Joint Angle Estimation Research Articles

A Novel Sparse Framework for Angle and Frequency Estimation.

The topic of joint angle and frequency estimation (JAFE) has aroused extensive interests in the past decades. Current estimation algorithms mainly rely on the Nyquist sampling criterion. In order not to cause ambiguity for parameter estimation, the space-time intervals must be smaller than given thresholds, which results in complicated hardware costs and a huge computational burden. This paper aims to reduce the complexity for JAFE, and a novel sparsity-aware framework is proposed. Unlike the current uniform sampling architectures, the incoming narrow-band singles are sampled by a series of space-time coprime samplers. An improved rotational invariance estimator is introduced, which offers closed-form solutions for both angle and frequency estimation. The mathematical treatments indicate that our methodology is inherent in larger spatial/temporal aperture than the uniform sampling architectures; hence, it provides more accurate JAFE compared to alternative approaches relying on uniform sampling. Additionally, it attains nearly the same complexity as the current rotational invariance approach. Numerical results agree with the theoretical advantages of our methodology.

Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network.

Intention recognition based on surface electromyography (sEMG) signals is pivotal in human-machine interaction (HMI), where continuous motion estimation with high accuracy has been the challenge. The convolutional neural network (CNN) possesses excellent feature extraction capability. Still, it is difficult for ordinary CNN to explore the dependencies of time-series data, so most researchers adopt the recurrent neural network or its variants (e.g., LSTM) for motion estimation tasks. This paper proposes a multi-feature temporal convolutional attention-based network (MFTCAN) to recognize joint angles continuously. First, we recruited ten subjects to accomplish the signal acquisition experiments in different motion patterns. Then, we developed a joint training mechanism that integrates MFTCAN with commonly used statistical algorithms, and the integrated architectures were named MFTCAN-KNR, MFTCAN-SVR and MFTCAN-LR. Last, we utilized two performance indicators (RMSE and [Formula: see text]) to evaluate the effect of different methods. Moreover, we further validated the performance of the proposed method on the open dataset (Ninapro DB2). When evaluating on the original dataset, the average RMSE of the estimations obtained by MFTCAN-KNR is 0.14, which is significantly less than the results obtained by LSTM (0.20) and BP (0.21). The average [Formula: see text] of the estimations obtained by MFTCAN-KNR is 0.87, indicating the anti-disturbance ability of the architecture. Moreover, MFTCAN-KNR also achieves high performance when evaluating on the open dataset. The proposed methods can effectively accomplish the task of motion estimation, allowing further implementations in the human-exoskeleton interaction systems.

Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits.

Inertial measurement units (IMUs) offer an attractive way to study human lower-limb kinematics without traditional laboratory constraints. We present an error-state Kalman filter method to estimate 3D joint angles, joint angle ranges of motion, stride length, and step width using data from an array of seven body-worn IMUs. Importantly, this paper contributes a novel joint axis measurement correction that reduces joint angle drift errors without assumptions of strict hinge-like joint behaviors of the hip and knee. We evaluate the method compared to two optical motion capture methods on twenty human subjects performing six different types of walking gait consisting of forward walking (at three speeds), backward walking, and lateral walking (left and right). For all gaits, RMS differences in joint angle estimates generally remain below 5 degrees for all three ankle joint angles and for flexion/extension and abduction/adduction of the hips and knees when compared to estimates from reflective markers on the IMUs. Additionally, mean RMS differences in estimated stride length and step width remain below 0.13 m for all gait types, except stride length during slow walking. This study confirms the method's potential for non-laboratory based gait analysis, motivating further evaluation with IMU-only measurements and pathological gaits.

Continuous estimation of upper limb joint angle from sEMG based on multiple decomposition feature and BiLSTM network

In human-robot interaction systems oriented to rehabilitation training, surface electromyogram (sEMG)-based human motion intention recognition has essential application value. Compared with discrete motion classification, continuous motion estimation is more natural, fast, and accurate. However, due to the non-stability, non-linearity, and strong randomness of sEMG, the effective motion information of sEMG is often lost when extracting the time-domain features of sEMG, and there are also cases where sEMG and joint angle data are not completely synchronized in practical applications, all of which affect the performance of continuous motion estimation. To solve the above problems, this paper firstly proposed a multiple decomposition feature (MDF) representation method based on variational mode decomposition (VMD) and wavelet packet transform (WPT), which can extract more hidden motion information of sEMG from multiple frequency scales; then introduced a bi-directional long short-term memory (BiLSTM) network to establish the regression model between sEMG and joint angle to deal with the incomplete synchronization problem between the input and output data. The experimental results showed that the multiple decomposition feature and the BiLSTM network regression model used in this paper could significantly improve the estimation performance in continuous motion estimation.

VQF: Highly accurate IMU orientation estimation with bias estimation and magnetic disturbance rejection

The miniaturization of MEMS-based inertial measurement units (IMUs) facilitates their widespread use in a growing number of application domains. The fundamental sensor fusion task of orientation estimation is a prerequisite for most further data processing steps in inertial motion tracking, such as position and velocity estimation, joint angle estimation, and 3D visualization. Errors in the estimated orientations severely affect all further processing steps. Recent systematic comparisons of existing algorithms show that out-of-the-box accuracy is often low and that application-specific tuning is required to obtain high accuracy. In the present work, we propose and extensively evaluate a quaternion-based orientation estimation algorithm that is based on a novel approach of filtering the acceleration measurements in an almost-inertial frame and that includes extensions for gyroscope bias estimation and magnetic disturbance rejection, as well as a variant for offline data processing. In contrast to all existing work, we perform an extensive evaluation, using a large collection of publicly available datasets and eight literature methods for comparison. The proposed method consistently outperforms all eight literature methods and achieves an average RMSE of 2.9°, while the errors obtained with literature methods range from 5.3° to 16.7°. This improved accuracy with respect to the state of the art is observed not only in average but also for each of several different motion characteristics, as well as for gyroscope bias estimation. Since the evaluation was performed with one single fixed parametrization across a very diverse dataset collection, we conclude that the proposed method provides unprecedented out-of-the-box performance for a broad range of motions, sensor hardware, and environmental conditions. This gain in orientation estimation accuracy is expected to advance the field of IMU-based motion analysis and provide performance benefits in numerous applications. The provided open-source implementation makes it easy to employ the proposed method.

An ISB-consistent upper limb model based on the Denavit-Hartenberg convention for joint angle estimation during prolonged rehabilitation exercises

An accurate and robust joint kinematics description is a crucial prerequisite for the development of tele- rehabilitation applications. Miniaturized wearable sensors (IMUs), along with the use of efficient sensor fusion algorithms, represent a suitable solution. However, orientation drift can affect real-time joint kinematics over prolonged acquisition. In this work, we proposed an optimization kinematics framework which exploits a Denavit-Hartenberg (DH) model of the upper limbs, compliant with the guidelines of the International Society of Biomechanics. The accuracy of the estimated 3D shoulder and elbow kinematics was evaluated using data recorded by two IMUs attached to a robotic arm during a 20- minute planar exercise and compared with the errors obtained using a model-free approach. The upper limb was modelled as a three-segment chain including trunk, upper arm (UA), and the forearm (FA) (Fig. 1). Shoulder and elbow joints were modelled with three (φ1, φ2, φ3) and two degrees of freedom (φ4, φ6), respectively. The carrying angle (φ5) was introduced as a fixed subject-specific parameter to describe the physiological abduction of the FA with respect to the UA. The joint angles were computed in an optimization process which minimizes at each time step the difference between the orientation predicted using the DH model and the corresponding orientation estimated by a sensor fusion algorithm without the magnetometer [1]. In addition, joint angle constraints were set based on the maximum angular velocity, the physiological angular limits, and the specific range of movement when known a-priori . To validate the methods, two IMUs (Xsens – MTw) were attached to the UA and FA of a robotic arm (Kinova – Jaco2) programmed to mimic a shoulder flexion-extension (φ1, φ2, φ3), an elbow flexion-extension (φ4), and a forearm prono-supination (φ6), simultaneously, for 20 minutes (~150 cycles) [3]. The φ5 was equal to zero for the robot. IMU and reference data were collected at 100 Hz. The gyroscope offset was computed during a preliminary acquisition and then subtracted from the measurements. The accuracy was evaluated in terms of root mean square difference (RMSD) between the model-based optimized and robot reference joint angles. Furthermore, the RMSD corresponding to the Euler inversion of the FA-to-UA relative orientation (model-free) was also computed. The RMSD (deg) for the joint angles obtained with the optimization (model-free) process amounted to 1 (10.2), 0.4 (0.2), 0.9 (1.4), 3.0 (3.1), 0 (8.2), and 1 (6) for φ1, φ2, φ3, φ4, φ5, φ6, respectively. The proposed methods allowed to reduce joint angles errors of almost a factor 10 over long period compared to model-free approach (1.1 vs 8.9 deg on average). These results if further confirmed on human experiments can contribute to the design of tele-rehabilitation applications relying on the accurate upper limb joint kinematics estimates. Funded by Sardegna Ricerche (POR FESR 2014/2020).

Computer-vision based rapid entire body analysis (REBA) estimation

Although much attention has been paid to the safety risk of construction sites and ergonomic risk assessment of workers, the automation of ergonomic risk assessment has not been significantly developed. This article presents a non-intrusive, automated ergonomic risk assessment approach based on computer vision, machine learning, and Rapid Entire Body Assessment (REBA). The method is called Computer-Vison Based Rapid Entire Body Analysis Estimation (CVRE). This approach is expected to realize automated monitoring and early-stage warning of ergonomic risks by automating the procedure of calculating REBA scores for construction site workers. This method consists of machine learning-based key joints and joint angles estimation of human bodies and computer-vision-based automated risk estimation. With the extensive development of machine learning and computer vision, researchers have been paying attention to assessing ergonomic risks with machine learning techniques. The proposed method has been further validated using the experimental data obtained by a motion capture system.

3-D Parameterized Multipath Channel Estimation for Massive MIMO–OFDM Systems

In this article, we propose a three-dimensional (3-D) joint angle and delay estimation approach for massive multiple-input and multiple-output orthogonal frequency division multiplexing systems on the basis of the channel state information of a single base station, which is equipped with multiple antennas. By modeling the propagation channel by a finite number of multipath rays, we present a joint azimuth-angle, elevation-angle, and path delay estimation algorithm, in which a new version of subcarrier smoothing is developed for full-rank correlation matrix construction. The new algorithm offers closed-form parameter estimates and is applicable to arbitrary array configurations. The Cramér–Rao bound for the considered problem is derived as well. Simulation results with a typical outdoor millimeter-wave setup are finally presented to verify the efficacy of the proposed approach.

Joint angle and frequency estimation for linear array: an extended DOA-matrix method

An approach for joint direction of arrival (DOA) angle and frequency estimation for a linear array is investigated in this paper. Specifically, we make the utmost of the autocorrelation and cross-correlation information to propose an extended DOA-matrix (EDOAM) method. Subsequently, we obtain the auto-paired angle and frequency estimates by the eigenvalues and the corresponding eigenvectors of the novel DOA matrix. Furthermore, the proposed method surpasses the DOA-matrix method which partly ignores the autocorrelation and cross-correlation information. Finally, the proposed method works well for both uniform and non-uniform linear arrays. The simulation consequences indicate the superiority of our proposed approach.

Research progress on sparse array MIMO radar parameter estimation

Making full use of the virtual aperture expansion property with the sparse array multi-input multi-output (MIMO) radar, we can achieve positive contributions for parameter estimation. We focus on the design of the sparse array MIMO radar and the optimization of conventional algorithms. According to the joint subspace-based method and compressive sensing theory, we study the analysis and design of the sparse arrays, direction of arrival estimation, and joint angle estimation for improved performance and low complexity, aiming to establish systematic parameter estimation theory for the sparse array MIMO radar. Combining the aspects of array optimization design, mixed target parameter estimation, nonideal factor suppression, and distributed platform construction, the future development trend of the sparse array MIMO radar is discussed.

Deep Learning Networks for View-independent Knee and Elbow Joint Angle Estimation.

Vision-based human joint angle estimation is essential for remote and continuous health monitoring. Most vision-based angle estimation methods use the locations of human joints extracted using optical motion cameras, depth cameras, or human pose estimation models. This study aimed to propose a reliable and straightforward approach with deep learning networks for knee and elbow flexion/extension angle estimation from the RGB video. Fifteen healthy participants performed four daily activities in this study. The experiments were conducted with four different deep learning networks, and the networks took nine subsequent frames as input while output was knee and elbow joint angles extracted from an optical motion capture system for each frame. The BiLSTM network-based joint angles estimator can estimate both joint angles with a correlation of 0.955 for knee and 0.917 for elbow joints regardless of the camera view angles.

Accurate Continuous Prediction of 14 Degrees of Freedom of the Hand from Myoelectrical Signals through Convolutive Deep Learning.

Natural control of assistive devices requires continuous positional encoding and decoding of the user's volition. Human movement is encoded by recruitment and rate coding of spinal motor units. Surface electromyography provides some information on the neural code of movement and is usually decoded into finger joint angles. However, the current approaches to mapping the electrical signal into joint angles are unsatisfactory. There are no methods that allow precise estimation of joint angles during natural hand movements within the large numbers of degrees of freedom of the hand. We propose a framework to train a neural network from digital cameras and high-density surface electromyography from the extrinsic (forearm and wrist) hand muscles. Furthermore, we show that our 3D convolutional neural network optimally predicted 14 functional flexion/extension joints of the hand. We found in our experiments (4 subjects; mean age of 26±2.12 years) that our model can predict individual sinusoidal finger movement at different speeds (0.5 and 1.5 Hz), as well as two and three finger pinching, and hand opening and closing, covering 14 degrees of freedom of the hand. Our deep learning method shows a mean absolute error of 2.78±0.28 degrees with a mean correlation coefficient between predicted and expected joint angles of 0.94, 95% confidence interval (CI) [0.81, 0.98] with simulated real-time inference times lower than 30 milliseconds. These results demonstrate that our approach is capable of predicting the user's volition similar to digital cameras through a non-invasive wearable neural interface. Clinical relevance- This method establishes a viable interface that can be used for both immersive virtual reality medical simulations environments and assistive devices such as exoskeleton and prosthetics.

A cable driven robotic palpation system with contact force sensing based on cable tension observation.

Prostate Cancer screening based on manual palpation is subjective. Robotic palpation systems can objectively acquire stiffness conditions of the prostate. A 2DoF cable driven robotic system for prostate palpation is proposed. An indirect method to estimate the contact force based on cable tension observation is presented. Kinematic models and a joint angle estimation method to determine the tip position of the probe are derived. Positioning accuracy was verified using an optical marker tracking system and by displacement measurement, respectively. The contact force estimation method was validated on silicone phantom samples. A good consistence between the estimated and measured contact force was observed. The contact force was correlated with the elastic modulus of each silicone phantom. There was also a good agreement between the theoretical and the measured tip position. In the proposed palpation system, the indirect contact force estimation method is viable and holds potential for the stiffness assessment of the prostate. The tip position vital for palpation can be determined through estimated joint angles.

A CNN-Attention Network for Continuous Estimation of Finger Kinematics from Surface Electromyography

Dexterous control of robotic hand driven by human motor intent has drawn a lot of attention in both industrial and rehabilitation scenarios. Providing simultaneous and proportional control has become a prevailing solution recently. Towards improving the finger kinematics estimation precision and reducing its computational cost, a convolution model with attention mechanism (CNN-Attention) was proposed in this study. For comparison purpose, two previously used deep learning models, the long short-term memory (LSTM) and the Sparse Pseudo-input Gaussian processes (SPGP) were also included. By using surface electromyography (sEMG) and kinematic signals corresponding to six hand grasp movements, the estimation performance of each of the three models was evaluated with three measures, Pearson Correlation Coefficient (CC), Root Mean Square Error (RMSE), and coefficient of determination (R2) between real and estimated joint angles. The results demonstrated that the proposed CNN-Attention model outperformed LSTM and SPGP significantly ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> -value<0.05), with an average value of CC, RMSE, and R2, 0.87, 9.65 degrees, and 0.73, respectively. Also, the CNN-Attention model is more stable and versatile over various subjects and joint angles in comparison to other models. Additionally, the computational time to build a CNN-Attention was obviously shorter than that to train a LSTM model (43.00 ± 4.25 min vs. 73.40 ± 5.81 min). These findings suggest that the CNN-Attention would be a promising model for continuous estimation of hand movements in the human-machine interaction and cooperation.

A Fast PARAFAC Algorithm for Parameter Estimation in Monostatic FDA-MIMO Radar

This paper studies the joint range and angle estimation of monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) radar and proposes a joint estimation algorithm. First, the transmit direction matrix is converted into real values by unitary transformation, and the Vandermonde-like matrix structure is used to construct an augmented output that doubles the aperture of the receive array. Then the augmented output is combined into a third-order tensor. Next, the factor matrices are initially estimated. Finally, the direction matrices are estimated utilizing parallel factor (PARAFAC) decomposition, and the range and angle are calculated by employing least square fitting. As contrasted with the classic PARAFAC method, the proposed method can estimate more targets and provide better estimation performance, and requires less computational complexity. The availability and excellence of the proposed method are reflected by numerical simulations and complexity analysis.

Optimal estimation of complex aerial movements using dynamic optimisation

ABSTRACT When estimating full-body motion from experimental data, inverse kinematics followed by inverse dynamics does not guarantee dynamical consistency of the resulting motion, especially in movements where the trajectory depends heavily on the initial state, such as in free-fall. Our objective was to estimate dynamically consistent joint kinematics and kinetics of complex aerial movements. A 42-degrees-of-freedom model with 95 markers was personalised for five elite trampoline athletes performing various backward and forward twisting somersaults. Using dynamic optimisation, our algorithm estimated joint angles, velocities and torques by tracking the recorded marker positions. Kinematics, kinetics, angular and linear momenta, and marker tracking difference were compared to results of an Extended Kalman Filter (EKF) followed by inverse dynamics. Angular momentum and horizontal linear momentum were conserved throughout the estimated motion, as per free-fall dynamics. Marker tracking difference went from 17 ± 4 mm for the EKF to 36 ± 11 mm with dynamic optimisation tracking the experimental markers, and to 49 ± 9 mm with dynamic optimisation tracking EKF joint angles. Joint angles from the dynamic optimisations were similar to those of the EKF, and joint torques were smoother. This approach satisfies the dynamics of complex aerial rigid-body movements while remaining close to the experimental 3D marker dataset.

A Muscle Synergy-Driven ANFIS Approach to Predict Continuous Knee Joint Movement

Continuous motion prediction plays a significant role in realizing seamless control of robotic exoskeletons and orthoses. Explicitly modeling the relationship between coordinated muscle activations from surface electromyography (sEMG) and human limb movements provides a new path of sEMG-based human–machine interface. Instead of the numeric features from individual channels, we propose a muscle synergy-driven adaptive network-based fuzzy inference system (ANFIS) approach to predict continuous knee joint movements, in which muscle synergy reflects the motor control information to coordinate muscle activations for performing movements. Four human subjects participated in the experiment while walking at five types of speed: 2.0 km/h, 2.5 km/h, 3.0 km/h, 3.5 km/h, and 4.0 km/h. The study finds that the acquired muscle synergies associate the muscle activations with human joint movements in a low-dimensional space and have been further utilized for predicting knee joint angles. The proposed approach outperformed commonly used numeric features from individual sEMG channels with an average correlation coefficient of 0.92 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \pm $</tex-math></inline-formula> 0.05. Results suggest that the correlation between muscle activations and knee joint movements is captured by the muscle synergy-driven ANFIS model and can be utilized for the estimation of continuous joint angles.

Continuous and simultaneous estimation of lower limb multi-joint angles from sEMG signals based on stacked convolutional and LSTM models

The smooth and natural interaction between human and lower limb exoskeleton is important. However, one of the challenges is that obtaining the joint rotation angles in time and accurately is difficult. In this paper, we propose the stacked convolutional and long–short term memory networks (Conv-LSTM) to estimate the hip, knee, and ankle joint angles from sEMG signals in locomotion modes including walk, run, stair descent, stair ascent, stand-to-sit, sit-to-stand, and jump. The joint angles are calculated from the kinematic models using the Euler angle signals measured by IMUs. The sEMG and joint angles are segmented according to the gait cycles measured by footswitch signals. Time–frequency analysis of sEMG signals is carried out using continuous wavelet transform. The Conv-LSTM model can extract the spatiotemporal information from the input to establish the mapping from sEMG sequences to multi-joint angle sequences. The evaluation effects of coefficient of determination (R2), root mean squared error, and Dynamic Time Warping on estimation performance are compared. The time domain (TD) features of sEMG perform better on joint angle estimation than the frequency domain and time–frequency domain features (p<0.05). The Conv-LSTM model with TD features as input outperforms the BP and state-of-the-art machine learning algorithms (kernel ridge, random forest, and support vector regression) on multi-joint angle estimation (R2: 0.9334, 0.9110, 0.9236, 0.9238, 0.8999, 0.9430, 0.9351, p<0.05). The estimation results are simulated in V-REP for exoskeleton control.

Reduction of secondary lobes in joint angle and delay estimation in angle of arrival localization to detect MAC address spoofing in wireless networks

in this paper, we solve the problem of secondary lobes that are due to noise that comes from constructive and destructive multipath interference that are resulted in received signal strength (RSS) variation over time. This is to develop a very efficient localization algorithm that uses a unique fingerprint angle of arrivals (AOAs), in a specified range, with associated time delays (TDs), in the surrounded sparsity design promoting multipath parameter (i.e:RSS). We solve this problem to detect physical identity spoofing of nodes in radio wireless networks, and localize adversaries and jammers of wireless networks. All radio waves are vulnerable to many types of attacks due to the ability to capture them and sniff or eavesdropping on them in the open space. Physical identity spoofing is used to launch many types of attacks against wireless networks like Denial of Service (DOS), Man-In-The-Middle and Session Hijacking and eavesdropping. Eavesdropping is a human-based social engineering attack. Active adversaries are able to jam and eavesdrop simultaneously, while passive adversaries can only eavesdrop on passed signals. In TCP/IP protocol for example, Media Access Card (MAC) Address is transferred in 802.11 frames. Detection process was carried out by analyzing electromagnetic radio waves that are used to transfer data, in the form of radio wave signals that are formed by the modulation process which mixes the electromagnetic wave, with another one of different frequency or amplitude to produce the signal with a specified pattern of frequency and amplitude. We depended on the angle of arrival of vectors and time delay across scattered areas in the surrounded space to solve the problem of co-location in detection and localization of jammers. We used Maximum Likelihood (ML) angle of arrival determination because ML approaches, known to their higher accuracy and enhanced resolution capabilities. And we assessed their computational complexity that was considered as the major drawback for designers to their implementation in practice. Our solution was tested on a jammer that changed the signal strength of received signal at the receiver at an angle of arrival 30 degree. And we used scatterers density to determine the angle of arrival of the sender. The simulation has observed that the power of the received signal has changed from the range of angles 20 to 40 degrees. We used scatterers because they describe the density of the signal power, and also enhance the signal to noise ratio, that resulted from the multipath fading of the signal strength. And also overcoming the problem of secondary lobes that are due to signal propagation, while determining the angle of arrival of a signal sender. So, we developed a new passive technique to detect MAC address spoofing based on angle of arrival localization. And assessed the computation complexity of the localization technique through depending on a range angle to estimate the angle of arrival of the adversary within it. And we reduced number of secondary lobes, and their peaks, in the importance function, while determining the angle of arrival, and so increasing the accuracy of angle of arrival measurement. We compared our work to other techniques and find that our technique is better than these techniques.

Joint range and angle estimation of low-elevation target with bistatic meter-wave FDA-MIMO radar

Frequency diverse array multiple-input-multiple-output (FDA-MIMO) radar, which has gained much attention in recent years, can be used for the estimation of multidimensional parameters due to the range-dependent property of the beampattern. The localization of low-elevation targets is a key problem to be solved in meter-wave radar. Combining the advantages of the bistatic system, this paper investigates the joint direction of departure (DOD), direction of arrival (DOA), and range multiparameter estimation problem of the bistatic meter-wave FDA-MIMO radar. Firstly, the signal model of the bistatic meter-wave FDA-MIMO radar is derived. Then the signal model is deformed and then applied to the generalized multiple signal classification (MUSIC) algorithm which does not need to consider the signal coherence. A better performance steering vector matching (SVM) reduced dimension MUSIC algorithm is proposed by exploring the signal model characteristics. Then the computational complexity of the two algorithms and the derivation of Cramer Rao bound (CRB) for estimating low-elevation target parameters is given. The innovation of this paper is to provide a joint localization model for bistatic meter-wave FDA-MIMO, which extends the study of bistatic FDA-MIMO radar to the field of coherent signals. The simulation results verify the effectiveness of two localization algorithms without decoherence processing.