Compressed sensing theory-based four-dimensional antenna array DOA estimation method

A compressed sensing, antenna array technology, applied in radio wave direction/bias determination systems, direction finders using electromagnetic waves, direction finders using radio waves, etc. The angle range is not large, etc.

Active Publication Date: 2019-04-05
UNIV OF ELECTRONICS SCI & TECH OF CHINA
View PDF7 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For example, the literature "G.Li, S.Yang, and Z.Nie, "Direction of arrival estimation in time modulated linear arrays with unidirectional phase center motion," IEEE Trans.Antennas Propag., vol.58, no.4, pp.1105 –1111,Apr.2010”disclosed a four-dimensional linear array DOA estimation method based on the MUSIC algorithm. Due to the use of the MUSIC algorithm, the estimation performance is poor under the conditions of low signal-to-noise ratio and low number of snapshots, and the estimated angle range not big
Document "C.He, A.Cao, J.Chen, X.Liang, W.Zhu, J.Geng and R.Jin, "Direction finding by time-modulated linear array", IEEE Trans.Antennas Propag.,vol. 66, no.7, pp.3642–3652, Mar.2018" discloses a method of wave arrival estimation using the specific relationship between harmonics and incident angles, and its corr...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Compressed sensing theory-based four-dimensional antenna array DOA estimation method
  • Compressed sensing theory-based four-dimensional antenna array DOA estimation method
  • Compressed sensing theory-based four-dimensional antenna array DOA estimation method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0067] Example 1: Optimal timing of 8-unit half-wavelength four-dimensional linear array

[0068] Consider a uniform 4D linear array of 8 elements. The center frequency and switching modulation frequency are set to f 0 = 3GHz and f p =100KHz, the timing adopts the pulse translation timing. In order to include the angle of the entire space as much as possible while taking into account the calculation efficiency, the entire upper half space is evenly divided into 180 parts from -90 to 90, that is, sampling is performed at intervals of 1 degree. Later, after roughly knowing the range of the incident direction of the signal, it can be more accurate. Angle division. The differential evolution algorithm mentioned above is used to optimize the time sequence so that the fitness function value of (20) is as small as possible. At the same time, in order to analyze the influence of the number of sidebands taken, Q=2 and Q=3 are used for timing optimization respectively.

[0069] f...

Embodiment 2

[0070] Example 2: Spatial spectrum estimated by 8-unit half-wavelength four-dimensional linear array DOA

[0071] In order to illustrate the effectiveness of the optimized time series for 4D array DOA estimation, the above optimized time series is used for DOA estimation, which is mainly illustrated by calculating the estimated spatial spectrum. Assume that there are three narrowband signals with the same frequency and the same power from the far field θ 1 =-10°, θ 2 = 0° and θ 3 =5° is incident on this 8-element four-dimensional linear array. Other related parameters are set as follows: number of snapshots L=100, number of sidebands Q=3. Figure 5Indicates the spatial spectrum calculated by the sparse signal recovery algorithm according to the optimized timing as a function of the signal-to-noise ratio. It can be seen that even under the condition of low signal-to-noise ratio, the incident direction of the signal can be estimated by using the optimized timing. For compar...

Embodiment 3

[0072] Example 3: Resolution probability of 8-unit four-dimensional linear array DOA estimation

[0073] In order to continue to illustrate the effectiveness of the optimized timing for 4D array DOA estimation, it is illustrated here by comparing the estimated resolution probabilities. Suppose there are two narrowband signals with the same frequency and the same power from the far field θ 1 = -10° and θ 2 =0° is incident on this 8-element four-dimensional linear array. Other related parameters are set as follows: number of snapshots L=100, number of sidebands Q=3. Figure 7 The variation of the estimated resolution probability with the signal-to-noise ratio is described when the proposed method of the present invention and the MUSIC algorithm perform DOA estimation under the same parameter conditions. It can be found that the estimation probability of the present invention is higher than that of the MUSIC algorithm under the condition of low signal-to-noise ratio. Next, we...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a compressed sensing theory-based four-dimensional antenna array DOA estimation method. According to the method, a sparse signal model for four-dimensional antenna array DOA estimation is established to find that time sequences have important influences on sparse signal recovery, and an improper time sequence is possible to change time modulated noise into color noise so asto seriously worsen the sparse signal recover ability. Therefore, matrix dependency and a noise covariance matrix are introduced to quantitatively analyze the influences, on sparse signal recovery and noises, of different time sequences; and on such basis, a differential evolution algorithm is utilized to establish a time sequence-oriented optimization model. An I1 norm singular value decomposition-based sparse signal recovery algorithm applied to traditional arrays is extended into four-dimensional antenna arrays and the optimized time sequences are combined to carry out four-dimensional antenna array DOA estimation. Numerical simulation results prove that the method has large advantages in the aspects of resolution characteristic and accuracy characteristic when being compared with other four-dimensional array DOA estimation methods, particularly under the conditions of low signal to noise ratio and small snapshot number.

Description

technical field [0001] The invention belongs to the field of antenna technology and signal processing, and relates to how to use a four-dimensional antenna array to perform DOA estimation. Specifically, the frequency domain sideband signal and the center frequency signal formed by the time modulation effect of the four-dimensional antenna array form a signal space. The sparse signal recovery signal model is established by using the inherent characteristics that the actual incident signal is limited, and the timing sequence of the control switch is optimized by using the compressed sensing theory to realize high-performance DOA estimation of the four-dimensional antenna array. Background technique [0002] DOA estimation, also known as direction of arrival estimation, refers to the use of array antennas to receive electromagnetic wave signals to obtain angle information of targets or sources relative to array antennas, including angles and quantities. With the rapid developme...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G01S3/14G01S3/78
Inventor 杨仕文杨锋陈科锦孙磊龙伟军李斌陈益凯
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products