Joint DOD and DOA Estimation for Coherently Distributed Sources in Bistatic MIMO Radar Based on Joint Diagonalization

Xie, Qingguo; Pan, Xiaoyi; Chen, Jiyuan; Xiao, Shunping

doi:10.1109/access.2019.2933907

Cited by 11 publications

(10 citation statements)

References 34 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…x N (L) and x R , x N (p) and x M (u), and x N (q) and x M (v) lie as k 1 , k 2 and k 3 . Other parameters are defined in the same way as section (1). The following derivation is obtained:…”

Section: B Emdmentioning

confidence: 99%

See 1 more Smart Citation

An Effective Source Number Enumeration Approach Based on SEMD

Rum

Ibrahim

et al. 2022

IEEE Access

View full text Add to dashboard Cite

In signal processing, empirical mode decomposition (EMD) first decomposes the received single-channel signal into several intrinsic mode functions (IMFs) and a residual, and then uses machine learning methods for source number enumeration. EMD, however, has an end effect that can undermine the accuracy of source number enumeration. To address this issue, this paper proposed a new EMD method named Supplementary Empirical Mode Decomposition (SEMD), which improved the accuracy by extending the signal length. The proposed method can be better applied to the modal parameter identification of nonstationary and nonlinear data in the engineering field. This method first identifies two candidate extreme points, which are the closest to the function value of the first extreme point near the endpoint. Then, on one side of the candidate point, it finds a waveform similar to that at the endpoint. Finally, the maximum and minimum points at each end of the signal will be added to extend the length of the signal. The added extreme points are candidate extreme points in similar waveforms. For the improved source number enumeration method based on SEMD, the instantaneous phase is obtained first by SEMD and Hilbert transform (HT). Then, the instantaneous phase feature is extracted to obtain a high-dimensional eigenvalue vector. Finally, the back propagation (BP) neural network is used to predict the number of sources. Experiment shows that SEMD can effectively restrain the end effect, and the source number enumeration algorithm based on SEMD has a higher correct detection probability than others. 17 18 if the number of signal sources is wrong. Therefore, the 48 estimation of the number of signal sources is the primary task 49 of DOA estimation. 50 Early methods of estimating the number of signal sources 51 were based on the method of hypothesis testing, but these 52 methods may be affected by subjective behavior during man-53 ual settings. In order to avoid this problem, later researchers 54 proposed estimation methods based on information theoretic 55 criteria (ITC). Akaike information criterion (AIC) proposed 56 by Wax and Kailath [11] is a representative of these algo-57 rithms. The AIC method has good estimation performance 58 under a low signal-to-noise ratio (SNR), but the method is 59 not consistent in estimation and performs poorly at high SNR 60 and a large number of snapshots. Minimum description length 61 (MDL) proposed by Wax and Ziskind [12] achieved consis-62 tent estimation, but it performs pooly at low SNR and a small 63 number of snapshots. Later, researchers improved the source 64 number estimation method based on ITC Guo et al. [13] pro-65 posed a new source number estimation method based on 66 MDL, which uses the modified covariance matrix and its 67 eigenvectors to replace the eigenvalues used to estimate the 68 source number. A new decision variable with better anti-noise 69 ability is obtained by a series of transformations on the snap-70 shot vector and the feature vector. Experiment shows that 71 this metho...

show abstract

Section: B Emdmentioning

confidence: 99%

“…Research on array signal processing in the direction of arrival (DOA) estimation is vital and has been widely used in radar [1], sonar [2], communications [3], and other fields [4], [5]. The algorithms based on subspace are an integral part of DOA estimation algorithms.…”

Section: Introductionmentioning

confidence: 99%

An Effective Source Number Enumeration Approach Based on SEMD

Rum

Ibrahim

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Without loss of generality, we assume the location parameter µ = 0. Thus, the characteristic function of the SαS can be expressed as ϕ (t) = exp (−γ |t| α ), and its pdf could be given by f (s) = (1/2π ) e −γ |t| α dt (32) Let g = 2/π 3 +∞ −∞ e −γ |t| α dt; we can then obtain g < +∞. The derivation can be referred to in [24].…”

Section: The Error Function and Its Derivativementioning

confidence: 99%

“…For example, without snapshots accumulation, peak searching, and correlation process, a 2D DOA estimation method for CD sources is proposed in a high real-time situation using only one snapshot for the real-time situation [31]. An efficient estimator has been proposed to deal with the joint direction of departure (DOD) and DOA estimation for CD sources in bistatic MIMO radar, which constructs the rotational invariance relationship for central DODs and DOAs and pairs the estimated central DODs and DOAs automatically by using joint diagonalization [32]. Ref.…”

Section: Introductionmentioning

confidence: 99%

A Novel DOA Estimation for Distributed Sources in an Impulsive Noise Environment

et al. 2020

View full text Add to dashboard Cite

Direction of arrival (DOA) estimation has emerged as a promising technology for many military and civilian applications, such as radio astronomy and wireless communications. However, it remains very challenging to estimate DOA for distributed sources within impulsive noise environments. As is widely known, the utilization of the subspace technology in array signal processing can enable high-resolution DOA estimation. In many instances, most of these algorithms assume that the noise is Gaussian. Considering the impulsive noise environments, the derivative of the error function is derived as the iteratively weighted factor in the objective function of low-rank decomposition. Two iterative algorithms are proposed to compute the signal subspace to implement DOA estimation for coherently distributed sources. To further improve the DOA estimation accuracy and increase the number of estimated sources, the virtual data samples are reconstructed from the extended co-prime array. Simulation results confirm that the two proposed impulsive noise suppression algorithms provide better performance than the existing algorithms. The virtual data samples that are reconstructed from the extended co-prime array can be utilized as the effective inputs for subspace-like algorithms.

show abstract

“…(2) DOA measurement is possible even for silent target by using camera or other sensing devices; (3) multi-path effect on DOA measurements is supposed to be negligible in future networks, due to the very densely located sensing devices, applying mmWave techniques. DOA measuring techniques are well studied such as in [17]- [19]. However, discussion of practical techniques is out of the scope of this paper.…”

Section: Introductionmentioning

confidence: 99%

Integrated Factor Graph Algorithm for DOA-Based Geolocation and Tracking

2020

View full text Add to dashboard Cite

This paper proposes a new position tracking algorithm by integrating extended Kalman filter (EKF) and direction-of-arrival (DOA)-based geolocation into one factor graph (FG) framework. A distributed sensor network is assumed for detecting an anonymous target, where the process and observation equations in the state space model (SSM) are unknown. Importantly, the predicted state information can be utilized not only for filtering, but also for enhancing the observation process. To be specific, by taking the prediction into account as the a priori, a new FG scheme is proposed for GEolocation, denoted by FG-GE. The benefits are twofold , compared with the conventional geolocation scheme which does not rely on the a priori information. First of all, significant performance improvement can be observed, in terms of the root mean square error (RMSE), when severe sensing errors are suddenly encountered. Furthermore, the proposed FG-GE can achieve dramatic reduction of computational complexity. In addition, this paper also proposes the use of a predicted Cramer-Rao lower bound (P-CRLB) to dynamically estimate the observation error variance, which demonstrates more robust tracking performance than that with only fixed average variance approximation. INDEX TERMS Factor graph (FG), direction of arrival (DOA), extended Kalman filter (EKF), geolocation, tracking, complexity analysis, CRLB.

show abstract

Joint DOD and DOA Estimation for Coherently Distributed Sources in Bistatic MIMO Radar Based on Joint Diagonalization

Cited by 11 publications

References 34 publications

An Effective Source Number Enumeration Approach Based on SEMD

An Effective Source Number Enumeration Approach Based on SEMD

A Novel DOA Estimation for Distributed Sources in an Impulsive Noise Environment

Integrated Factor Graph Algorithm for DOA-Based Geolocation and Tracking

Contact Info

Product

Resources

About