency and phase consistence errors

In passive location systems using time differences of arrival (TDOA) measurements, in order to digital processing, the received radio frequency (RF) signals are usually down-converted to baseband. In down-conversions, oscillators are used to mix the signals-of-interest to baseband. Frequency and phase consistence error between two oscillators degrade the performance of TDOA estimation between two sensors. Considering the oscillator consistence errors, the modified baseband data model of time delay estimation is presented. Next, the approximate maximum likelihood (AML) TDOA estimator based modified model is proposed to reduce the TDOA estimation error. Furthermore, two simplified versions of AML estimator are also developed, which include the two-dimensional (2D) search and one-dimensional (1D) search AML methods. Simulation results confirm the performance of these estimators.

A variable step size subband affine projection algorithm with dynamic selection of subband filters

In this paper, we present a novel variable step size subband affine projection (VSS-SAP) algorithm which dynamically selects subband filters in order to reduce the computational complexity and improve the convergence performance. It is called as dynamic selection variable step size subband affine projection algorithm (DS-VSS-SAP). The optimum selection of subband filters is derived by the largest decrease between the successive mean square deviations (MSDs) at every iteration. Simulation results show that the proposed algorithm has a lower misadjustment compared with the existing SAP algorithms. In addition, it retains a lower overall computational complexity.

A Frequency Domain LMS Algorithm with Dynamic Selection of Frequency Bins

Frequency-domain (FD) adaptive filter algorithms are able to achieve a low computational complexity by using the overlap-and-save implementation means compared to time-domain (TD) ones. In this article, we propose a new FD least-mean-square (FD-LMS) algorithm which dynamically selects frequency bins in order to reduce the computational complexity and maintain the convergence performance of the conventional FD-LMS. The optimal selection of frequency bins is derived by the largest decrease between the successive FD mean square deviations (MSDs) at every data block. Simulation results show that the proposed algorithm provides a low steady-state normalized MSD (NMSD) and similar convergence rate compared to the conventional FD-LMS algorithm. In addition, it gains a low computational complexity.

Synthesis of thinned conical arrays using simulated annealing algorithm

the peak side lobe level (PSLL) is an important factor of conformal antenna arrays. This paper thins the basic conformal arrays-conical arrays using simulated annealing algorithm (SA). The simulation results indicate that SA can minimize the PSLL effectively, and attain the satisfactory results.

Distributed Airborne MIMO Radar Detection in Compound-Gaussian Clutter without Training Data

This paper deals with the detection problem of a moving target for the distributed airborne multi-input multi-output radar embedded in the compound-Gaussian and non-homogeneous clutters, without assuming that training data are available. A novel detector combing the Bayesian approach and the generalized likelihood ratio test is proposed, where we model the covariance of clutter as an inverse Wishart distribution with an unknown average clutter covariance matrix (ACCM). More precisely, we regard the unknown ACCM as a structured matrix with the Hadamard product form involving two independent parts, composing of the covariance matrix taper (CMT) and the Doppler spectrum component. Further, the proposed detector exploits a nonlinear processing in an iteration fashion to reconstruct sparse signals with the aim of estimating the unknown spectrum of clutters. As to the CMT component, we resort to generalized CMT model to improve estimation accuracy. Numerical simulations are provided to assess the capability of the proposed detector in different complicated scenarios.