scholarly journals Target Localization Using Double-Sided Bistatic Range Measurements in Distributed MIMO Radar Systems

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2524 ◽  
Author(s):  
Hyuksoo Shin ◽  
Wonzoo Chung
Keyword(s):  
Multiple Input Multiple Output ◽  
Target Position ◽  
Mimo Radar ◽  
Target Localization ◽  
Range Measurements ◽  
Performance Improvements ◽  
Localization Algorithms ◽  
Novel Approach ◽  
Multiple Input ◽  
Input Multiple Output

We develop a novel approach improving existing target localization algorithms for distributed multiple-input multiple-output (MIMO) radars based on bistatic range measurements (BRMs). In the proposed algorithms, we estimate the target position with auxiliary parameters consisting of both the target–transmitter distances and the target–receiver distances (hence, “double-sided”) in contrast to the existing BRM methods. Furthermore, we apply the double-sided approach to multistage BRM methods. Performance improvements were demonstrated via simulations and a limited theoretical analysis was attempted for the ideal two-dimensional case.

scholarly journals DOA and Range Estimation for FDA-MIMO Radar with Sparse Bayesian Learning

2021 ◽  
Vol 13 (13) ◽  
pp. 2553
Author(s):  
Qi Liu ◽  
Xianpeng Wang ◽  
Mengxing Huang ◽  
Xiang Lan ◽  
Lu Sun
Keyword(s):  
Bayesian Learning ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Signal Recovery ◽  
Sparse Bayesian Learning ◽  
Localization Algorithms ◽  
Multiple Input ◽  
Dependent Component ◽  
Input Multiple Output ◽  
Grid Points

Due to grid division, the existing target localization algorithms based on sparse signal recovery for the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar not only suffer from high computational complexity but also encounter significant estimation performance degradation caused by off-grid gaps. To tackle the aforementioned problems, an effective off-grid Sparse Bayesian Learning (SBL) method is proposed in this paper, which enables the calculation the direction of arrival (DOA) and range estimates. First of all, the angle-dependent component is split by reconstructing the received data and contributes to immediately extract rough DOA estimates with the root SBL algorithm, which, subsequently, are utilized to obtain the paired rough range estimates. Furthermore, a discrete grid is constructed by the rough DOA and range estimates, and the 2D-SBL model is proposed to optimize the rough DOA and range estimates. Moreover, the expectation-maximization (EM) algorithm is utilized to update the grid points iteratively to further eliminate the errors caused by the off-grid model. Finally, theoretical analyses and numerical simulations illustrate the effectiveness and superiority of the proposed method.

Parameter Estimation and Cramer-Rao Bound in Wideband Bistatic MIMO Radar System

2015 ◽  
Vol 713-715 ◽  
pp. 651-655 ◽  
Author(s):  
Li Li
Keyword(s):  
Parameter Estimation ◽  
Fractional Fourier Transform ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Radar System ◽  
Novel Approach ◽  
Direction Of Departure ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Cramer Rao Bound

The problem of target localization and parameter estimation in wideband bistatic Multiple-Input Multiple-Output (MIMO) radar system is considered. In this paper, we use a novel approach to estimate Doppler stretch and time delay in fractional Fourier transform (FRFT) domain. We also develop two sub-array models to accurately estimate the direction-of-departure (DOD) and the direction-of-arrival (DOA). Furthermore, the Cramér-Rao bound for target parameter estimation is derived and computed in closed form. Parameter estimation performances are evaluated and studied theoretically and via simulations

Target Localization for Distributed Multiple-Input Multiple-Output Radar Using Capon Algorithm with Diagonal Loading

2012 ◽  
Vol 229-231 ◽  
pp. 1599-1604
Author(s):  
Jin Li Chen ◽  
Jia Qiang Li ◽  
Yan Ping Zhu
Keyword(s):  
High Resolution ◽  
Covariance Matrix ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Target Localization ◽  
Diagonal Loading ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Simulation Results ◽  
High Level

The distributed multiple-input multiple-output (MIMO) radar can achieve the high- resolution capabilities of target localization by coherent processing, far exceeding the bandwidth-dependent resolution of traditional radar. The conventional beam former synchronizing the phase across the widely separated transmitting and receiving antennas creates high level sidelobes that causes ambiguity in target localization. The Capon beam former with lower level sidelobes for target localization suffers from the irreversible of the covariance matrix when the numbers of transmitting and receiving antennas increase. Thus, the Capon algorithm with diagonal loading is applied to distributed MIMO radar for target localization with lower level sidelobes. Simulation results are presented to verify the effectiveness of the proposed method.

scholarly journals Angle Estimation for MIMO Radar in the Presence of Gain-Phase Errors with One Instrumental Tx/Rx Sensor: A Theoretical and Numerical Study

2021 ◽  
Vol 13 (15) ◽  
pp. 2964
Author(s):  
Fangqing Wen ◽  
Junpeng Shi ◽  
Xinhai Wang ◽  
Lin Wang
Keyword(s):  
Numerical Study ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Phase Errors ◽  
Direction Of Departure ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Angle Pair ◽  
Parallel Factor ◽  
Parafac Decomposition

Ideal transmitting and receiving (Tx/Rx) array response is always desirable in multiple-input multiple-output (MIMO) radar. In practice, nevertheless, Tx/Rx arrays may be susceptible to unknown gain-phase errors (GPE) and yield seriously decreased positioning accuracy. This paper focuses on the direction-of-departure (DOD) and direction-of-arrival (DOA) problem in bistatic MIMO radar with unknown gain-phase errors (GPE). A novel parallel factor (PARAFAC) estimator is proposed. The factor matrices containing DOD and DOA are firstly obtained via PARAFAC decomposition. One DOD-DOA pair estimation is then accomplished from the spectrum searching. Thereafter, the remainder DOD and DOA are achieved by the least squares technique with the previous estimated angle pair. The proposed estimator is analyzed in detail. It only requires one instrumental Tx/Rx sensor, and it outperforms the state-of-the-art algorithms. Numerical simulations verify the theoretical advantages.

scholarly journals Beam division multiple access for millimeter wave massive MIMO: Hybrid zero-forcing beamforming with user selection

2022 ◽  
Vol 12 (1) ◽  
pp. 445
Author(s):  
Hong Son Vu ◽  
Kien Truong ◽  
Minh Thuy Le
Keyword(s):  
Multiple Access ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Zero Forcing ◽  
Multiuser Interference ◽  
Novel Approach ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Promising Solution

<p>Massive multiple-input multiple-output (MIMO) systems are considered a promising solution to minimize multiuser interference (MUI) based on simple precoding techniques with a massive antenna array at a base station (BS). This paper presents a novel approach of beam division multiple access (BDMA) which BS transmit signals to multiusers at the same time via different beams based on hybrid beamforming and user-beam schedule. With the selection of users whose steering vectors are orthogonal to each other, interference between users is significantly improved. While, the efficiency spectrum of proposed scheme reaches to the performance of fully digital solutions, the multiuser interference is considerably reduced.</p>

scholarly journals A Barrage Jamming Strategy Based on CRB Maximization against Distributed MIMO Radar

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2453 ◽  
Author(s):  
Guangyong Zheng ◽  
Siqi Na ◽  
Tianyao Huang ◽  
Lulu Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Parameters Estimation ◽  
Swarm Optimization ◽  
Counter Measures ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Simulation Results ◽  
Cramer Rao Bound

Distributed multiple input multiple output (MIMO) radar has attracted much attention for its improved detection and estimation performance as well as enhanced electronic counter-counter measures (ECCM) ability. To protect the target from being detected and tracked by such radar, we consider a barrage jamming strategy towards a distributed MIMO. We first derive the Cramer–Rao bound (CRB) of target parameters estimation using a distributed MIMO under barrage jamming environments. We then set maximizing the CRB as the criterion for jamming resource allocation, aiming at degrading the accuracy of target parameters estimation. Due to the non-convexity of the CRB maximizing problem, particle swarm optimization is used to solve the problem. Simulation results demonstrate the advantages of the proposed strategy over traditional jamming methods.

Oil-Spills Detection in MIMO-SAR Radar Images by High Order Statistics

2013 ◽  
Vol 443 ◽  
pp. 649-652
Author(s):  
Yan Ling Luo
Keyword(s):  
Oil Spills ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Radar Signal ◽  
Future Research ◽  
Signal Model ◽  
Radar Images ◽  
Multiple Input ◽  
Input Multiple Output ◽  
High Order Statistics

MIMO radar (Multiple input multiple output radar) is a hot topic which gets lots of attention from researchers all around the world recently. It can achieve better detection performance than conventional phased radar. In this paper, the MIMO radar signal model is studied, and then the concept of MIMO radar is applied into SAR. The technique is employed to detect the oil spill in sea. At last, some conclusion is drawn. And some item for future research in presented also.

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