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Information-sequence-based adaptive fading extended kalman particle filter (AFEKPF) doppler frequency shift estimation method

An extended Kalman and Doppler frequency shift technology, which is applied to baseband system components, shaping networks in transmitters/receivers, etc., can solve problems such as cumbersome calculation process, complex formula expression, and easy to change over time

Inactive Publication Date: 2016-06-22
DALIAN UNIV
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Problems solved by technology

In the Doppler frequency shift estimation method, the formula expression is complex and the calculation process is cumbersome, and the nonlinear function to be estimated is often processed by local linearization. For the Doppler frequency shift estimation system under high dynamics, the system cannot be accurately modeled or The problem that the model error is prone to change over time

Method used

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  • Information-sequence-based adaptive fading extended kalman particle filter (AFEKPF) doppler frequency shift estimation method
  • Information-sequence-based adaptive fading extended kalman particle filter (AFEKPF) doppler frequency shift estimation method
  • Information-sequence-based adaptive fading extended kalman particle filter (AFEKPF) doppler frequency shift estimation method

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Embodiment

[0149]

[0150] (1) First filter initialization. Initialize k=0, sample That is, according to p(X 0 ) distribution sampling to get Execute (2).

[0151] (2) The filtering process is as follows:

[0152] Step1: Use AFEKF for particle update.

[0153] Step2: Weight calculation: sampling Calculate weights The weights are normalized to get:

[0154] Step3: Resampling: if Then perform resampling, the original weighted sample Mapping to equal weight samples

[0155] Step4: Output state estimation: let k=k+1, return to Step1 for recursive calculation.

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Abstract

The invention discloses an information-sequence-based adaptive fading extended kalman particle filter (AFEKPF) doppler frequency shift estimation method. The method mainly comprises the steps of: firstly, receiving signal parameters by means of an antenna receiver, and defining a sampled signal by adopting a vector table method; secondly, regarding a carrier phase of the sampled signal as a state variable of a particle filter equation, and carrying out dynamic unfolding to obtain a state transition matrix of a system and a covariance matrix of a noise interference vector; estimating a state vector by using adaptive fading kalman with fading particles to obtain optimal filter estimation; and generating an importance density function through AFEKPF, updating posteriori distribution continuously through gaussian approximation to achieve recursive estimation, and completing AFEKPF (adaptive fading extended kalman particle filter).

Description

technical field [0001] The invention relates to a Doppler frequency shift estimation algorithm, in particular to a Doppler frequency shift estimation method based on an adaptive fading extended Kalman particle filter based on an information sequence. Parts of transmission systems not included in a single group H04B3 / 00 to H04B13 / 00; parts of transmission systems not distinguished by the transmission medium used H04B1 / 69 Spread spectrum technique H04B1 / 707 utilizes direct sequence modulation. Background technique [0002] At present, many scholars have proposed a variety of estimation algorithms for Doppler frequency shift, but these methods are difficult to apply to Doppler frequency shift estimation in high dynamic environment. Doppler frequency shift estimation is studied in a highly dynamic environment to provide a basis for Doppler frequency shift compensation, which can effectively improve the confidentiality of the communication system, reduce the bit error rate, and ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L25/02H04L25/03
Inventor 陈波吴旭杜秀丽邱少明
Owner DALIAN UNIV
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