<title>Aspect angle estimation in FLIR images: a model-based vision approach</title>

In this paper, we address the transmit angle and receive angle estimation problem for a bistatic multiple-input multiple-output (MIMO) radar. This paper links MIMO radar angle estimation problem to the compressed sensing trilinear model. Exploiting this link, it derives a compressed sensing trilinear model-based angle estimation algorithm, which can obtain automatically paired two-dimensional angle estimation. The proposed algorithm requires no spectral peak searching or pair matching, and it has better angle estimation performance than conventional algorithms including estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Simulation results illustrate performance of the algorithm.

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Compressed PARAFAC Model-based Two-Dimensional Angle Estimation for Acoustic Vector-Sensor Arrays

Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017) ◽

10.2991/mecs-17.2017.141 ◽

2017 ◽

Author(s):

Le Xu ◽

You Sun ◽

Na Shi ◽

Xiaofei Zhang

Keyword(s):

Sensor Arrays ◽

Two Dimensional ◽

Acoustic Vector Sensor ◽

Angle Estimation ◽

Model Based ◽

Parafac Model ◽

Vector Sensor

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A fusion methodology for sideslip angle estimation on the basis of kinematics-based and model-based approaches

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407019892156 ◽

2019 ◽

Vol 234 (7) ◽

pp. 1930-1943

Author(s):

Xiaoyu Li ◽

Nan Xu ◽

Qin Li ◽

Konghui Guo ◽

Jianfeng Zhou

Keyword(s):

Magnitude Estimation ◽

Front Wheel ◽

Lateral Acceleration ◽

Area Network ◽

Sideslip Angle ◽

Small Magnitude ◽

Angle Estimation ◽

Model Based ◽

Road Friction ◽

Basic Approaches

This article introduces a reliable fusion methodology for vehicle sideslip angle estimation, which only needs the Controller Area Network–Bus signals of production vehicles and has good robustness to vehicle parameters, tire information, and road friction coefficient. The fusion methodology consists of two basic approaches: the kinematic-based approach and the model-based approach. The former is constructed into the extended Kalman filter for transient stage and large magnitude estimation, while the latter is designed to be an adaptive scheme for steady-state and small magnitude estimation. On this basis, combining the advantages of the two methods, a weight allocation strategy is proposed based on the front wheel steering angle and transient characteristics of lateral acceleration and yaw rate. The validity of the method is verified by simulation and experiment, and it is proved that the method can be effectively used for the sideslip angle estimation.

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An Improved Trilinear Model-Based Angle Estimation Method for Co-Prime Planar Arrays

Sensors ◽

10.3390/s18124180 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4180

Author(s):

Chenxi Guo ◽

Xinhong Hao ◽

Ping Li

Keyword(s):

Estimation Method ◽

Sparse Recovery ◽

Estimation Methods ◽

Reconstruction Method ◽

Angle Estimation ◽

Trilinear Decomposition ◽

Model Based ◽

Planar Arrays ◽

Trilinear Model ◽

Virtual Array

Angle estimation methods in two-dimensional co-prime planar arrays have been discussed mainly based on peak searching and sparse recovery. Peak searching methods suffer from heavy computational complexity and sparse recovery methods face some problems in selecting the regularization parameters. In this paper, we propose an improved trilinear model-based method for angle estimation for co-prime planar arrays in the view of trilinear decomposition, namely parallel factor analysis. Due to the principle of trilinear decomposition, our method does not require peak searching and can conduct auto-pairing easily, which can reduce the computational loads and avoid parameter selection problems. Furthermore, we exploit the virtual array concept of the whole co-prime planar array through the cross-correlation matrix obtained from the received signal data and present a matrix reconstruction method using the Khatri–Rao product to tackle the matrix rank deficiency problem in the virtual array condition. The simulation results show that our proposed method can not only achieve high estimation accuracy with low complexity compared to other similar approaches, but also utilize limited sensor number to implement the angle estimation tasks.

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