scholarly journals Estimating DOA and PPS parameters of signal received by ULA in heavy noise environment

2020 ◽  
Vol 71 (3) ◽  
pp. 175-184
Author(s):  
Igor Djurović ◽  
Marko Simeunović ◽  
Vladimir V. Lukin
Keyword(s):  
Linear Array ◽  
Parameters Estimation ◽  
Algorithm Performance ◽  
Signal Parameters ◽  
Normalization Strategy ◽  
Polynomial Phase ◽  
Multicomponent Signals ◽  
Phase Signal ◽  
Heavy Tailed ◽  
Quasi Maximum Likelihood

AbstractEstimation of the direction-of-arrival (DOA) and parameters of polynomial phase signal (PPS) impinging on the uniform linear array (ULA) of sensors in heavy-tailed noise environments is considered in this paper. To estimate signal parameters, a recently proposed quasi maximum-likelihood (QML) estimator is adopted. The proposed algorithm consists of two successive steps: (1) noise influence mitigation by using the proposed normalization strategy and (2) signal parameters estimation using the DOA-QML approach. The algorithm performance is evaluated for both monocomponent and multicomponent signals.

Parameter Estimation of Polynomial Phase Signal by Unscented Kalman Filtering

2014 ◽  
Vol 644-650 ◽  
pp. 4253-4256
Author(s):  
Wan Ge Li ◽  
Jin Feng Hu ◽  
Hui Ai ◽  
Zhi Rong Lin ◽  
Ya Xuan Zhang
Keyword(s):  
Parameter Estimation ◽  
Kalman Filtering ◽  
State Space Model ◽  
Radar Signal ◽  
Signal Parameters ◽  
Polynomial Phase ◽  
Polynomial Phase Signals ◽  
Phase Signal ◽  
Stable Estimation ◽  
Core Issues

The parameter estimation of the Polynomial Phase Signals (PPS) is one of the core issues. In this paper, UKF-based algorithm is proposed to estimate the parameter of PPS embedded in Gaussian noise. The algorithm constructs an adequate state-space model to represent the PPS and the model can also be implied in real radar signal. Unscented Kalman filtering is applied to estimate the signal parameters. The method achieves the lower SNR threshold, the faster convergence speed, the higher accuracy and more stable estimation performance compared with the existing methods. Simulation also verifies the efficiency of the proposed method.

scholarly journals DOA Estimation for Coprime Linear Array Based on MI-ESPRIT and Lookup Table

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3043 ◽  
Author(s):  
Weike Zhang ◽  
Xi Chen ◽  
Kaibo Cui ◽  
Tao Xie ◽  
Naichang Yuan
Keyword(s):  
Linear Array ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Spatial Spectrum ◽  
Angle Measurement ◽  
Lookup Table ◽  
Signal Parameters ◽  
Estimation Precision ◽  
Simulation Results ◽  
The Relationship

In order to improve the angle measurement performance of a coprime linear array, this paper proposes a novel direction-of-arrival (DOA) estimation algorithm for a coprime linear array based on the multiple invariance estimation of signal parameters via rotational invariance techniques (MI-ESPRIT) and a lookup table method. The proposed algorithm does not require a spatial spectrum search and uses a lookup table to solve ambiguity, which reduces the computational complexity. To fully use the subarray elements, the DOA estimation precision is higher compared with existing algorithms. Moreover, the algorithm avoids the matching error when multiple signals exist by using the relationship between the signal subspace of two subarrays. Simulation results verify the effectiveness of the proposed algorithm.

scholarly journals A Novel Multicomponent PSO Algorithm Applied in FDE–AJTF Decomposition

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 51
Author(s):  
Lei Yu ◽  
Guochao Lao ◽  
Chunsheng Li ◽  
Yang Sun ◽  
Yingying Li
Keyword(s):  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Optimal Parameters ◽  
Time Frequency ◽  
Modified Maximum Likelihood ◽  
Maneuvering Targets ◽  
Polynomial Phase ◽  
Phase Signal ◽  
Evolution Algorithm

The echo of maneuvering targets can be expressed as a multicomponent polynomial phase signal (mc-PPS), which should be processed by time frequency analysis methods, while, as a modified maximum likelihood (ML) method, the frequency domain extraction-based adaptive joint time frequency (FDE–AJTF) decomposition method is an effective tool. However, the key procedure in the FDE–AJTF method is searching for the optimal parameters in the solution space, which is essentially a multidimensional optimization problem with different extremal solutions. To solve the problem, a novel multicomponent particle swarm optimization (mc-PSO) algorithm is presented and applied in the FDE–AJTF decomposition with the new characteristic that can extract several components simultaneously based on the feature of the standard PSO, in which the population is divided into three groups and the neighborhood of the best particle in the optimal group is set as the forbidden area for the suboptimal group, and then two different independent components can be obtained and extracted in one extraction. To analyze its performance, three simulation tests are carried out and compared with a standard PSO, genetic algorithm, and differential evolution algorithm. According to the tests, it is verified that the mc-PSO has the best performance in that the convergence, accuracy, and stability are improved, while its searching times and computation are reduced.

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