Blind adaptive channel mismatch equalisation method for GNSS antenna arrays

Lu, Zukun; Chen, Huaming; Chen, Feiqiang; Nie, Junwei; Ou, Gang

doi:10.1049/iet-rsn.2017.0416

Cited by 21 publications

(16 citation statements)

References 13 publications

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“…The iterative formula of LMS algorithm is as follows 20 :

w_{N} false(k + 1 false) = w_{N} false(k false) + μ x_{N} false(k false) e_{N}^{*} false(k false)

where the negative sign represents the iteration to the gradient negative square. The iterative formula with variable step size can be written as follows 29 :

w_{N} false(k + 1 false) = w_{N} false(k false) + μ false(k false) x_{N} false(k false) e_{N}^{*} false(k false)

…”

Section: Methodsmentioning

confidence: 99%

“…In the multipath channel of the baseband signal, the signal is composed of M paths, where ( M − 1) is a multipath, and n ( t ) is signal noise, which is generally referred to as Gaussian white noise. Therefore, in the multipath fading channel, the signal received by the receiver is expressed by the following formula 20‐22 :

r false(t false) = {false\sum}_{k = 1}^{M} A_{k} s false(t - τ_{k} false) e^{j false(ϕ_{k} false)} + n false(t false)

where A k , τ k , and ϕ k respectively correspond to the amplitude, multipath delay parameter, and instantaneous phase in the multipath signal. Therefore, the output of the correlator of the corresponding baseband signal is:

y false(τ false) = {false\sum}_{k = 1}^{M} a_{k} g false(τ - τ_{k} false) + ω false(τ false)

where

a_{k} = A_{k} e^{j ϕ_{k}}

is the complex path coefficient.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Channel compensation multipath mitigation technique for Kalman‐Based Least Mean Square based on Kalman estimation

Wang

Zhao

et al. 2020

Concurrency and Computation

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In the Global Navigation Satellite System, the traditional adaptive equalization algorithm re‐captures and tracks poor performance under the multipath channel for multipath errors caused by occlusion or reflection of buildings. A channel compensation multipath mitigation technique based on Kalman‐Based Least Mean Square based (KBLMS) on Kalman estimation. First, in the tracking loop of the receiver, a KBLMS‐based delay estimation module is designed to compensate for multipath distortion on the received signal. The coefficients of the filter are adaptively adjusted using the feedback signal. Second, a line of sight best estimate block is designed to generate a control error signal in the feedback loop for adaptively updating the filter coefficients. Finally, the performance of the proposed algorithm is verified by analyzing the performance of KBLMS, Recursive Least Squares (RLSs) and Least Mean Squares (LMS) algorithms through measured data and experimental simulation. The results show that KBLMS can converge quickly in multipath channels compared with RLS and LMS, and the code tracking error and carrier tracking error are reduced by 0.1chip, 0.2 cycle and final residual error is reduced by 0.015 compared with the RLS algorithm, which is 0.035 lower than the LMS algorithm, which shows that the KBLMS algorithm can make more accurate estimation.

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“…The iterative formula of LMS algorithm is as follows 20 :

w_{N} false(k + 1 false) = w_{N} false(k false) + μ x_{N} false(k false) e_{N}^{*} false(k false)

where the negative sign represents the iteration to the gradient negative square. The iterative formula with variable step size can be written as follows 29 :

w_{N} false(k + 1 false) = w_{N} false(k false) + μ false(k false) x_{N} false(k false) e_{N}^{*} false(k false)

…”

Section: Methodsmentioning

confidence: 99%

r false(t false) = {false\sum}_{k = 1}^{M} A_{k} s false(t - τ_{k} false) e^{j false(ϕ_{k} false)} + n false(t false)

y false(τ false) = {false\sum}_{k = 1}^{M} a_{k} g false(τ - τ_{k} false) + ω false(τ false)

where

a_{k} = A_{k} e^{j ϕ_{k}}

is the complex path coefficient.…”

Section: Methodsmentioning

confidence: 99%

Channel compensation multipath mitigation technique for Kalman‐Based Least Mean Square based on Kalman estimation

Wang

Zhao

et al. 2020

Concurrency and Computation

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show abstract

“…We require that the power of the signal be minimal and not equal to 0 [13], i.e., minbold-italicw bold-italicwHbold-italicRxboldws.t. bold-italicwHbolds=1 where bold-italicRx=Efalse[boldXbold-italicXHfalse], bold-italics is a constraint vector. From Equation (12), we construct the Lagrangian function [14,15] and get boldw=bold-italicRx−1boldsbold-italicsHbold-italicRx−1bolds…”

Section: Dynamic Model and Signal Modelmentioning

confidence: 99%

An Anti-Jamming Null-Steering Control Technique Based on Double Projection in Dynamic Scenes for GNSS Receivers

Wang

Chang

2019

Sensors

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When a global navigation satellite system (GNSS) receiver suppresses interference in a dynamic scene, the direction of the interference signal arriving at the receiver may change rapidly. The null formed by the spatial filtering based on the array antenna will become wider and shallower, and the anti-jamming performance will deteriorate. A null-steering control technique based on a dual projection algorithm is proposed in this paper, which can effectively increase the depth of the null. In this paper, the dynamic model between the interference and the receiver is established first. Based on the model, the rate of change of the arrival direction of the interference is analyzed, and the phenomenon of the spatial filtering null becoming wider and shallower is simulated and verified. Then, the double projection algorithm is introduced to effectively deepen the null. The simulation results show that the proposed method can effectively increase the null depth by 30 to 50 dB, which significantly improves the anti-jamming performance of the spatial filtering in dynamic scenes.

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“…For military or core civilian areas, anti-jamming performance is an important indicator to evaluate the navigation receivers [7,8]. The interference cancellation technology with antenna array is a common technology for a user terminal because it not only can suppress narrowband interference, but can also suppress wideband interference [9][10][11][12]. The navigation in the transmission process would encounter dense forests or urban canyons, but the interference transmission would encounter the same situation [13].…”

Section: Introductionmentioning

confidence: 99%

Suppression of Jammer Multipath in GNSS Antenna Array Receiver

Huang

Xiao

et al. 2022

Remote Sensing

Self Cite

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Interference multipath is an important factor to affect the anti-jamming performance for the global navigation satellite system (GNSS) antenna array receiver. However, interference multipath must be considered in practical application. In this paper, the antenna array model for interference multipath is analyzed, and an equivalent model for interference multipath is proposed. According to the equivalent interference multipath model, the influence of interference multipath on anti-jamming performance is analyzed from the space only processing (SOP) and space-time adaptive processing (STAP). Interference multipath can cause loss of the degree of freedom (DoF) of SOP. Through analysis of the equivalent model and STAP mechanism, it further reveals how the STAP can solve the interference multipath. The simulation experiments prove that the equivalent model is effective, and the analysis conclusion is correct. This paper also points out that the interference bandwidth is wider and more taps in STAP are required, under the same experiment conditions.

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Blind adaptive channel mismatch equalisation method for GNSS antenna arrays

Cited by 21 publications

References 13 publications

Channel compensation multipath mitigation technique for Kalman‐Based Least Mean Square based on Kalman estimation

Channel compensation multipath mitigation technique for Kalman‐Based Least Mean Square based on Kalman estimation

An Anti-Jamming Null-Steering Control Technique Based on Double Projection in Dynamic Scenes for GNSS Receivers

Suppression of Jammer Multipath in GNSS Antenna Array Receiver

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