Improved FRM-based maximally decimated filter bank with NTB for software radio channelizer

Zhang, Wenxu; Li, Guangqi; Zhang, Wei; He, Junpeng; Chen, Tao

doi:10.1016/j.aeue.2018.03.025

Cited by 8 publications

(2 citation statements)

References 28 publications

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“…The application of the channelisation technology and filter bank technology to the design of a radar transmitter can improve the bandwidth of the transmitter, but it will cause some problems such as frequency imaging. When the transition bandwidth of an FB is extremely wide, the adjacent channel leakage increases and the spectrum utilisation is reduced [5]. When the transition bandwidth of an FB is extremely narrow, the order of the FB rapidly increases and occupies a lot of multiplier resources.…”

Section: Introductionmentioning

confidence: 99%

Design of a novel CEM‐FRM‐based low‐complexity channelised radar transmitter

Zhang

Cui

et al. 2021

IET Radar Sonar & Navi

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With the development of the radar technology, it is required that a radar has wider bandwidth and can transmit multiple signals simultaneously. The polyphase digital channelised transmitter structure can transmit multiple signals simultaneously and increase the bandwidth of the output signal. However, in order to reduce the frequency aliasing and improve the spectrum utilisation, the prototype filter needs to have sharp transition bandwidth. The design of the prototype filter directly affects the performance of the radar transmitter. Using the complex-exponential modulation frequency-response masking (CEM-FRM) approach to design the prototype filter can obtain both sharp transition bandwidth and low complexity. The application of a polyphase structure can further reduce the complexity of the filter bank and make the sampling rate of the output signal several times the field programmable gate array operating frequency. The proposed CEM-FRM-based low complexity polyphase channelised radar transmitter is verified by MATLAB simulation and the theoretical complexity of the proposed CEM-FRM-based channelised radar transmitter structure is compared with other methods.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Introductionmentioning

confidence: 99%

Design of a novel CEM‐FRM‐based low‐complexity channelised radar transmitter

Zhang

Cui

et al. 2021

IET Radar Sonar & Navi

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“…In the mean time, a uniform decimation has been applied to the subband components of a uniform filter bank to reduce the total number of the elements of all the represented components [19]- [21]. If this technique is applied to the singular spectrum analysis, then the rows of the trajectory matrix are the polyphase components of the original signal [16]- [18].…”

Section: Introductionmentioning

confidence: 99%

Length Reduction of Singular Spectrum Analysis With Guarantee Exact Perfect Reconstruction via Block Sliding Approach

Wang

Ling

2020

IEEE Access

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The conventional singular spectrum analysis is to divide a signal into segments where there is only one non-overlapping point between two consecutive segments. By putting these segments into the columns of a matrix and performing the singular value decomposition on the matrix, various one dimensional singular spectrum analysis vectors are obtained. Since different one dimensional singular spectrum analysis vectors represent different parts of the signal such as the trend part, the oscillation part and the noise part of the signal, the singular spectrum analysis plays a very important role in the nonlinear and adaptive signal analysis. However, as the length of each one dimensional singular spectrum analysis vector is the same as that of the original signal, there is a redundancy among these one dimensional singular spectrum analysis vectors. In order to reduce the required computational power and the required units for the memory storage for performing the singular spectrum analysis, this paper proposes a method to reduce the total number of the elements of all the one dimensional singular spectrum analysis vectors. In particular, the length of the shift block for generating the trajectory matrix is increased from one to a positive integer greater than one under a certain criterion. In this case, the total number of the columns of the trajectory matrix is reduced. As a result, the total number of the off-diagonals of all the two dimensional singular spectrum analysis matrices is reduced. Hence, the total number of the elements of all the one dimensional singular spectrum analysis vectors is reduced. In order to guarantee exact perfect reconstruction, this paper reformulates the de-Hankelization process. In particular, the first element of the off-diagonals of all the two dimensional singular spectrum analysis matrices is taken as the elements of the one dimensional singular spectrum analysis vectors. Exact perfect reconstruction condition is derived. Simulations show that exact perfect reconstruction can be achieved while the total number of the elements of all the one dimensional singular spectrum analysis vectors is significantly reduced.

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