Method for estimating parameters of a navigation signal

• US 20080154487A1
• Filed: 12/04/2007
• Published: 06/26/2008
• Est. Priority Date: 12/08/2006
• Status: Active Grant

• Current Assignee: Deutsches Zentrum Fã¼R Luft- Und Raumfahrt E.V.
• Original Assignee: Deutsches Zentrum Fã¼R Luft- Und Raumfahrt E.V.
• Inventors: Lentmaier, Michael, Krach, Bernhard
• Granted Patent: US 7,894,983 B2
• Time in Patent Office: Days
• US Class Current: 701/200
• CPC Class Codes: G01S 19/22 Multipath-related issues

Abstract

The method for estimating parameters of a navigation signal received by a receiver which receives navigation signals through a plurality of paths wherein the parameters include data modulated on the navigation signal and complex amplitudes i.e. amplitude and phase shift, and time delays of the individual paths, comprises receiving a navigation signal and sampling the received navigation signal. Moreover, the parameters are sequentially estimated in terms of a posterior probability density function. For facilitating the sequential estimation, the received vector is transformed into a compressed vector without loss of information by using a correlator bank having a plurality of correlator reference signals. The sets of samples representing the posterior probability function of the parameters are sequentially calculated by nonlinearly, recursively filtering the compressed vector and optionally predicting the complex amplitude and time delays by performing equivalent time delay shifts and phase shifts of the correlator signals of the correlator bank.

Claims

1. A method for estimating parameters of a navigation signal received by a receiver which receives navigation signals through a plurality of paths wherein the parameters include data modulated on the navigation signal and complex amplitudes i.e. amplitude and phase shift, and time delays of the individual paths, the method comprising the steps of:

receiving the navigation signal,

sampling the received navigation signal and dividing the samples into blocks of a given length and grouping the samples of each block into a received vector,

sequentially estimating the parameters in terms of a posterior probability density function at a current time based on the navigation signals received at former times and the current time,

wherein the sequential estimation comprises (i) a predicting stage for estimating a prior probability density function of the parameters with respect to the navigation signals received at the former times, wherein the predicting stage is based on the posterior probability density function of the parameters at the former time with respect to the navigation signals received at the former times and a parameter transition model describing the change of parameters between a former time just prior to the current time and the current time, and (ii) an updating stage for estimating the posterior probability density function of the parameters with respect to the navigation signals received at the former times and at the current time, wherein the updating stage is based on the prior probability density function with respect to the navigation signals received at the former times and a likelihood for the assumption that based on the navigation signal currently received the parameters are included in the currently received navigation signal,

transforming the received vector into a compressed vector having less elements than the number of samples of the received vector, wherein the transformation is performed substantially without loss of information using a correlator bank having a plurality of correlator reference signals,

nonlinearly, recursively filtering the compressed vector such that the updating stage for all parameters together with the predicting stage for the data is represented, and

predicting the complex amplitude and time delays by performing equivalent time delay shifts and phase shifts of the correlator signals of the correlator bank.

2. A method for estimating parameters of a navigation signal received by a receiver which receives navigation signals through a plurality of paths wherein the parameters include data modulated on the navigation signal and complex amplitudes i.e. amplitude and phase shift, and time delays of the individual paths, the method comprising:

receiving the navigation signal,

sampling the received navigation signal and dividing the samples into blocks of a given length and grouping the samples of each block into a received vector,

sequentially estimating the parameters in terms of a posterior probability density function at a current time based on the navigation signals received at former times and the current time,

wherein the sequential estimation comprises (i) a predicting stage for estimating a prior probability density function of the parameters with respect to the navigation signals received at the former times, wherein the predicting stage is based on the posterior probability density function of the parameters at the former time with respect to the navigation signals received at the former times and a parameter transition model describing the change of parameters between a former time just prior to the current time and the current time, and (ii) an updating stage for estimating the posterior probability density function of the parameters with respect to the navigation signals received at the former times and at the current time, wherein the updating stage is based on the prior probability density function with respect to the navigation signals received at the former times and a likelihood for the assumption that based on the navigation signal currently received the parameters are included in the currently received navigation signal,

transforming the received vector into a compressed vector having less elements than the number of samples of the received vector, wherein the transformation is performed substantially without loss of information using a correlator bank having a plurality of correlator reference signals, and

nonlinearly, recursively filtering the compressed vector such that the updating stage for all parameters is represented together with the predicting stage for all parameters.