MIMO radar sequence design with constant envelope and low correlation side-lobe levels

2021 ◽  
Vol 136 ◽  
pp. 153769
Author(s):  
Ankur Thakur ◽  
Davinder Singh Saini
Keyword(s):  
Side Lobe ◽  
Mimo Radar ◽  
Sequence Design ◽  
Low Correlation ◽  
Constant Envelope

scholarly journals Designing a waveform using LFM, NLFM for MIMO radar

2017 ◽  
Vol 7 (1.5) ◽  
pp. 84
Author(s):  
G S Krishnam Naidu Yedla ◽  
D. Siva Sankar Prasad ◽  
P. Raghavendra Rao ◽  
M Siva Kumar ◽  
M VenuGopala Rao
Keyword(s):  
Frequency Modulation ◽  
Frequency Component ◽  
Side Lobe ◽  
Mimo Radar ◽  
Linear Frequency Modulation ◽  
Wave Form ◽  
Boundary Segment ◽  
Middle Segment ◽  
Time Frequency ◽  
Linear Frequency

We propose a waveform that includes Linear frequency modulation and non linear frequency modulation wave applicable for MIMO radar. The wave form consists of three segments where the boundary segment consists of LFM content and the middle segment consists of NLFM. The time frequency component in the middle segment is controlled. The range and Doppler side lobe suppression is improved. The genetic algorithm is implemented to suppress the side lobes in the auto correlation and cross correlation functions. The performance is analysed by using ambiguity function.

scholarly journals SELECTING THE LORENZ PARAMETERS FOR WIDEBAND RADAR WAVEFORM GENERATION

2011 ◽  
Vol 21 (09) ◽  
pp. 2539-2545 ◽  
Author(s):  
MATT S. WILLSEY ◽  
KEVIN M. CUOMO ◽  
ALAN V. OPPENHEIM
Keyword(s):  
Autocorrelation Function ◽  
Lorenz System ◽  
Power Level ◽  
Side Lobe ◽  
Spectral Leakage ◽  
System Parameters ◽  
Constant Envelope ◽  
Radar Waveforms ◽  
The Lorenz System ◽  
Wideband Radar

Radar waveforms based on chaotic systems have occasionally been suggested for a variety of radar applications. In this paper, radar waveforms are constructed with solutions from a particular chaotic system, the Lorenz system, and are called Lorenz waveforms. Waveform properties, which include the peak autocorrelation function side-lobe and the transmit power level, are related to the system parameters of the Lorenz system. Additionally, scaling the system parameters is shown to correspond to an approximate time and amplitude scaling of Lorenz waveforms and also corresponds to scaling the waveform bandwidth. The Lorenz waveforms can be generated with arbitrary time lengths and bandwidths and each waveform can be represented with only a few system parameters. Furthermore, these waveforms can then be systematically improved to yield constant-envelope output waveforms with low autocorrelation function sidelobes and limited spectral leakage.

A nonlinear-ADMM method for designing MIMO radar constant modulus waveform with low correlation sidelobes

2019 ◽  
Vol 159 ◽  
pp. 93-103 ◽  
Author(s):  
Ziyang Cheng ◽  
Bin Liao ◽  
Zishu He ◽  
Jun Li ◽  
Chunlin Han
Keyword(s):  
Mimo Radar ◽  
Constant Modulus ◽  
Low Correlation

scholarly journals An MIMO Radar System Based on the Sparse-Array and Its Frequency Migration Calibration Method

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3580 ◽  
Author(s):  
Yue Ma ◽  
Chen Miao ◽  
Yangying Zhao ◽  
Wen Wu
Keyword(s):  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Calibration Method ◽  
Transformation Method ◽  
Side Lobe ◽  
Mimo Radar ◽  
Radar System ◽  
Side Lobe Level ◽  
Automotive Radar ◽  
Sparse Array

In this paper, a Multiple Input Multiple Output (MIMO) radar system based on a sparse-array is proposed. In order to reduce the side-lobe level, a genetic algorithm (GA) is used to optimize the array arrangement. To reduce the complexity of the system, time-division multiplexing (TDM) technology is adopted. Since the signals are received in different periods, a frequency migration will emerge if the target is in motion, which will lead to the lower direction-of-arrival (DOA) performance of the system. To solve this problem, a stretching transformation method in the fast-frequency slow-time domain is proposed, in order to eliminate frequency migration. Only minor adjustments need to be implemented for the signal processing, and the root-mean-square error (RMSE) of the DOA estimation will be reduced by about 90%, compared with the one of an uncalibrated system. For example, a uniform linear array (ULA) MIMO system with 2 transmitters and 20 receivers can be replaced by the proposed system with 2 transmitters and 12 receivers, achieving the same DOA performance. The calibration formulations are given, and the simulation results of the automotive radar system are also provided, which validate the theory.

Millimeter-Wave Phase-Coded CW MIMO Radar Using Zero- and Low-Correlation-Zone Sequence Sets

2016 ◽  
Vol 64 (12) ◽  
pp. 4312-4323 ◽  
Author(s):  
Heinz Haderer ◽  
Reinhard Feger ◽  
Clemens Pfeffer ◽  
Andreas Stelzer
Keyword(s):  
Millimeter Wave ◽  
Mimo Radar ◽  
Wave Phase ◽  
Low Correlation

scholarly journals Detection of Subsurface Target Based on FDA-MIMO Radar

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Qinghua Liu ◽  
Chang Jiang ◽  
Liangnian Jin ◽  
Shan Ouyang
Keyword(s):  
Target Detection ◽  
Side Lobe ◽  
Mimo Radar ◽  
Far Field ◽  
Radar Detection ◽  
Signal Model ◽  
Theoretical Simulation ◽  
New Type ◽  
Good Improvement ◽  
Side Lobe Suppression

As a new type of radar, the FDA-MIMO radar has a good improvement on side lobe suppression and target detection performance compared with the conventional MIMO radar. However, the existing researches on FDA-MIMO radar are almost based on far-field. In this paper, FDA-MIMO radar is applied to the detection of subsurface targets. Aimed at near-subsurface targets, we formulated the signal model of FDA-MIMO radar and combined it with the algorithm of grid of beam (GOB) to detect. Compared with conventional MIMO radar detection, we verified the effectiveness of the proposed method through theoretical simulation.

scholarly journals New correlated MIMO radar covariance matrix design with low side lobe levels and much lower complexity

2021 ◽  
Vol 34 (1) ◽  
pp. 327-335
Author(s):  
Roholah VAHDANI ◽  
Hossein KHALEGHI BIZAKI ◽  
Mohsen FALLAH JOSHAGHANI
Keyword(s):  
Covariance Matrix ◽  
Side Lobe ◽  
Mimo Radar ◽  
Lower Complexity ◽  
Matrix Design
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