Angle Estimation Algorithm Research Articles

차량 롤 각 추정 알고리즘 및 롤 저감력 분배 제어 전략

Abstract : The ROM (roll over mitigation) system is a next-generation suspension system that can improve vehicle- driving stability and ride comfort. Currently, mass-produced safety systems, such as ESC (electronic stability control) and ECS (electronic control suspension), enable measurements of longitudinal and lateral acceleration as well as yaw rate through inertial sensor clusters, but they lack direct measurements of the roll angle . Therefore, in this paper, a roll angle estimation algorithm from ESC system sensors and tire normal force has been proposed. Furthermore, this study presents a method for roll over mitigation force distribution between the front and rear of a ROM system. Performance and reliability of the roll angle estimation and roll over mitigation force distribution were investigated through simulations. The simulation results showed that the proposed control algorithm and strategy are reliable during vehicle rollovers. Key words : Roll over mitigation system( 롤 오버 저감 시스템), Active roll stabilizer system( 능동형 롤 스테빌라이져 시스템), Roll angle estimation(롤 각도 추정), Roll stability(롤 안정성)

An Enhanced Channel Estimation Method for Millimeter Wave Systems With Massive Antenna Arrays

In this letter, we focus on the efficient channel estimation problem for millimeter wave (MMW) systems with massive antenna arrays and RF constraints, aiming at achieving a fast and high resolution angle-of-arrival/angle-of-departure (AoA/AoD) estimation. We first propose a presentation of antenna array with virtual elements (AAVE) by appending additional virtual antenna elements into the original antenna array. On the basis of the AAVE structure, we explore the channel sparsity in the angular domain and develop an efficient angle estimation algorithm by using compressive sensing theories. We then proposed a training design and prove that the sensing matrix in the proposed training can guarantee the accurate detection of angles with a high probability. Both the analytical and simulation results show that, without changing the physical antenna arrays, the proposed approach can achieve not only a lower overhead, but also a significantly higher resolution in angles estimation, compared to the existing algorithms.

Practical rotation angle measurement method by monocular vision

A practical vision-based method is proposed to measure single axis rotation angles. Compared with existing angle measurement methods, the proposed method is more practical because of the simple equipment required and an easy installation. Furthermore, unlike most existing methods, the proposed method does not require the rotation axis information at all. The information is calibrated by two-view geometry of the single axis motion. Then, on the basis of the calibration results, an angle estimation algorithm with point matching is proposed. Experiments on synthetic and real images show that the proposed method is robust with respect to image noises and occlusion. A measurement accuracy of less than 0.1° is obtained in real experiments only using a provided camera and a normal printed checkboard. Moreover, a single axis rotation angle measurement MATLAB toolbox is developed, which is available online (http://quanquan.buaa.edu.cn/Anglemeasurementtoolbox.html).

Estimating facial angles using Radon transform

Analysis of human facial images has become increasingly important due to its numerous applications. In this regard, extracting facial parameters is vital and various studies have been done in this field. Using these methods, different facial parameters such as the location of eyebrows, eye length, and nose angles are extracted. In this article, a robust and automatic method is introduced for determining facial angles from profile view images using the Radon transform. The Radon transform is a type of linear integration along a specific direction and angles play an important role in its performance. The proposed algorithm not only has good precision, but also efficient performance. The precision of this method in angle measurements is 98.51% compared with manual measurement. Compared to various other angle estimation algorithms, the proposed method has higher efficiency.

The modeling and position sensor-less estimation technique for a nine-phase interior permanent magnet machine using high frequency injections

This paper presents a sensorless method to estimate the rotor angle and speed of a nine-phase interior permanent-magnet (IPM) machine. The major contributions of this work include the injection of high-frequency voltage signals into a nontorque-producing circuit of the machine in which the rotor angle is estimated without generating any high-frequency torque ripple. The inductances of the third sequence of the nine-phase machine are used to modulate the high-frequency injected voltage signals from which the third-sequence currents are extracted. Using a heterodyning method and the Luenberger observer, the rotor position is estimated. The requirement of only one low-pass filter in the angle estimation algorithm is another novelty. This rotor estimation method can be used for a wide speed range including the zero speed. The proposed method has been tested using the coupled full-order model of the nine-phase IPM machine including all the higher order magnetomotive force (MMF) harmonics. Simulation and experimental results for the motor operating under open-loop constant volts per hertz are presented to validate the estimation technique.

Improved Azimuth/Elevation Angle Estimation Algorithm for Three-Parallel Uniform Linear Arrays

Two-dimensional (2-D) direction-of-arrival (DOA) estimation method using three-parallel uniform linear arrays (ULAs) is proposed in this letter. The 2-D DOA estimation problem is addressed by making full use of elements of the three-parallel ULAs. Furthermore, the proposed algorithm has better angle estimation performance in practical mobile elevation angles between 70 ${^ \circ} $ and 90 ${^ \circ} $ and can automatically pair the estimated azimuth and elevation angles with lower complexity. Simulation results demonstrate the effectiveness of the proposed algorithm.

Two-Dimensional Direction of Arrival (DOA) Estimation for Rectangular Array via Compressive Sensing Trilinear Model

We investigate the topic of two-dimensional direction of arrival (2D-DOA) estimation for rectangular array. This paper links angle estimation problem to compressive sensing trilinear model and derives a compressive sensing trilinear model-based angle estimation algorithm which can obtain the paired 2D-DOA estimation. The proposed algorithm not only requires no spectral peak searching but also has better angle estimation performance than estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Furthermore, the proposed algorithm has close angle estimation performance to trilinear decomposition. The proposed algorithm can be regarded as a combination of trilinear model and compressive sensing theory, and it brings much lower computational complexity and much smaller demand for storage capacity. Numerical simulations present the effectiveness of our approach.

Reduced-Dimensional PARAFAC-Based Algorithm for Joint Angle and Doppler Frequency Estimation in Monostatic MIMO Radar

In this article, the problem of joint direction of arrival (DOA) and Doppler frequency estimation using parallel factor (PARAFAC) analysis in the monostatic multiple-input multiple-output radar is investigated and a reduced-dimensional PARAFAC (RD-PARAFAC) algorithm is proposed. In order to overcome the shortcoming of the heavy computational load in conventional PARAFAC algorithm, we firstly utilize a RD transformation by employing the property of uniform linear arrays, which can remove the redundant entries of steering matrix. By means of the reduced-dimensional transformation, the proposed algorithm can reduce the computational complexity, save memory capacity significantly and has very close joint DOA and Doppler frequency estimation performance when compared with conventional PARAFAC algorithm. Meanwhile, it outperforms the estimation of signal parameters via rotational invariance technique and the propagator method. Furthermore, our algorithm needs no spectral peak searching or pair matching. The complexity analysis and the Cramer-Rao Bound of the DOA and frequency estimation is derived. Simulations are provided to verify its effectiveness and superiority.

Angle Measurement of Robotic Fish Based on Kalman Filter

In water polo competition of robotic fish, due to the influence of factors like ripple disturbance, target point drift and image acquisition delay, the angle between robotic fish and water polo that competition platform return has great error. To improve the shortage, this article put forward an optimal angle estimation algorithm based on Kalman filter. Let the angle go through Kalman filter to eliminate error interference in process and measurement. So that output angle approach truth value. Results of experiment prove that this method can effectively get the better angle between robotic fish and water polo.

Study on the Compressed Matrices in Compressed Sensing Trilinear Model

In this paper, we make study on the compressed matrices in the compressed sensing trilinear model-based angle estimation algorithm, whose complexity is lower than conventional trilinear decomposition-based method, due to the use of compressed matrices. And we take the problem of angle estimation for bistatic multiple-input multiple-output (MIMO) radar as an example. Simulation results can provide reference for the choice of compressed matrices.

The Bistatic MIMO Imaging Algorithm Based on Waveform Diversity

Due to the large distance between transmitting array and receiving array in bistatic MIMO, The bistatic MIMO is unable to use the waveform diversity technology base on the monostatic MIMO to improve the resolution of the detection. And bistatic MIMO angle estimation algorithm based on the spatial spectral estimation is unable to get coherent gain from beamforming. In this paper, position calculating of the bistatic sonar is used to get the relationship among DOD DOA and propagation distance. The array manifold of virtual array is constructed in each scan position in 2-d imaging. The improved waveform diversity technology is used in the imaging of bistatic MIMO to improve the resolution of the detection.

Robust antenna array calibration and accurate angle estimation based on least trimmed squares

A robust antenna array calibration and single target angle estimation algorithm is proposed. The proposed algorithm is based on the least trimmed squares algorithm and operates in two steps. First, the conventional least squares algorithm is used to estimate the intermediate phases (or angle) and the residual values at each element are calculated. In the second step, it excludes large residual elements and uses only the smallest of them, which prevents large errors during the angle estimation. The least trimmed-based phase difference approximation algorithm is simple to implement and is a practical way of mitigating errors at the antenna elements that are due to hardware and imperfect calibration. The results demonstrate that our proposed algorithm is robust and outperforms other algorithms in three scenarios.

A New Central Angle Estimation Method of Bistatic MIMO Radar

Based on the theory that the steering vectors of coherently distributed targets for bistatic MIMO radar possess a property called Hadamard product rotational invariance (HPRI), a novel central angle estimation algorithm is derived. The proposed algorithm has the advantage of no searching and easily pair the estimated angles. Furthermore, it can deal with different coherently distributed targets with different angular distribution functions or unknown angular distribution functions. Simulation results show the proposed method is effective.

A central profile-based 3D face pose estimation

In this study, we develop a central profile-based 3D face pose estimation algorithm. The central profile is a unique curve on a 3D face surface that starts from forehead center, goes down through nose ridge, nose tip, mouth center, and ends at a chin tip. The points on the central profile are co-planar and belong to a symmetry plane that separates human face into two identical parts. The central profile is protrusive and has a certain length. Most importantly, the normal vectors of the central profile points are parallel to the symmetry plane. Based on the properties of the central profile, Hough transform is employed to determine the symmetry plane by invoking a voting procedure. An objective function is introduced in the parameter space to quantify the vote importance for face profile points and map the central profile to an accumulator cell with the maximal value. Subsequently, a nose model matching algorithm is used to detect nose tip on the central profile. A pitch angle estimation algorithm is also proposed. The pose estimation experiments completed for a synthetic 3D face model and the FRGC v2.0 3D database demonstrate the effectiveness of the proposed pose estimation algorithm. The obtained central profile detection rate is 99.9%, and the nose tip detection rate has reached 98.16% with error not larger than 10mm.

Angle Estimation and Self-Calibration Using ISM and ESPRIT for Bistatic MIMO Radar with Gain and Phase Uncertainties

Most high-resolution angle estimation algorithms suffer from sensitivity to gain and phase uncertainties. In this letter, a novel ESPRIT-based algorithm, which uses the instrumental sensors method (ISM), is proposed for bistatic MIMO radar with gain and phase uncertainties. With the help of a few well-calibrated transmitting and receiving instrumental sensors, the proposed algorithm is able to achieve favorable and unambiguous angle estimation and accurate self-calibration without the knowledge of gain and phase uncertainties. Simulation results demonstrate the validity of the proposed method.

Compressed Sensing Trilinear Model-Based Angle Estimation for Bistatic MIMO Radar

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.

Combining sum-difference and auxiliary beams for adaptive monopulse in jamming

Only in the presence of sidelobe jamming (SLJ), can the conventional adaptive monopulse technique null the jamming effectively and maintain the monopulse angle estimation accuracy simultaneously. While mainlobe jamming (MLJ) exists, the mainlobe of adaptive pattern will subject to serious distortion, which results in a failure of detecting and tracking targets by monopulse technique. Therefore, a monopulse angle estimation algorithm based on combining sum-difference beam and auxiliary beam is presented. This algorithm utilizes both high gain difference beams and high gain auxiliary beams for cancelling the mainlobe jammer and multiple sidelobe jammers (SLJs) while keeping an adaptive monopulse ratio. Theoretical analysis and simulation results indicate that the serious invalidation of monopulse technique in MLJ and SLJs scenarios is resolved well, which improves the monopulse angle accuracy greatly. Furthermore, the proposed algorithm is of simple implementation and low computational complexity.

A PARALIND Decomposition-Based Coherent Two-Dimensional Direction of Arrival Estimation Algorithm for Acoustic Vector-Sensor Arrays

In this paper, we combine the acoustic vector-sensor array parameter estimation problem with the parallel profiles with linear dependencies (PARALIND) model, which was originally applied to biology and chemistry. Exploiting the PARALIND decomposition approach, we propose a blind coherent two-dimensional direction of arrival (2D-DOA) estimation algorithm for arbitrarily spaced acoustic vector-sensor arrays subject to unknown locations. The proposed algorithm works well to achieve automatically paired azimuth and elevation angles for coherent and incoherent angle estimation of acoustic vector-sensor arrays, as well as the paired correlated matrix of the sources. Our algorithm, in contrast with conventional coherent angle estimation algorithms such as the forward backward spatial smoothing (FBSS) estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, not only has much better angle estimation performance, even for closely-spaced sources, but is also available for arbitrary arrays. Simulation results verify the effectiveness of our algorithm.

BLIND JOINT ANGLE, FREQUENCY AND POLARIZATION ESTIMATION FOR POLARIZATION SENSITIVE ARRAY USING QUADRILINEAR DECOMPOSITION

This paper links the polarization-sensitive-array parameter estimation problem to the quadrilinear model. Exploiting this link, it derives a blind joint angle, frequency and polarization estimation algorithm. The simulation results reveal that the proposed algorithm has better angle, frequency and polarization estimation performance than ESPRIT. This algorithm relies on a fundamental result of the uniqueness of low-rank four-way data decomposition. Furthermore, the proposed algorithm does not require pairing among multiple parameters. Simulation results illustrate performance of this algorithm.

Joint Angle and Array Gain-Phase Errors Estimation Using PM-like Algorithm for Bistatic MIMO Radar

This paper discusses the problem of the direction of departure (DOD) and the direction of arrival (DOA) estimation for multi-input multi-output (MIMO) radar with array gain-phase errors. In this paper, we propose a propagator method (PM)-like algorithm for joint angle and array gain-phase errors estimation in MIMO radar. The proposed method not only yields automatically paired estimates of the angles and gain-phase errors but also has much better gain-phase errors estimation performance than the estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm; this has higher computational cost than the proposed algorithm. Furthermore, the proposed algorithm has angle estimation performance very close to ESPRIT-like algorithm. We also derive the Cramer–Rao bound (CRB) for MIMO radar with array gain-phase errors. Simulation results present the usefulness of our approach.