An Improved ESPRIT-Like Algorithm for Coherent Signals DOA Estimation

Zhang, Wei; Han, Yujun; Jin, Minghe; Li, Xue-sheng

doi:10.1109/lcomm.2019.2953851

Cited by 63 publications

(47 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By finding the transform matrix between the two sub-matrices of the singular matrix, all signal directions can be determined simultaneously following an additional Eigen value decomposition. Enhanced schemes such as [10] employs an additional time-frequency data model exhibiting multi-invariance to improve the estimation accuracy and scheme [11] can tackle the coherent signal detection problem. These two categories of estimation are computationally intensive because of various matrix decompositions, which requires iterative computations to obtain the results.…”

Section: Related Work Of Doa Estimationmentioning

confidence: 99%

A Low Complexity, High Throughput DoA Estimation Chip Design for Adaptive Beamforming

Chen

Ma²,

Hwang

et al. 2020

Electronics

View full text Add to dashboard Cite

Direction of Arrival (DoA) estimation is essential to adaptive beamforming widely used in many radar and wireless communication systems. Although many estimation algorithms have been investigated, most of them focus on the performance enhancement aspect but overlook the computing complexity or the hardware implementation issues. In this paper, a low-complexity yet effective DoA estimation algorithm and the corresponding hardware accelerator chip design are presented. The proposed algorithm features a combination of signal sub-space projection and parallel matching pursuit techniques, i.e., applying signal projection first before performing matching pursuit from a codebook. This measure helps minimize the interference from noise sub-space and makes the matching process free of extra orthogonalization computations. The computing complexity can thus be reduced significantly. In addition, estimations of all signal sources can be performed in parallel without going through a successive update process. To facilitate an efficient hardware implementation, the computing scheme of the estimation algorithm is also optimized. The most critical part of the algorithm, i.e., calculating the projection matrix, is largely simplified and neatly accomplished by using QR decomposition. In addition, the proposed scheme supports parallel matches of all signal sources from a beamforming codebook to improve the processing throughput. The algorithm complexity analysis shows that the proposed scheme outperforms other well-known estimation algorithms significantly under various system configurations. The performance simulation results further reveal that, subject to a beamforming codebook with a 5° angular resolution, the Root Mean Square (RMS) error of angle estimations is only 0.76° when Signal to Noise Ratio (SNR) = 20 dB. The estimation accuracy outpaces other matching pursuit based approaches and is close to that of the classic Estimation of Signal Parameters Via Rotational Invariance Techniques (ESPRIT) scheme but requires only one fifth of its computing complexity. In developing the hardware accelerator design, pipelined Coordinate Rotation Digital Computer (CORDIC) processors consisting of simple adders and shifters are employed to implement the basic trigonometric operations needed in QR decomposition. A systolic array architecture is developed as the computing kernel for QR decomposition. Other computing modules are also realized using various linear systolic arrays and chained together seamlessly to maximize the computing throughput. A Taiwan Semiconductor Manufacturing Company (TSMC) 40 nm CMOS process was chosen as the implementation technology. The gate count of the chip design is 454.4k, featuring a core size of 0.76 mm 2 , and can operate up to 333 MHz. This suggests that one DoA estimation, with up to three signal sources, can be performed every 2.38 μs.

show abstract

Section: Related Work Of Doa Estimationmentioning

confidence: 99%

A Low Complexity, High Throughput DoA Estimation Chip Design for Adaptive Beamforming

Chen

Ma²,

Hwang

et al. 2020

Electronics

View full text Add to dashboard Cite

show abstract

“…For the DOA estimation algorithms under the condition of small snapshot number, researchers have also explored other methods [ 30 , 31 ]. In reference [ 30 ], a Toeplitz matrix, using which to obtain the signal subspace and noise subspace, is constructed using any row of the received signal’s covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Yan

Chen

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).

show abstract

“…Direction-of-arrival (DOA) estimation has been extensively investigated due to its wide applications in radar, sonar, wireless communication, and navigation [1][2][3][4]; e.g., DOA estimation is of significance for source location in MIMO radar [5], and the issue of DOA estimation for noncircular sources is valuable in practical communications [6]. Subspace-based methods are preferred among numerous DOA estimation methods due to their high superresolution and ease of implementation [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Obtain the output of the uniform linear antenna array x(t) from equation(1) and compute the sample covariance matrix R as in equation (3). (ii) Step 2.…”

mentioning

confidence: 99%

DOA Estimation for Sources with Large Power Differences

Fang

Jin

Liu

et al. 2021

International Journal of Antennas and Propagation

Self Cite

View full text Add to dashboard Cite

Sources with large power differences are very common, especially in complex electromagnetic environments. Classical DOA estimation methods suffer from performance degradation in terms of resolution when dealing with sources that have large power differences. In this paper, we propose an improved DOA algorithm to increase the resolution performance in resolving such sources. The proposed method takes advantage of diagonal loading and demonstrates that the invariant property of noise subspace still holds after diagonal loading is performed. We also find that the Cramer–Rao bound of the weak source can be affected by the power of the strong source, and this has not been noted before. The Cramer–Rao bound of the weak source deteriorates as the power of the strong source increases. Numerical results indicate that the improved algorithm increases the probability of resolution while maintaining the estimation accuracy and computational complexity.

show abstract

An Improved ESPRIT-Like Algorithm for Coherent Signals DOA Estimation

Cited by 63 publications

References 11 publications

A Low Complexity, High Throughput DoA Estimation Chip Design for Adaptive Beamforming

A Low Complexity, High Throughput DoA Estimation Chip Design for Adaptive Beamforming

A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

DOA Estimation for Sources with Large Power Differences

Contact Info

Product

Resources

About