scholarly journals Three-Dimensional Target Localization and Cramér-Rao Bound for Two-Dimensional OFDM-MIMO Radar

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Xingxing Li ◽  
Dangwei Wang ◽  
Xiaoyan Ma
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Mimo Radar ◽  
Joint Estimation ◽  
Target Localization ◽  
Two Dimensional ◽  
Frequency Diversity ◽  
Cramer Rao Bound

Target localization using a frequency diversity multiple-input multiple-output (MIMO) system is one of the hottest research directions in the radar society. In this paper, three-dimensional (3D) target localization is considered for two-dimensional MIMO radar with orthogonal frequency division multiplexing linear frequency modulated (OFDM-LFM) waveforms. To realize joint estimation for range and angle in azimuth and elevation, the range-angle-dependent beam pattern with high range resolution is produced by the OFDM-LFM waveform. Then, the 3D target localization proposal is presented and the corresponding closed-form expressions of Cramér-Rao bound (CRB) are derived. Furthermore, for mitigating the coupling of angle and range and further improving the estimation precision, a CRB optimization method is proposed. Different from the existing methods of FDA-based radar, the proposed method can provide higher range estimation because of multiple transmitted frequency bands. Numerical simulation results are provided to demonstrate the effectiveness of the proposed approach and its improved performance of target localization.

scholarly journals Three-Dimensional High-Resolution MIMO Radar Imaging via OFDM Modulation and Unitary ESPRIT

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Jingjing Zhao ◽  
Yongxiang Liu ◽  
Kai Huo ◽  
Jiaxi Ye ◽  
Bo Xiao
Keyword(s):  
High Resolution ◽  
Orthogonal Frequency Division Multiplexing ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Mimo Radar ◽  
Eigenvalue Decomposition ◽  
Resolution Image ◽  
High Resolution Image ◽  
Ofdm Modulation

Imaging and recognition of targets with complex maneuvers bring a new challenge to conventional radar applications. In this paper, the three-dimensional (3D) high-resolution image is attained in real-time by a Multiple-Input-Multiple-Output (MIMO) radar system with single Orthogonal-Frequency-Division-Multiplexing (OFDM) pulse. First, to build the orthogonal transmit waveform set for MIMO transmission, we utilize complex orthogonal designs (CODs) for OFDM subcarrier modulation. Based on the OFDM modulation, a preprocessing method is developed for transmit waveform separation without conventional matched filtering. The result array manifold is the Kronecker product of the steering vectors of subcarrier/transmit antenna/receive antenna uniform linear arrays (ULAs). Then, the high-resolution image of target is attained by the Multidimensional Unitary Estimation of Signal Parameters via Rotational Invariant Techniques (MD-UESPRIT) algorithm. The proposed imaging procedures include the multidimensional spatial smoothing, the unitary transform via backward-forward averaging, and the joint eigenvalue decomposition (JEVD) algorithm for automatically paired coordinates estimation. Simulation tests compare the reconstruction results with the conventional methods and analyze the estimation precision relative to signal-to-noise ratio (SNR), system parameters, and errors.

scholarly journals Interchannel Interference and Mitigation in Distributed MIMO RF Sensing

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7496
Author(s):  
Sahil Waqar ◽  
Matthias Pätzold
Keyword(s):  
Continuous Wave ◽  
Channel Model ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Mimo Radar ◽  
Radar System ◽  
Channel Interference ◽  
Rf Sensing ◽  
K Band

In this paper, we analyze and mitigate the cross-channel interference, which is found in multiple-input multiple-output (MIMO) radio frequency (RF) sensing systems. For a millimeter wave (mm-Wave) MIMO system, we present a geometrical three-dimensional (3D) channel model to simulate the time-variant (TV) trajectories of a moving scatterer. We collected RF data using a state-of-the-art radar known as Ancortek SDR-KIT 2400T2R4, which is a frequency-modulated continuous wave (FMCW) MIMO radar system operating in the K-band. The Ancortek radar is currently the only K-band MIMO commercial radar system that offers customized antenna configurations. It is shown that this radar system encounters the problem of interference between the various subchannels. We propose an optimal approach to mitigate the problem of cross-channel interference by inducing a propagation delay in one of the channels and apply range gating. The measurement results prove the effectiveness of the proposed approach by demonstrating a complete elimination of the interference problem. The application of the proposed solution on Ancortek’s SDR-KIT 2400T2R4 allows resolving all subchannel links in a distributed MIMO configuration. This allows using MIMO RF sensing techniques to track a moving scatterer (target) regardless of its direction of motion.

A companding approach for PAPR suppression in OFDM based massive MIMO system

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ajay Kumar Yadav ◽  
Pritam Keshari Sahoo ◽  
Yogendra Kumar Prajapati
Keyword(s):  
Massive Mimo ◽  
Orthogonal Frequency Division Multiplexing ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Average Power ◽  
Base Station ◽  
Convex Optimization Problem ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Ofdm

Abstract Orthogonal frequency division multiplexing (OFDM) based massive multiuser (MU) multiple input multiple output (MIMO) system is popularly known as high peak-to-average power ratio (PAPR) issue. The OFDM-based massive MIMO system exhibits large number of antennas at Base Station (BS) due to the use of large number of high-power amplifiers (HPA). High PAPR causes HPAs to work in a nonlinear region, and hardware cost of nonlinear HPAs are very high and also power inefficient. Hence, to tackle this problem, this manuscript suggests a novel scheme based on the joint MU precoding and PAPR minimization (PP) expressed as a convex optimization problem solved by steepest gradient descent (GD) with μ-law companding approach. Therefore, we develop a new scheme mentioned to as MU-PP-GDs with μ-law companding to minimize PAPR by compressing and enlarging of massive MIMO OFDM signals simultaneously. At CCDF = 10−3, the proposed scheme (MU-PP-GDs with μ-law companding for Iterations = 100) minimizes the PAPR to 3.70 dB which is better than that of MU-PP-GDs, (iteration = 100) as shown in simulation results.

scholarly journals Fast Estimation Method of Space-Time Two-Dimensional Positioning Parameters Based on Hadamard Product

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Haiwen Li ◽  
Nae Zheng ◽  
Xiyu Song ◽  
Yinghua Tian
Keyword(s):  
Frequency Domain ◽  
Orthogonal Frequency Division Multiplexing ◽  
Hadamard Product ◽  
Estimation Method ◽  
Space Time ◽  
Joint Estimation ◽  
Two Dimensional ◽  
Music Algorithm ◽  
Fast Estimation ◽  
Response Vector

The estimation speed of positioning parameters determines the effectiveness of the positioning system. The time of arrival (TOA) and direction of arrival (DOA) parameters can be estimated by the space-time two-dimensional multiple signal classification (2D-MUSIC) algorithm for array antenna. However, this algorithm needs much time to complete the two-dimensional pseudo spectral peak search, which makes it difficult to apply in practice. Aiming at solving this problem, a fast estimation method of space-time two-dimensional positioning parameters based on Hadamard product is proposed in orthogonal frequency division multiplexing (OFDM) system, and the Cramer-Rao bound (CRB) is also presented. Firstly, according to the channel frequency domain response vector of each array, the channel frequency domain estimation vector is constructed using the Hadamard product form containing location information. Then, the autocorrelation matrix of the channel response vector for the extended array element in frequency domain and the noise subspace are calculated successively. Finally, by combining the closed-form solution and parameter pairing, the fast joint estimation for time delay and arrival direction is accomplished. The theoretical analysis and simulation results show that the proposed algorithm can significantly reduce the computational complexity and guarantee that the estimation accuracy is not only better than estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm and 2D matrix pencil (MP) algorithm but also close to 2D-MUSIC algorithm. Moreover, the proposed algorithm also has certain adaptability to multipath environment and effectively improves the ability of fast acquisition of location parameters.

scholarly journals A Novel Unitary ESPRIT Algorithm for Monostatic FDA-MIMO Radar

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 827 ◽  
Author(s):  
Feilong Liu ◽  
Xianpeng Wang ◽  
Mengxing Huang ◽  
Liangtian Wan ◽  
Huafei Wang ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Estimation Accuracy ◽  
Phase Ambiguity ◽  
Steering Vector ◽  
Input Multiple Output ◽  
Joint Direction ◽  
Esprit Algorithm ◽  
Cramer Rao Bound

A novel unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, for the joint direction of arrival (DOA) and range estimation in a monostatic multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA), is proposed. Firstly, by utilizing the property of Centro-Hermitian of the received data, the extended real-valued data is constructed to improve estimation accuracy and reduce computational complexity via unitary transformation. Then, to avoid the coupling between the angle and range in the transmitting array steering vector, the DOA is estimated by using the rotation invariance of the receiving subarrays. Thereafter, an automatic pairing method is applied to estimate the range of the target. Since phase ambiguity is caused by the phase periodicity of the transmitting array steering vector, a removal method of phase ambiguity is proposed. Finally, the expression of Cramér–Rao Bound (CRB) is derived and the computational complexity of the proposed algorithm is compared with the ESPRIT algorithm. The effectiveness of the proposed algorithm is verified by simulation results.

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.

scholarly journals A MIMO Radar-Based DOA Estimation Structure Using Compressive Measurements

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4706 ◽  
Author(s):  
Tao Chen ◽  
Jian Yang ◽  
Muran Guo
Keyword(s):  
Antenna Arrays ◽  
Multiple Input Multiple Output ◽  
Information Criterion ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Optimization Approach ◽  
Measurement Matrix ◽  
Estimation Performance ◽  
Input Multiple Output ◽  
Cramer Rao Bound

In this paper, we propose a novel direction-of-arrival (DOA) estimation structure based on multiple-input multiple-output (MIMO) radar with colocated antennas, referred to as compressive measurement-based MIMO (CM-MIMO) radar, where the compressive sensing (CS) is employed to reduce the number of channels. Therefore, the system complexity and the computational burden are effectively reduced. It is noted that CS is used after the matched filters and that a measurement matrix with less rows than columns is multiplied with the received signals. As a result, the configurations of the transmit and receive antenna arrays are not affected by the CS and can be determined according to the practical requirements. To study the estimation performance, the Cramér–Rao bound (CRB) with respect to the DOAs of the proposed CM-MIMO radar is analyzed in this paper. The derived CRB expression is also suitable for the conventional MIMO radar by setting the measurement matrix as an identity matrix. Moreover, the CRB expression can work in the under-determined case, since the sum-difference coarray structure is considered. However, the random measurement matrix leads to high information loss, thus compromising the estimation performance. To overcome this problem, we consider that the a prior probability distribution of the DOAs associated with the targets can be obtained in many scenarios and an optimization approach for the measurement matrix is proposed in this paper, where the maximum mutual information criterion is adopted. The superiority of the proposed structure is validated by numerical simulations.

scholarly journals ANALISIS UNJUK KERJA ZERO FORCING EQUALIZER PADA SISTEM OFDM MIMO

2018 ◽  
Vol 5 (1) ◽  
pp. 68
Author(s):  
R. A. Prayitno ◽  
N.M.A.E.D. Wirastuti ◽  
I.G.A.K.D.D. Hartawan
Keyword(s):  
No Value ◽  
Orthogonal Frequency Division Multiplexing ◽  
Mimo System ◽  
Multipath Propagation ◽  
Multiple Input Multiple Output ◽  
Ideal Point ◽  
Zero Forcing ◽  
Eye Diagram ◽  
Eye Pattern ◽  
The Ideal

Wireless network is one of the most important things in the development of telecommunication. However, the existing wireless technology has not been able to efficiently create a very high data rate because it was very sensitive to fading. Therefore, Orthogonal Frequency Division Multiplexing (OFDM) technology combined with MIMO (Multiple Input Multiple Output) transceiver system was used to gain more diversity and bandwidth efficiency. The propagation performed on the OFDM MIMO system was multipath propagation. To reduce Intersymbol Interference (ISI) was used Zero Forcing (ZF) equalizer which works by combining channel response and equalizer response itself to eliminate ISI. This study aims to determine the effect of Zero Forcing Equalizer on OFDM MIMO system using rayleigh fading channel and compared the result with OFDM MIMO STBC system. The research method used was simulation using MatLab R2015a. The simulation results showed that the performance of OFDM MIMO ZF system was worse than OFDM MIMO STBC system i.e BER vs EbNo simulation, eye diagram simulation, and constellation diagram simulation. The OFDM MIMO ZF system was required an Eb / No value more than 25 dB to achieve BER 10-4 while the OFDM MIMO STBC system only required an Eb / No value of 10.5 dB to achieve BER 10-4. The eye pattern generated by the OFDM MIMO ZF system was more closed and the dispersion of constellation signals away from the ideal point while OFDM MIMO STBC system displayed a more open eye pattern and the dispersion of its constellation signal closer to the ideal point. It indicated more ISI occurs in the OFDM MIMO ZF system than that in OFDM MIMO STBC system.

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