scholarly journals Separate DOD and DOA Estimation for Bistatic MIMO Radar

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Lin Li ◽  
Fangfang Chen ◽  
Jisheng Dai
Keyword(s):  
Computational Complexity ◽  
Signal To Noise Ratio ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Music Algorithm ◽  
Signal To Noise ◽  
Bistatic Mimo Radar ◽  
Direction Of Departure ◽  
Type Algorithm ◽  
Simulation Results

A novel MUSIC-type algorithm is derived in this paper for the direction of departure (DOD) and direction of arrival (DOA) estimation in a bistatic MIMO radar. Through rearranging the received signal matrix, we illustrate that the DOD and the DOA can be separately estimated. Compared with conventional MUSIC-type algorithms, the proposed separate MUSIC algorithm can avoid the interference between DOD and DOA estimations effectively. Therefore, it is expected to give a better angle estimation performance and have a much lower computational complexity. Meanwhile, we demonstrate that our method is also effective for coherent targets in MIMO radar. Simulation results verify the efficiency of the proposed method, particularly when the signal-to-noise ratio (SNR) is low and/or the number of snapshots is small.

A Unitary-UCA-Root-MUSIC Algorithm Based on MSWF

2014 ◽  
Vol 610 ◽  
pp. 339-344
Author(s):  
Qiang Guo ◽  
Yun Fei An
Keyword(s):  
Computer Simulation ◽  
Computational Complexity ◽  
Real Time ◽  
Unitary Transformation ◽  
Wiener Filter ◽  
Doa Estimation ◽  
Signal Subspace ◽  
Music Algorithm ◽  
Multi Stage ◽  
Simulation Results

A UCA-Root-MUSIC algorithm for direction-of-arrival (DOA) estimation is proposed in this paper which is based on UCA-RB-MUSIC [1]. The method utilizes not only a unitary transformation matrix different from UCA-RB-MUSIC but also the multi-stage Wiener filter (MSWF) to estimate the signal subspace and the number of sources, so that the new method has lower computational complexity and is more conducive to the real-time implementation. The computer simulation results demonstrate the improvement with the proposed method.

scholarly journals Super resolution DOA estimation based on deep neural network

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Wanli Liu
Keyword(s):  
Neural Network ◽  
Potential Application ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Doa Estimation ◽  
Angular Distance ◽  
Signal To Noise ◽  
Simulation Results

AbstractRecently, deep neural network (DNN) studies on direction-of-arrival (DOA) estimations have attracted more and more attention. This new method gives an alternative way to deal with DOA problem and has successfully shown its potential application. However, these works are often restricted to previously known signal number, same signal-to-noise ratio (SNR) or large intersignal angular distance, which will hinder their generalization in real application. In this paper, we present a novel DNN framework that realizes higher resolution and better generalization to random signal number and SNR. Simulation results outperform that of previous works and reach the state of the art.

scholarly journals Target Localization Methods Based on Iterative Super-Resolution for Bistatic MIMO Radar

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 341
Author(s):  
Jianhe Du ◽  
Meng Han ◽  
Libiao Jin ◽  
Yan Hua ◽  
Shufeng Li
Keyword(s):  
Multiple Input Multiple Output ◽  
Super Resolution ◽  
Single Pulse ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Target Localization ◽  
Localization Accuracy ◽  
Bistatic Mimo Radar ◽  
Multiple Pulse ◽  
Direction Of Departure

The direction-of-departure (DOD) and the direction-of-arrival (DOA) are important localization parameters in bistatic MIMO radar. In this paper, we are interested in DOD/DOA estimation of both single-pulse and multiple-pulse multiple-input multiple-output (MIMO) radars. An iterative super-resolution target localization method is firstly proposed for single-pulse bistatic MIMO radar. During the iterative process, the estimated DOD and DOA can be moved from initial angles to their true values with high probability, and thus can achieve super-resolution estimation. It works well even if the number of targets is unknown. We then extend the proposed method to multiple-pulse configuration to estimate target numbers and localize targets. Compared with existing methods, both of our proposed algorithms have a higher localization accuracy and a more stable performance. Moreover, the proposed algorithms work well even with low sampling numbers and unknown target numbers. Simulation results demonstrate the effectiveness of the proposed methods.

Angle Estimation for Bistatic MIMO Radar with Unknown Mutual Coupling Based on Improved Propagator Method

2014 ◽  
Vol 513-517 ◽  
pp. 3029-3033 ◽  
Author(s):  
Jian Feng Li ◽  
Wei Yang Chen ◽  
Xiao Fei Zhang
Keyword(s):  
Mutual Coupling ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Rotational Invariance ◽  
Angle Estimation ◽  
Direction Of Departure ◽  
Input Multiple Output ◽  
Joint Direction

In this paper, joint direction of departure (DOD) and direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar with unknown mutual coupling is studied. An improved propagator calculation method is proposed to overcome the performance degradation problem when signal to noise ratio (SNR) is low. Thereafter, according to the Toeplitz structure of the mutual coupling matrix, the rotational invariance can be extracted for the angle estimation regardless of the mutual coupling from the augmented propagator matrix. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm and conventional PM-like method, and angles are automatically paired. The simulation results verify the effectiveness of the algorithm.

A Root-MUSIC Algorithm for Angle Estimation in Bistatic MIMO Radar

2013 ◽  
Vol 756-759 ◽  
pp. 4031-4035
Author(s):  
Jia Wei Liu ◽  
Ren Zheng Cao ◽  
Xiao Fei Zhang
Keyword(s):  
Doa Estimation ◽  
Mimo Radar ◽  
Signal Classification ◽  
Music Algorithm ◽  
Angle Estimation ◽  
Multiple Signal Classification ◽  
Direction Of Departure ◽  
Multiple Input ◽  
Roots Of Polynomials ◽  
Load Simulation

This paper discusses the problem of direction of departure (DOD) and direction of arrival (DOA) estimation using the root multiple signal classification (MUSIC) algorithm in a bistatic multiple input and multiple output (MIMO) radar. The proposed algorithm gets the estimation of DOA and DOD via computing the roots of polynomials and it avoids the spectral peak searching in the conventional MUSIC algorithm. Thus the Root-MUSIC algorithm has much lower computational load. Simulation results illustrate our proposed algorithm has better angle estimation performance than the conventional algorithms.

scholarly journals The PARAFAC-MUSIC Algorithm for DOA Estimation with Doppler Frequency in a MIMO Radar System

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Nan Wang ◽  
Wenguang Wang ◽  
Fan Zhang ◽  
Yunneng Yuan
Keyword(s):  
Doppler Frequency ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Radar System ◽  
Signal Classification ◽  
Moving Targets ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Parallel Factor ◽  
Simulation Results

The PARAFAC-MUSIC algorithm is proposed to estimate the direction-of-arrival (DOA) of the targets with Doppler frequency in a monostatic MIMO radar system in this paper. To estimate the Doppler frequency, the PARAFAC (parallel factor) algorithm is firstly utilized in the proposed algorithm, and after the compensation of Doppler frequency, MUSIC (multiple signal classification) algorithm is applied to estimate the DOA. By these two steps, the DOA of moving targets can be estimated successfully. Simulation results show that the proposed PARAFAC-MUSIC algorithm has a higher accuracy than the PARAFAC algorithm and the MUSIC algorithm in DOA estimation.

scholarly journals Joint DOD and DOA Estimation for High Speed Target Using Bistatic MIMO Radar

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jinli Chen ◽  
Jiaqiang Li ◽  
Peng Li ◽  
Yanping Zhu ◽  
Weijun Long
Keyword(s):  
High Speed ◽  
Matched Filter ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Slow Time ◽  
Bistatic Mimo Radar ◽  
Time Frequency ◽  
Direction Of Departure ◽  
Virtual Array ◽  
Range Migration

In bistatic multiple-input multiple-output (MIMO) radar, range migration and invalidly synthesized virtual array resulting from the serious mismatch of matched filter make it difficult to estimate direction of departure (DOD) and direction of arrival (DOA) of high speed target using the traditional superresolution algorithms. In this study, a method for joint DOD and DOA estimation of high speed target using bistatic MIMO radar is proposed. After multiplying the received signals with the conjugate of the delayed versions of the transmitted signals, Fourier transform (FT) of the multiplied signals over both fast time and slow time is employed. Then, the target components of radar return corresponding to the different transmitted waveforms can be perfectly separated at the receivers by extracting the target frequency-domain data along slow-time frequency dimension when the delay between the transmitted signals and their subsequent returns is timed. By splicing the separated target components distributed along several range cells, the virtual array can be formed, and then DOD and DOA of high speed target can be estimated using the superresolution algorithm with the range migration and the mismatch of matched filter properly removed. Simulation results have proved the validity of the proposed algorithm.

Improved Algorithm for Joint DOD and DOA Estimation in Bistatic MIMO Radar Based on Fourth Order Cumulants

2013 ◽  
Vol 694-697 ◽  
pp. 2550-2556 ◽  
Author(s):  
Ding Jie Xu ◽  
Mo Xuan Li ◽  
Xian Peng Wang
Keyword(s):  
Fourth Order ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Likelihood Method ◽  
Eigenvalue Decomposition ◽  
Invariance Property ◽  
Bistatic Mimo Radar ◽  
Direction Of Departure ◽  
Definition Of ◽  
Improved Algorithm

An improved algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation in bistatic MIMO radar based on fourth order cumulants is presented. Firstly, the data of receiver is reset and divided to acquire the rotational invariance property of transmitter and receiver, respectively. The fourth order cumulants matrixes in twain are constructed which are based on the basic definition of the cumulant. Then we use the propagator method (PM), which only requires linear operation but does not involve any eigendecomposition of the cumulant matrix, to estimate the DODs and DOAs, respectively. Finally, the maximum likelihood method is used to solve the pairing problem. The proposed method is effective in prohibiting the Gaussian colored noise and improves the performance of the angle estimation slightly. It does not need two-dimensional spectrum peak searching and eigenvalue decomposition on the cumulant matrix, thus the computation complexity is reduced. At the same time, it has no exceptive claim on the number of receive arrays or receive arrays. Simulation results verify the effectiveness and feasibility of the proposed method.

scholarly journals Cooperative Spectrum Sensing Based on Convolutional Neural Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4440
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Neural Networks ◽  
Spectrum Sensing ◽  
Convolutional Neural Networks ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Majority Voting ◽  
Signal To Noise ◽  
Hidden Terminal ◽  
Simulation Results ◽  
Sensing Performance

Cooperative spectrum sensing (CSS) is an important topic due to its capacity to solve the issue of the hidden terminal. However, the sensing performance of CSS is still poor, especially in low signal-to-noise ratio (SNR) situations. In this paper, convolutional neural networks (CNN) are considered to extract the features of the observed signal and, as a consequence, improve the sensing performance. More specifically, a novel two-dimensional dataset of the received signal is established and three classical CNN (LeNet, AlexNet and VGG-16)-based CSS schemes are trained and analyzed on the proposed dataset. In addition, sensing performance comparisons are made between the proposed CNN-based CSS schemes and the AND, OR, majority voting-based CSS schemes. The simulation results state that the sensing accuracy of the proposed schemes is greatly improved and the network depth helps with this.

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