scholarly journals Minimum Array Elements for Resolution of Several Direction of Arrival Estimation Methods in Various Noise-Level Environments

2018 ◽  
Vol 2 ◽  
pp. 87-94
Author(s):  
Ismail El Ouargui ◽  
Said Safi ◽  
Miloud Frikel
Keyword(s):  
Gaussian Noise ◽  
Noise Level ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Minimum Number ◽  
Informative Signals

The resolution of a Direction of Arrival (DOA) estimation algorithm is determined based on its capability to resolve two closely spaced signals. In this paper, authors present and discuss the minimum number of array elements needed for the resolution of nearby sources in several DOA estimation methods. In the real world, the informative signals are corrupted by Additive White Gaussian Noise (AWGN). Thus, a higher signal-to-noise ratio (SNR) offers a better resolution. Therefore, we show the performance of each method by applying the algorithms in different noise level environments.

scholarly journals Direction of Arrival Estimation in the presence of Scatterer in noisy environment

2017 ◽  
Vol 6 (3) ◽  
pp. 33
Author(s):  
T. Aslam ◽  
I. Ahmed ◽  
M. I. Aslam ◽  
S. M. U. Ali ◽  
T. Malik
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Estimation Error ◽  
Additive White Gaussian Noise ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Noisy Environment ◽  
Signal To Noise ◽  
Incoming Wave

We present an algorithm to estimate direction of arrival (DOA) of an incoming wave received at an array antenna in the scenario where the incoming wave is contaminated by the additive white Gaussian noise and scattered by arbitrary shaped 3D scatterer(s). We present different simulation examples to show the validity of the proposed method. It is observed that the proposed algorithm is capable of closely estimating the DOA of an incoming wave irrespective of the shape of the scatterer provided the decision is made over multiple iterations. Moreover, presence of noise affects the estimate especially in the case of low signal-to-noise ratio (SNR) that gives a relatively large estimation error. However, for larger SNR the DOA estimation is primarily dependent on the scatterer only.

scholarly journals Multi-Frequency Based Direction-of-Arrival Estimation for 2q-Level Nested Radar & Sonar Arrays

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3385 ◽  
Author(s):  
Hao Zhou ◽  
Guoping Hu ◽  
Junpeng Shi ◽  
Ziang Feng
Keyword(s):  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Research Area ◽  
Frequency Separation ◽  
Frequency Method ◽  
Virtual Sensors ◽  
Order Difference ◽  
Sonar Systems ◽  
Minimum Number

Direction finding is a hot research area in radar and sonar systems. In the case of q ≥ 2, the 2qth-order cumulant based direction of arrival (DOA) estimation algorithm for the 2q-level nested array can achieve high resolution performance. A virtual 2qth-order difference co-array, which contains O(N2q) virtual sensors in the form of a uniform linear array (ULA), is yielded and the Gaussian noise is eliminated. However, some virtual elements are separated by the holes among the 2qth-order difference co-array and cannot be fully used. Even though the application of the multi-frequency method for minimum frequency separation (MFMFS) can fill the holes with low computation complexity, it requires that the number of frequencies must increase with the number of holes. In addition, the signal spectra have to be proportional for all frequencies, which is hard to satisfy when the number of holes is large. Aiming at this, we further propose a multi-frequency method for a minimum number of frequencies (MFMNF) and discuss the best frequency choice under two specific situations. Simulation results verify that, compared with the MFMFS method, the proposed MFMNF method can use only one frequency to fill all the holes while achieving a longer virtual array and the DOA estimation performance is, therefore, improved.

scholarly journals Blind Direction-of-Arrival Estimation with Uniform Circular Array in Presence of Mutual Coupling

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Song Liu ◽  
Lisheng Yang ◽  
Shizhong Yang ◽  
Qingping Jiang ◽  
Haowei Wu
Keyword(s):  
Mutual Coupling ◽  
Coupling Parameter ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Circular Array ◽  
Signal Parameters ◽  
Simulation Results ◽  
Parameter Estimation Methods

A blind direction-of-arrival (DOA) estimation algorithm based on the estimation of signal parameters via rotational invariance techniques (ESPRIT) is proposed for a uniform circular array (UCA) when strong electromagnetic mutual coupling is present. First, an updated UCA model with mutual coupling in a discrete Fourier transform (DFT) beam space is deduced, and the new manifold matrix is equal to the product of a centrosymmetric diagonal matrix and a Vandermonde-structure matrix. Then we carry out blind DOA estimation through a modified ESPRIT method, thus avoiding the need for spatial angular searching. In addition, two mutual coupling parameter estimation methods are presented after the DOAs have been estimated. Simulation results show that the new algorithm is reliable and effective especially for closely spaced signals.

scholarly journals An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4403
Author(s):  
Ji Woong Paik ◽  
Joon-Ho Lee ◽  
Wooyoung Hong
Keyword(s):  
Covariance Matrix ◽  
Phased Array ◽  
Null Space ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Improve Performance ◽  
Space Projection

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

scholarly journals 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

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2191
Author(s):  
Huichao Yan ◽  
Ting Chen ◽  
Peng Wang ◽  
Linmei Zhang ◽  
Rong Cheng ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Spatial Clustering ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Cluster Center ◽  
Direction Of Arrival Estimation ◽  
Vector Hydrophone ◽  
Regularization Parameters ◽  
Selection Of

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).

scholarly journals Low Complexity Sparse Beamspace DOA Estimation Via Single Measurement Vectors for Uniform Circular Array

2021 ◽  
Author(s):  
Di Zhao ◽  
Weijie Tan ◽  
Zhongliang Deng ◽  
Gang Li
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Methods ◽  
Circular Array ◽  
Single Measurement ◽  
Signal Model ◽  
Convex Optimization Problem

Abstract In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.

scholarly journals DOA Estimation for UCAs Based on Virtual Subarray Beamforming Technique

2018 ◽  
Vol 232 ◽  
pp. 01012
Author(s):  
Bo Xu ◽  
Zhigang Huang
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Signal To Noise ◽  
Circular Arrays ◽  
Noise Ratio ◽  
Subarray Beamforming ◽  
Beamforming Technique

Direction-of-arrival (DOA) estimation is always a hotspot research in the fields of radar, sonar, communication and so on. And uniform circular arrays (UCAs) are more attractive in the context of DOA estimation since their symmetrical structures have potential to provide two directions coverage. This paper proposed a new DOA estimation method for UCAs via virtual subarray beamforming technique. The method would provide an acceptable DOA estimate even if the number of sources is great than the number of array elements. Also, the performance of the proposed method would hold good when the snapshot length or the signal-to-noise ratio (SNR) is small. Simulations show that the proposed technique offers significantly improved estimation resolution, capacity, and accuracy relative to the existing techniques.

scholarly journals Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 339 ◽  
Author(s):  
Yongsong Li ◽  
Zhengzhou Li ◽  
Kai Wei ◽  
Weiqi Xiong ◽  
Jiangpeng Yu ◽  
...  
Keyword(s):  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Haar Wavelet ◽  
Image Sensor ◽  
Median Absolute Deviation ◽  
Noise Estimation ◽  
Estimation Methods ◽  
Absolute Deviation

Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks can be selected with several biggest LGSE values in a descending order. Then, the Haar wavelet-based local median absolute deviation (HLMAD) is presented to compute the local variance of these selected homogenous blocks. After that, the noise parameters can be estimated accurately by applying the maximum likelihood estimation (MLE) to analyze the local mean and variance of selected blocks. Extensive experiments on synthesized noised images are induced and the experimental results show that the proposed method could not only more accurately estimate the noise of various scene images with different noise levels than the compared state-of-the-art methods, but also promote the performance of the blind de-noising algorithm.

scholarly journals Underwater Ambiguity Elimination Method Based on Co-Prime Sensor Array

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6058
Author(s):  
Tian Lan ◽  
Yilin Wang ◽  
Longhao Qiu
Keyword(s):  
High Resolution ◽  
Sensor Array ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Spatial Spectrum ◽  
Estimation Methods ◽  
Sensing Applications ◽  
Passive Sensing ◽  
Spatial Spectra ◽  
Beam Patterns

Recently, the direction of arrival estimation with co-prime arrays has gradually been applied in underwater scenarios because of its significant advantages over traditional uniform linear arrays. Despite the advantages of co-prime arrays, the spatial spectra obtained directly from conventional beamforming can be degraded by grating lobes due to the sparse spatial sampling in passive sensing applications, which will seriously deteriorate the estimation performance. In this paper, capon beamforming is applied to a co-prime sensor array as a pretreatment before high-resolution direction of arrival (DOA) estimation methods. The amplitudes extracted from the beam-domain outputs of two subarrays and the phases extracted from the cross-spectrum of the spatial spectrum are exploited to suppress the spurious peaks in beam patterns and eliminate ambiguities. Consequently, interference can be further mitigated, and the performance of high-resolution DOA methods will be guaranteed. Simulations show that the method proposed can improve the reliability and accuracy of DOA estimation with great value in practice.

scholarly journals Evaluasi Modulasi MFSK untuk Transmisi Data Melalui Kanal Suara GSM

Jurnal INKOM ◽  
2014 ◽  
Vol 8 (1) ◽  
pp. 37
Author(s):  
Rika Sustika ◽  
Oka Mahendra
Keyword(s):  
Mobile Communication ◽  
Bit Error Rate ◽  
Error Rate ◽  
Gaussian Noise ◽  
Linear Prediction ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Signal To Noise ◽  
Global System ◽  
Shift Keying

Pada tulisan ini, dievaluasi performansi skema modulasi MFSK (M-ary Frequency Shift Keying) untuk aplikasi pengiriman data melalui kanal suara GSM (Global System for Mobile Communication). Parameter yang dievaluasi berupa kesalahan bit trasmisi yang dinyatakan dengan laju kesalahan bit atau bit error rate (BER). Evaluasi ini dilakukan untuk menentukan besarnya orde M yang akan dipilih pada aplikasi pengiriman data digital melalui kanal suara GSM. Pada proses simulasi, data digital dikodekan menjadi simbol-simbol lalu dimodulasi menggunakan modulator MFSK menjadi data menyerupai pembicaraan (suara). Suara yang dihasilkan dikodekan dengan algoritma CELP (Code Excited Linear Prediction), kemudian dikirimkan melalui udara yang dimodelkan sebagai kanal AWGN (Additive White Gaussian Noise). Di sisi penerima, sinyal terima yang menyerupai suara ini didemodulasi dan dikonversi kembali menjadi data digital. Dari simulasi menggunakan Eb/No (signal to noise ratio) sebesar 6 dB, diperoleh laju bit 2,5 kbps dengan BER 2,01 x 10-3 untuk M=4, 2,22 x 10-3 untuk M=8, dan 1,87 x 10-3 untuk M=16.

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