CN1235429C - Method for estimating position - Google Patents

Abstract

The present invention discloses a method for estimating positions, which comprises: the time difference of arrival (TDOA) between a main base station and two adjacent base stations referring to a measuring and positioning request is firstly measured; the zero mean value correction of the errors of the non light of sight (NLOS) in the TDOA measurement quantity is carried out by making use of the NLOS error mean value calculated by the error distribution parameters of NLOS. Accordingly, the time of arrival (TOA) from a mobile station to the main base station is estimated, and TOA from the mobile station to the adjacent base stations is calculated. The zero mean value correction of the NLOS errors in TOA from the mobile station to the main base station and from the mobile station to the adjacent base stations is carried out by making use of the NLOS error mean value. The self-adaptive adjustment of the weighting matrix in the TOA position estimation is carried out by making use of NLOS error variance; the weighting least square estimation is carried out by making use of the adjusted weighting matrix, and the positional estimate value of the mobile station is obtained. The present invention can greatly suppress the NLOS errors and the GDOP influence, and the position estimating accuracy is obviously increased.

Description

A kind of location estimation method

Technical field

The present invention relates to the location technology in the wireless communication field, be specifically related to a kind of location estimation method mobile position estimation.

Background technology

In wireless communication field, the cellular mobile station location is made up of measurement and two basic links of location estimation.In measurement links, two kinds of basic measuring amount are arranged, a kind of is poor (TDOA) the measuring amount time of advent, a kind of is (TOA) the measuring amount time of advent; In the location estimation link, two kinds of basic location estimation methods are arranged, a kind of is the TDOA location estimation method, a kind of is the TOA location estimation method.Angle analysis from measuring amount, though these two kinds of measuring amount of TDOA and TOA all will be subjected to the influence of non line of sight (NLOS) error, but, usually the TOA measuring amount is to utilize the two-way time of system (RTT) measurement function to obtain, in the TOA measuring amount except comprising the NLOS error, some errors of introducing in the RTT measuring process have also been comprised, for example the radio-frequency channel time delay error of base station and travelling carriage, travelling carriage transmit after receive time delay error etc., these errors will reduce the accuracy of TOA measuring amount in hi-Fix.And the TDOA measuring amount does not comprise the error of introducing in the NLOS sum of errors RTT measuring process, and therefore, in mobile position estimation, the TDOA measuring amount is more more desirable than TOA measuring amount.Angle analysis from location estimation, because hyp nonlinear interaction, the TDOA location estimation more is subject to the influence of geometric dilution of precision (GDOP) than TOA location estimation, in other words, the TOA location estimation has suppressed the influence of GDOP with respect to the TDOA location estimation, therefore, the TOA location estimation is more more desirable than TDOA location estimation.

In existing TDOA location estimation method, Y.T. old (Y.T.Chen) divides disclosed " a kind of simple and effective hyperbolic fix position estimator " article (" A simple and efficientestimator for hyperbolic location " in the periodical at the electrical equipment of the 42nd volume of publication on August 8th, 1994 and the signal processing of Electronic Engineering Association's proceedings, IEEE Trans Signal processing, vol.42, no.8, Aug.1994, pp.1905-1915) the Chen Shi algorithm described in is typical case's representative.

In existing TOA location estimation method, " communication journal " the 22nd volume the 3rd interim article " a kind of TOA location algorithm of considering non-line-of-sight propagation influence " of publishing in March calendar year 2001 is typical case's representative, this article is divided into location-estimation algorithm and two kinds of situations of the location-estimation algorithm under the NLOS channel circumstance under sighting distance (LOS) channel circumstance to the discussion of TOA location-estimation algorithm, and LOS environment and the location-estimation algorithm under the NLOS environment with document discussion is called LOS-TOA Chen Shi algorithm and NLOS-TOA relaxed algorithm here.

In existing TOA-TDOA hybrid position algorithm for estimating, " utilize dominant base TOA and carry out the method and apparatus of location estimation with respect to the TDOA of dominant base " patent application is a representative.

In above three kinds of location estimation methods, LOS-TOA Chen Shi algorithm and TOA-TDOA hybrid position algorithm for estimating all are that the thinking of finding the solution of using for reference the Chen Shi algorithm is constructed.The common feature of these two kinds of location estimation methods is: they all adopt twice weighted linear least-squares estimation to be similar to and realize maximal possibility estimation; They have versatility widely, both go for the measuring amount number and equaled the situation of radiation source coordinate number, also be applicable to the situation of measuring amount number, both be applicable to the situation that linear sensor is arranged simultaneously, also be applicable to the situation that transducer is arranged arbitrarily greater than radiation source coordinate number.The common drawback of these two kinds of location estimation methods is: because these existing algorithms all are to construct under the prerequisite that has line of sight between hypothesis radiation source and transducer or the location receiver, under this hypothesis prerequisite, the error signal that is input to location-estimation algorithm only derives from the time delay evaluated error of transducer, and this time delay evaluated error is the zero-mean Gaussian Profile, therefore, these location-estimation algorithm do not possess NLOS error inhibition ability.

In above three kinds of location estimation methods, the basic ideas of NLOS-TOA relaxed algorithm are to introduce slack variable in TOA measures, and adopt the method for search to ask for rational location solution.The essence of this method on how much is the TOA radius of a circle size that comprises the NLOS error according to certain mode adjustment, wishes can eliminate by this method the influence of a part of NLOS error, obtains location estimation value more accurately.The search criteria of this NLOS location estimation method is: with the output of the nearest point in the position that obtains when the LOS algorithm is used for the NLOS channel that searches as the NLOS algorithm.The shortcoming of NLOS-TOA relaxed algorithm is: do not provide a method with definite slack variable of objective basis, therefore be difficult to choose slack variable in actual applications, do not possess practicality.

In " a kind of TDOA/TOA of employing smoothly reaches the positioning accuracy raising method of reconstruct " patent application, provide a kind of TOA of comprehensive utilization and TDOA measuring amount and had the location estimation method that the NLOS error suppresses ability, the characteristics of this method are: 1) travelling carriage that obtains by rough location estimation and base station apart from d, utilize the time delay extended by tau _RmsIntermediate value T at a km place ₁(empirical value) and formula τ _Rms=T ₁d ^εξ obtains the time delay extended by tau that power time delay distributes _Rms, in formula, ζ is the stochastic variable of a logarithm normal distribution; ε is the exponential factor of a value between 0.5～1.The time delay extended by tau _RmsDistributed constant as the NLOS error.2) utilize the estimation of smoothly carrying out NLOS identification and NLOS error variance of TOA measured value.

But this method also exists certain shortcoming, and that is exactly: 1) time delay extended by tau _RmsAnd getting in touch in logic not between the distributed constant of NLOS error.2) formula τ _Rms=T ₁d ^εξ is an abstract relation that is used for Channel Modeling of coming out, the time delay extended by tau that calculates thus _Rmsτ with the physical location of travelling carriage _RmsBetween also be difficult to coincide.3) utilize the TOA measured value smoothly carry out NLOS identification and NLOS error variance estimation approach does not possess real-time, and poor accuracy.In view of above shortcoming, this method is difficult to effectively be used in actual measurement with in calculating.

In a word, in existing cellular mobile station location technology, lack the location-estimation algorithm that has NLOS error rejection, GDOP rejection and practicality simultaneously at present.

Summary of the invention

In view of this, the objective of the invention is to propose a kind of location estimation method that can possess NLOS error rejection, GDOP rejection and practicality simultaneously.

The objective of the invention is to be achieved by the following technical solutions:

A kind of location estimation method, the time of advent poor (TDOA) between the dominant base that first measurement and positioning request relates to and two the adjacent base stations, then:

A. utilizing the NLOS error mean of being obtained by non line of sight (NLOS) error profile parameter that the NLOS error in the TDOA measuring amount is carried out zero-mean corrects, and estimate the time of advent (TOA) of travelling carriage to dominant base according to the TDOA measuring amount after correcting, utilize travelling carriage that travelling carriage obtains to the TOA and the systematic survey of dominant base to arriving the TOA of adjacent base station without the TDOA calculating travelling carriage of correcting between dominant base and the adjacent base station then;

B. utilize the NLOS error mean of obtaining by NLOS error profile parameter that travelling carriage is carried out the zero-mean rectification to dominant base and travelling carriage to the NLOS error among the TOA of adjacent base station;

C. utilize the NLOS error variance of obtaining by NLOS error profile parameter that the weighting matrix in the TOA location estimation is carried out the self adaptation adjustment, and the position of travelling carriage is weighted least-squares estimation with adjusted weighting matrix, obtain the estimated value of location of mobile station.

In above-mentioned location estimation method, can repeating step a to step c, obtain averaging greater than 1 location of mobile station estimated value and to them, obtain final location of mobile station estimated value.

In above-mentioned location estimation method, step a may further include:

A1. determine to carry out the TDOA measuring amount that zero-mean is corrected by NLOS identification, and utilize NLOS error profile parameter to determine the average of NLOS error in the TDOA measuring amount;

A2. the NLOS error mean of determining according to step al carries out the zero-mean rectification to the NLOS error in the TDOA measuring amount;

A3. the TDOA measuring amount of correcting the back gained according to step a2 is calculated the TOA of travelling carriage to dominant base, and the travelling carriage that obtains to the TOA value and the systematic survey of dominant base according to the travelling carriage that calculates is to arriving the TOA of each adjacent base station without the TDOA calculating travelling carriage of rectification between dominant base and the adjacent base station again.

In above-mentioned location estimation method, in step a3, may further include following steps: after calculating the TOA of dominant base, carry out again, calculate the TOA value of each adjacent base station greater than the mean value of 1 TOA value and the travelling carriage that systematic survey obtains to the TDOA between dominant base and the adjacent base station without rectification according to what obtain greater than 1 time calculating.

In above-mentioned location estimation method, step b may further include:

B1. determine to obtain by NLOS identification by step a3, need carry out the dominant base that zero-mean corrects and the TOA value of adjacent base station, and utilize NLOS error profile parameter to determine the average of NLOS error in the TOA value of the dominant base obtained by step a3, need carry out the zero-mean rectification and adjacent base station;

B2. the average of the NLOS error in described that obtained by step a3, the TOA value that need carry out dominant base that zero-mean corrects and adjacent base station is carried out the zero-mean rectification.

In above-mentioned location estimation method, NLOS identification can utilize the power time delay of obtaining from radiation source to select the size of the sample coefficient of dispersion of one group of most powerful path to realize distributing, and also can utilize between the footpath of the power time delay of obtaining from radiation source on distributing between difference power or footpath amplitude difference to realize.

In above-mentioned location estimation method, step c may further include:

C1. the covariance sum that is set to the rectification residual error of TOA time delay estimation error covariance and TOA under sighting distance (LOS) environment by the leading diagonal element is determined the weighting matrix form;

Each covariance value of rectification residual error that comprises the pairing TOA of channel of NLOS error of channel that c2. will be used for calculating TOA is made as zero;

C3. measure to estimate by under the LOS channel circumstance system being carried out TOA, perhaps by system emulation, perhaps TOA time delay estimation error covariance under the LOS environment that needs to adjust in the weighting matrix of the statistics determining step c1 by the TDOA measure error; If the NLOS error that TOA comprises is a discrete form, the TOA that needs in the weighting matrix of the probability density function determining step c1 by the discrete shape NLOS error among NLOS error profile parameter and the TOA to adjust corrects the covariance of residual error; If the NLOS error that TOA comprises is conitnuous forms, obtain the probability density function of the continuous shape NLOS error of TDOA by NLOS error profile parameter, the probability density function of the continuous shape NLOS error by TDOA needing in the weighting matrix of probability density function determining step c1 of continuous shape NLOS error of TOA to obtain the TOA that adjusts to correct the covariance of residual error then.

In above-mentioned location estimation method, can or exist LOS channel criterion to determine the TDOA measuring amount by the GDOP minimum criteria among the step a.

By technical scheme of the present invention as can be seen, in carrying out the TOA position estimation procedure, correct by the NLOS error in the TDOA measuring amount being carried out zero-mean, the NLOS error among the TOA is carried out zero-mean correct and utilize the NLOS error variance that the weighting matrix of TOA is carried out the adaptivity adjustment, and find the solution step such as TOA location estimation value according to this weighting matrix, greatly suppressed of the precision influence of NLOS error to the TOA estimated value.Simultaneously, because the present invention's employing is the TOA location estimation method, therefore can suppress the influence of GDOP effectively.Each step by technical solution of the present invention it can also be seen that each step of the present invention implements all very easy, has good practicability in actual applications.

Description of drawings

Fig. 1 is the location estimation method flow chart that the present invention suppresses NLOS sum of errors GDOP influence;

Fig. 2 is a NLOS error zero-mean antidote flow chart in the TDOA measuring amount of the present invention;

Fig. 3 is a NLOS error zero-mean antidote flow chart in the TOA value that calculates of the present invention;

Fig. 4 is the method flow diagram that the present invention adjusts weighting matrix adaptively.

Embodiment

Below in conjunction with the drawings and specific embodiments the present invention is further detailed.

The present invention provides and a kind of the TDOA measuring amount is converted to the TOA value, utilizes the TOA value to carry out the method for location estimation then.Specifically, at first utilize from dominant base and close on two TDOA measuring amount that any two adjacent base stations of dominant base obtain and obtain the TOA of dominant base to travelling carriage ₁Utilize TOA then ₁And TOA _i=TDOA _{I, 1}+ TOA ₁The TDOA that moving table measuring is obtained _{I, 1}Measuring amount is converted to TOA _i, TDOA wherein _{I, 1}Expression except that dominant base i base station and the TDOA value between the dominant base, i=2,3 ..., V, V are the number of the base station that can measure; Utilize TOA at last _iPosition with the TOA location-estimation algorithm estimation travelling carriage of constructing.

NLOS error in the TDOA measuring amount had both reduced the TOA that asks for from the TDOA measuring amount ₁Accuracy, also reduce the precision of follow-up location estimation based on the TOA value, in order to suppress the influence of NLOS error, the basic ideas that the present invention adopts are: under the prerequisite of the average of obtaining the NLOS error and variance, the average of using the NLOS error is corrected stochastic variable as zero-mean to non-negative NLOS error, use the variance structure weighted least-squares of the NLOS error weighting matrix in estimating tentatively to suppress the NLOS error then to the position estimation effect, last again according to the zero mean characteristic of the NLOS error after correcting, by multiple averaging, further suppress the NLOS error to the position estimated result.

Below with reference to Fig. 1 basic ideas of the present invention are elaborated.

Fig. 1 is the location estimation method flow chart that the present invention suppresses NLOS sum of errors GDOP influence, and as seen from Figure 1, method of the present invention is made up of following six basic steps:

Step 101 utilizes the average of the NLOS error of being obtained by NLOS error profile parameter that the NLOS error that comprises in the TDOA measuring amount is carried out the zero-mean rectification.At first from two or more TDOA measuring amount, according to certain criterion, for example GDOP minimum criteria or have LOS channel criterion is picked out two TDOA measuring amount and corresponding base station and pseudo noise code, and the zero-mean that these two TDOA measuring amount are carried out the NLOS error is corrected then.

Wherein, NLOS error in the TDOA measuring amount is carried out the method that zero-mean is corrected, as shown in Figure 2, specifically forms by substep 201,202,203:

The substep 201 of step 101 is determined to carry out the TDOA measuring amount that zero-mean is corrected by NLOS identification.At first identify the TDOA measuring amount TDOA that comprises the NLOS error _{(m) i, j}, TDOA _{(m) i, j}The original TDOA measuring amount that expression is corrected through the zero-mean of NLOS error, subscript i, j represent that this TDOA measuring amount is poor such as the time of advent between i radiation source of the base station in the cellular network and j the radiation source; ${\mathrm{TDOA}}_{\left(m\right)i,j}={\mathrm{TDOA}}_{\left(\mathrm{los}\right)i,j}^{\left(0\right)}+n_{\left(n\right)i,j}+{\mathrm{\μ}}_{\left(n\right)i,j}+n_{i,j}.$ In following formula, TDOA _{(los) i, j} ⁽⁰⁾It is the desirable TDOA value that does not comprise any error; n _{(n) i, j}Be the residual error of the NLOS error after correcting through zero-mean, its average is zero; μ _{(n) i, j}It is the average of the NLOS error that goes out according to NLOS error profile calculation of parameter; n _{I, j}Be the TDOA time delay evaluated error of system under the LOS environment, do not consider between each radiation source clock drift to the influence of TDOA measuring amount at this, so it be the stochastic variable of zero-mean normal distribution.Judge TDOA _{(m) i, j}The method that whether comprises the NLOS error is respectively i radiation source and j radiation source to be carried out NLOS identification, the recognition methods here has varied, can select a kind of therein arbitrarily, the size that for example can utilize one group of power time delay obtaining from this radiation source to pick out the sample coefficient of dispersion of one group of most powerful path distributing realizes NLOS identification, also can utilize between the footpath of the single or multiple power time delay of obtaining from this radiation source on distributing difference power or amplitude difference to realize NLOS identification.

The substep 202 of step 101 is determined the average of NLOS error in the TDOA measuring amount.According to the NLOS recognition result of step 201, at first from distributing, one group of power time delay of the channel of the TDOA measuring amount correspondence that comprises the NLOS error obtains the distributed constant of NLOS error.During specific implementation, both can adopt following formula (1), and also can adopt formula (2) to finish NLOS error profile parameter p _iAnd p _jEstimation.

$p_i=\frac{(m_1+m_2+\·\·\·+m_N)\×a}{W\×N}---\left(1\right)$

In formula (1), p _iIt is NLOS error profile parameter; m _kBe k (k ∈ 1,2 ..., the N) number in detected footpath in the individual scattering object statistic window, the scattering object statistic window here are from the intercepting that distributes of k power time delay, its starting point can be first directly afterwards certain position, also can comprise first path position; W is the width of scattering object statistic window, and unit is a chip, and the value of W is usually between 1～10 chip; N is for obtaining a p _iThe number that distributes of the power time delay that estimated value adopted, 1～10 between, regular hour interval in carry out N Multipath searching obtain usually by used N power time delay distribution for the value of N; A is the sampling number that carries out in the chip, number of samples that the footpath comprises just, and its span is generally 1～32.

$p_i=\frac{s_1+s_2+\·\·\·{+s}_N}{W\×N}---\left(2\right)$

In formula (2), p _iBe NLOS error profile parameter; s _kBe k (k ∈ 1,2 ..., N) the detected number that surpasses the sampling point of detection threshold in the individual scattering object statistic window, the scattering object statistic window here is to distribute from k power time delay to intercept, and its starting point can be certain position after the first footpath, also can comprise first path position; W is the width of scattering object statistic window, and unit is a sampling point, and within 40 sampling points, representative value is 20 sampling points to the value of W usually; N is for obtaining a p _iThe number that distributes of the power time delay that estimated value adopted, 1～10 between, regular hour interval in carry out N Multipath searching obtain usually by used N power time delay distribution for the value of N.

After the distributed constant that obtains the NLOS error, utilize NLOS error profile parameter p _iAnd p _jAnd the distribution form of NLOS error, calculate the average of NLOS error.

The average of the NLOS error of discrete form can be utilized p _i, p _jAnd the NLOS error delta of the TDOA of the discrete form of formula (3) expression _{(s) i, j}Probability density function directly obtain.

$f_{{\mathrm{\&delta;}}_{\left(s\right)i,j}}\left({\mathrm{\&delta;}}_{\left(s\right)i,j}\right)=\left\{\begin{array}{cc}{p}_i{p}_j\frac{(1-p_i)(1-p_j)}{1-(1-p_i)(1-p_j)};{\mathrm{\&delta;}}_{\left(s\right)i,j}=0& \left(3a\right)\\ p_i{p}_j\frac{(1-p_i)(1-p_j)}{1-(1-p_i)(1-p_j)}(1-p_j{)}^{{\mathrm{\&delta;}}_{\left(s\right)i,j}};{\mathrm{\&delta;}}_{\left(s\right)i,j}>0& \left(3b\right)\\ p_i{p}_j\frac{(1-p_i)(1-p_j)}{1-(1-p_i)(1-p_j)}{(1-p_i)}^{{\mathrm{\&delta;}}_{\left(s\right)i,j}};{\mathrm{\&delta;}}_{\left(s\right)i,j}<0& \left(3c\right)\end{array}\right.$

In formula (3), δ _{(s) i, j}Be i and j base station correspondence be the NLOS margin of error of the TDOA of unit with the sampling point number, unit is the sampling point number, δ _{(s) i, j}∈ (...-3 ,-2 ,-1,0,1,2,3...), this number of samples and sampling point product at interval is exactly the NLOS error, and its dimension is the time; p _iAnd p _jBe respectively the NLOS margin of error δ of the TOA measurement of i and j base station _{(s) i}, δ _{(s) j}Distributed constant; δ _{(s) i, j}=δ _{(s) i}-δ _{(s) j}

The average of the NLOS error of conitnuous forms can be utilized p _i, p _jAnd formula (4) is obtained the distributed constant θ of the NLOS error in the TDOA measuring amount of conitnuous forms _iAnd θ _j, utilize θ then _i, θ _jAnd the NLOS error delta of the TDOA of the conitnuous forms of formula (5) expression _{I, j}Probability density function obtain the average of NLOS error.

${\mathrm{\&theta;}}_i=T\frac{-1}{\ln (1-p_i)}---\left(4\right)$

In formula (4), T is systematic sampling sampling point blanking time, and unit is a microsecond.

$f_{{\mathrm{\&delta;}}_{i,j}}\left({\mathrm{\&delta;}}_{i,j}\right)=\left\{\begin{array}{cc}\frac{1}{{\mathrm{\&theta;}}_i+{\mathrm{\&theta;}}_j}{e}^{\frac{{\mathrm{\&delta;}}_{i,j}}{{\mathrm{\&theta;}}_j}};{\mathrm{\&delta;}}_{i,j}<0& \left(5a\right)\\ \frac{1}{{\mathrm{\&theta;}}_i+{\mathrm{\&theta;}}_j};{\mathrm{\&delta;}}_{i,j}=0& \left(5b\right)\\ \frac{1}{{\mathrm{\&theta;}}_i+{\mathrm{\&theta;}}_j}{e}^{-\frac{{\mathrm{\&delta;}}_{i,j}}{{\mathrm{\&theta;}}_i}};{\mathrm{\&delta;}}_{i,j}>0& \left(5c\right)\end{array}\right.$

In formula (5), θ _iAnd θ _jBe exactly δ _{I, j}Distributed constant.

The substep 203 of step 101 utilizes the average μ of the NLOS error that step 202 obtains _{(n) i, j}, carry out the zero-mean of NLOS error according to formula (6) and correct.

${\mathrm{TDOA}}_{i,j}^{(\mathrm{nlos}\_\mathrm{miti})}={\mathrm{TDOA}}_{\left(m\right)i,j}-{\mathrm{\&mu;}}_{\left(n\right)i,j}={\mathrm{TDOA}}_{\left(\mathrm{los}\right)i,j}^{\left(0\right)}+n_{\left(s\right)i,j}+n_{i,j}---\left(6\right)$

In formula (6), TDOA _{I, j} ^(nlos_miti)The TDOA measuring amount after NLOS error zero-mean is corrected is carried out in expression.

Step 102 is according to the TOA of the TDOA measuring amount calculating dominant base after the zero-mean rectification ₁TOA with adjacent base station _i

At first, calculate the TOA of dominant base ₁Its specific implementation method is: the TDOA that obtains from formula (6) _2,1 ^(nlos_miti)And TDOA _3,1 ^(nlos_miti)Multiply by the r that replaces behind the light velocity in the formula (7) _2,1And r _3,1, just obtaining location of mobile station coordinate x, y comprises intermediate variable r ₁Representation.

$\left[\begin{array}{c}x\\ y\end{array}\right]=-{\left[\begin{array}{cc}{x}_{\mathrm{2,1}}& y_{\mathrm{2,1}}\\ x_{\mathrm{3,1}}& y_{\mathrm{3,1}}\end{array}\right]}^{-1}\&times;\{\left[\begin{array}{c}{r}_{2,1}\\ r_{\mathrm{3,1}}\end{array}\right]r_1+\frac{1}{2}\left[\begin{array}{c}{r_{\mathrm{2,1}}}^2-K_2+K_1\\ {r_{\mathrm{3,1}}}^2-K_3+K_1\end{array}\right]\}---\left(7\right)$

In formula (7), $K_i=x_i^2+y_i^2(i=\mathrm{1,2,3}),$ (x _i, y _i) be the position coordinates of base station.

Then with the result of formula (7), i.e. x, y comprises intermediate variable r ₁The formula (8) of value when bringing i=1 into, obtain r ₁Quadratic form, r ₁Separate on the occasion of being exactly travelling carriage to the distance of reference base station, the reference base station here is numbered No. 1, divided by the light velocity, is exactly the TOA of requirement with this distance ₁Under very special situation, r ₁Separate and have two positive roots, at this moment need to utilize priori and bound fraction measurement data, as in conjunction with RTT measuring amount, the residing sector auxiliary information of travelling carriage, determine correct separating, and abandon another and separate.In order to improve TOA ₁Accuracy, a plurality of TOA that can measure TDOA repeatedly ₁Average.Because TOA ₁Be to use the TDOA measuring amount TDOA after the rectification _{I, 1} ^(nlos_miti)Obtain TOA ₁The average of NLOS error be zero.

$r_i^2={(x_i-x)}^2+{(y_i-y)}^2=K_i-2x_{i}x-2y_{i}y+x^2+y^2,i=\mathrm{1,2},...,M---\left(8\right)$

In formula (8), $K_i=x_i^2+y_i^2,$ (x _i, y _i) be the position coordinates of base station, be known parameter; (x is the location of mobile station coordinate y), is unknown parameter, parameter just to be found the solution.

Calculating the TOA of dominant base ₁Afterwards, calculate the TOA of each adjacent base station _i, the TOA here _iComprise the NLOS error, the specific implementation method is to calculate according to formula (9).

TOA _i＝-TDOA _(m)i，1+TOA ₁ (9)

In formula (9), TDOA _{(m) i, 1}Represent the TDOA measuring amount that comprises the NLOS error between i base station and the 1st base station.

Step 103 utilizes the average of the NLOS error of being obtained by NLOS error profile parameter that the NLOS error that comprises in the TOA measuring amount is carried out the zero-mean rectification.

Wherein, the NLOS error in the TOA measuring amount is carried out the process that zero-mean is corrected, as shown in Figure 3, forms by substep 301,302,303:

The substep 301 of step 103 is determined to carry out the TOA measuring amount that zero-mean is corrected by NLOS identification.Identify the TOA that comprises the NLOS error _i, subscript i represents this TOA _iBe such as i radiation source of the base station in the cellular network and the time of advent or the pseudorange between the receiver; ${\mathrm{TOA}}_i={\mathrm{TOA}}_{\left(\mathrm{los}\right)i}^{\left(0\right)}+n_{\left(n\right)i}+{\mathrm{\&mu;}}_{\left(n\right)i}+n_j.$ Wherein, TOA _{(los) i} ⁽⁰⁾It is the TOA value that does not comprise the perfect condition of any error; n _{(n) i}Be the residual error of the NLOS error after correcting through zero-mean, its average is zero; μ _{(n) i}It is the average of the NLOS error that goes out according to NLOS error profile calculation of parameter; n _iBeing the TOA time delay evaluated error of system under the LOS environment, is the stochastic variable of zero-mean normal distribution.Judge TOA _iThe method that whether comprises the NLOS error is that i radiation source carried out NLOS identification, the recognition methods here is a known technology, can in identification methods, select a kind of arbitrarily, the size that for example can utilize one group of power time delay obtaining from this radiation source to pick out the sample coefficient of dispersion of one group of most powerful path distributing realizes NLOS identification, also can utilize between the footpath of the single or multiple power time delay of obtaining from i radiation source on distributing difference power or amplitude difference to realize that NLOS discerns.

The substep 302 of step 103 carries out TOA _iThe estimation of the average of middle NLOS error.

At first, according to the NLOS recognition result of step 301, from comprising the TOA of NLOS error _iObtain the distributed constant of NLOS error during one group of power time delay of corresponding channel distributes, during specific implementation, both can adopt formula (1), also can adopt formula (2) to finish NLOS error profile parameter p _iEstimation.

Then, utilize NLOS error profile parameter p _iAnd TOA _iIn the distribution form of NLOS error, calculate the average of NLOS error.

The TOA of discrete form _iMiddle NLOS error is a geometric distributions, and its average can be utilized p _iAnd the TOA of the discrete form of formula (10) expression _iMiddle NLOS error delta _{(s) i}Probability density function directly obtain.

$f_{\mathrm{\&delta;}}\left({\mathrm{\&delta;}}_{\left(s\right)i}\right)=\left\{\begin{array}{cc}{p}_i{(1-p_i)}^{{\mathrm{\&delta;}}_{\left(s\right)i}},& {\mathrm{\&delta;}}_{\left(s\right)i}\&Element;(\mathrm{0,1,2}...)\\ 0,& {\mathrm{\&delta;}}_{\left(s\right)i}\&NotElement;(\mathrm{0,1,2}...)\end{array}\right.---\left(10\right)$

δ in the formula (10) _{(s) i}The number of samples of expression systematic sampling, this number of samples and sampling point product at interval is exactly the NLOS error, and its dimension is the time, p _iIt is the distributed constant of geometric distributions.

The TOA of conitnuous forms _iThe NLOS error obey monolateral exponential distribution, its average can be utilized p _iAnd formula (4), obtain the TOA of conitnuous forms _iIn the distributed constant θ of NLOS error _i, utilize θ then _iAnd the TOA of the conitnuous forms of formula (11) expression _iThe NLOS error delta _iProbability density function obtain the average of NLOS error.

$f_{\mathrm{\&delta;}}\left({\mathrm{\&delta;}}_i\right)=\left\{\begin{array}{cc}\frac{1}{{\mathrm{\&theta;}}_i}{e}^{-\frac{{\mathrm{\&delta;}}_i}{{\mathrm{\&theta;}}_i}},& {\mathrm{\&delta;}}_i>0\\ 0,& {\mathrm{\&delta;}}_i\&le;0\end{array}\right.---\left(11\right)$

δ in the formula (11) _iThe NLOS error of representing continuous value, θ _iIt is distributed constant.

The substep 303 of step 103 utilizes the average μ of the NLOS error that step 302 obtains _{(n) i}, carry out TOA according to formula (12) _iThe zero-mean of middle NLOS error is corrected.

${\mathrm{TOA}}_i^{(\mathrm{nlos}\_\mathrm{miti})}={\mathrm{TOA}}_i-{\mathrm{\&mu;}}_{\left(n\right)i}={\mathrm{TOA}}_{\left(\mathrm{los}\right)i}^{\left(0\right)}+n_{\left(n\right)i}+n_i---\left(12\right)$

In formula (12), TOA _i ^(nlos_miti)The TOA measuring amount after NLOS error zero-mean is corrected is carried out in expression; n _{(n) i}Be TOA _iThe rectification residual error; n _iBe the time delay evaluated error.

Step 104 utilizes the variance of the NLOS error of being obtained by NLOS error profile parameter that the element in the weighting matrix in the weighted least-squares estimation is carried out the self adaptation adjustment.

Wherein, the self adaptation adjustment of weighting matrix further comprises substep 401,402 and 403 as shown in Figure 4:

The substep 401 of step 104 is determined the form of weighting matrix, and the present invention adopts the weighting matrix of formula (13) as the TOA least-squares estimation.

$Q_{\left(r\right)}=Q_{\left(l\right)}+Q_{\left(n\right)}=\left.-\begin{array}{ccccc}{{\mathrm{\&sigma;}}_1}^2& 0& 0& \&CenterDot;& 0\\ 0& {{\mathrm{\&sigma;}}_2}^2& 0& \&CenterDot;& 0\\ 0& 0& {{\mathrm{\&sigma;}}_3}^2& \&CenterDot;& 0\\ \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;\\ 0& 0& 0& \&CenterDot;& {{\mathrm{\&sigma;}}_M}^2\end{array}\right]+\left[\begin{array}{ccccc}{{\mathrm{\&sigma;}}_{\left(n\right)1}}^2& 0& 0& \&CenterDot;& 0\\ 0& {{\mathrm{\&sigma;}}_{\left(n\right)2}}^2& 0& \&CenterDot;& 0\\ 0& 0& {{\mathrm{\&sigma;}}_{\left(n\right)3}}^2& \&CenterDot;& 0\\ \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;\\ 0& 0& 0& \&CenterDot;& {{\mathrm{\&sigma;}}_{\left(n\right)M}}^2\end{array}\right]---\left(13\right)$ (13)

Wherein,

$Q_{\left(l\right)}=\left[\begin{array}{ccccc}{{\mathrm{\&sigma;}}_1}^2& 0& 0& \&CenterDot;& 0\\ 0& {{\mathrm{\&sigma;}}_2}^2& 0& \&CenterDot;& 0\\ 0& 0& {{\mathrm{\&sigma;}}_3}^2& \&CenterDot;& 0\\ \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;\\ 0& 0& 0& \&CenterDot;& {{\mathrm{\&sigma;}}_M}^2\end{array}\right]---\left(14\right)$

Q _(l)Time delay evaluated error N=[n for TOA under the LOS environment ₁n ₂... n _M] ^TCovariance matrix, N is the M n dimensional vector n, the average of N is zero, the covariance matrix Q of N _(l)For M * M ties up symmetrical matrix.σ _i ²Be TOA time delay estimation error covariance under the LOS environment.

$Q_{\left(n\right)}=\left[\begin{array}{ccccc}{{\mathrm{\&sigma;}}_{\left(n\right)1}}^2& 0& 0& \&CenterDot;& 0\\ 0& {{\mathrm{\&sigma;}}_{\left(n\right)2}}^2& 0& \&CenterDot;& 0\\ 0& 0& {{\mathrm{\&sigma;}}_{\left(n\right)3}}^2& \&CenterDot;& 0\\ \&CenterDot;& 0& 0& \&CenterDot;& {{\mathrm{\&sigma;}}_{\left(n\right)M}}^2\\ 0& & & & \end{array}\right]---\left(15\right)$

Q _(n)Be the NLOS error n after the zero-mean rectification _{(n) i}The M n dimensional vector n N that constitutes _(n)=[n _{(n) 1}n _{(n) 2}... n _{(n) M}] ^TCovariance matrix, N _(n)Average be zero, Q _(n)For M * M ties up symmetrical matrix.

The substep 402 of step 104 is determined the element that needs are adjusted in the weighting matrix according to the NLOS recognition result.This step is directly utilized the result of step 401, as long as constitute TOA _iI the corresponding channel of radiation source comprise the NLOS error, will be to element corresponding in the weighting matrix, promptly weight coefficient is adjusted.For example, through NLOS identification, the channel of determining the 1st radiation source correspondence is the LOS channel, then the Q in the formula (13) _(n)Comprise σ _{(n) 1} ²Element need adjust i.e. σ _{(n) 1} ²Get null value.

The substep 403 of step 104 need in the weighting matrix to determine the value of the element adjusted.

At first, determine matrix Q _(l)In the value of each element, i.e. σ in the formula (14) _i ²Value.Can be similar to by the statistics of under the LOS channel circumstance system being carried out the TOA measure error and obtain σ _i ², also can be similar to and obtain σ by system emulation _i ², can also be similar to by the statistics of TDOA measure error and obtain σ _{I, j} ², utilize then ${\mathrm{\&sigma;}}_i^2=\frac{1}{2}{{\mathrm{\&sigma;}}_{i,j}}^2$ Determine σ _i ²

Then, determine matrix Q _(n)In the value of each element.The p that can utilize step 103 to ask for here _iAnd the TOA of formula (10) expression _iThe NLOS error delta _{(s) i}Probability density function obtain σ _{(n) i} ², the also p that can utilize step 103 to ask for _iAnd formula (4) is obtained θ _i, and then utilize the θ that obtains _iAnd the TOA of formula (11) expression _iMiddle NLOS error delta _iProbability density function obtain σ _{(n) i} ²TOA ₁The variance of NLOS error adopt the distributed constant p that obtains from dominant base ₁Calculate concrete steps and TOA _iThe computational methods of variance identical.

Step 105 is weighted least-squares estimation, obtains one group of estimated value that has tentatively suppressed the location of mobile station of NLOS error effect.

When the TDOA that measures greater than a plurality of base stations of 3, with a TOA who utilizes step 102 to obtain ₁With a plurality of TOA _i ^{(i=2,3 ..., M M 〉=4)}Nonlinear System of Equations of substitution formula (8) structure is by introducing intermediate variable d ₁=(x-x ₁) ²+ (y-y ₁) ²With utilize weighted least-squares to estimate to find the solution this Nonlinear System of Equations, just can obtain travelling carriage position coordinates (x, y).Suppress ability in order to make M have the NLOS error at 4 o'clock more than or equal to the solution procedure of the equation group (8) that obtained, when utilizing weighted least-squares to estimate to find the solution this Nonlinear System of Equations, the Q that determines with step 104 _(r)Replace the estimation error covariance matrix of equation group (8).The location estimation value that obtains has like this suppressed GDOP sum of errors NLOS error preferably.

Step 106 averages by one group of location of mobile station value that step 105 is obtained, and further suppresses the influence of NLOS error to position estimation accuracy.Just, at first, in such as several seconds short time interval, obtain a plurality of location estimation values of on independent TOA based measurement, exporting, then these location estimation values (coordinate components) are averaged, obtain a position coordinates by step 105.

This step utilization is zero characteristics according to the average that formula (12) carries out the NLOS error of the TOA measured value after zero-mean is corrected, average by a plurality of location estimation results that step 105 is obtained, further suppress the NLOS error and correct the influence of residual error, reduced the variance of position estimation error position estimation accuracy.

Above-mentioned just to the detailed description of one embodiment of the present of invention, be not limited to protection scope of the present invention.

Claims (9)

1. location estimation method, the difference TDOA time of advent between the dominant base that first measurement and positioning request relates to and two the adjacent base stations is characterized in that this method further comprises:

A. utilizing the NLOS error mean of being obtained by non line of sight NLOS error profile parameter that the NLOS error in the TDOA measuring amount is carried out zero-mean corrects, and estimate the time of advent TOA of travelling carriage to dominant base according to the TDOA measuring amount after correcting, utilize travelling carriage that described travelling carriage obtains to the TOA and the systematic survey of dominant base to arriving the TOA of adjacent base station without the TDOA calculating travelling carriage of rectification between dominant base and the adjacent base station then;

B. utilize the NLOS error mean of obtaining by NLOS error profile parameter that travelling carriage is carried out the zero-mean rectification to dominant base and travelling carriage to the NLOS error among the TOA of adjacent base station;

C. utilize the NLOS error variance of obtaining by NLOS error profile parameter that the weighting matrix in the TOA location estimation is carried out the self adaptation adjustment, and the position of travelling carriage is weighted least-squares estimation with adjusted weighting matrix, obtain the estimated value of location of mobile station.

2. location estimation method according to claim 1, it is characterized in that repeating said steps a obtains the location of mobile station estimated value greater than 1 to step c, described location of mobile station estimated value greater than 1 is averaged, obtain final location of mobile station estimated value.

3. location estimation method according to claim 1 and 2 is characterized in that step a further comprises:

A1. determine to carry out the TDOA measuring amount that zero-mean is corrected by NLOS identification, and utilize NLOS error profile parameter to determine the average of NLOS error in the TDOA measuring amount;

A2. the NLOS error mean of determining according to step a1 carries out the zero-mean rectification to the NLOS error in the TDOA measuring amount;

A3. the TDOA measuring amount of correcting the back gained according to step a2 is calculated the TOA of travelling carriage to dominant base, and the travelling carriage that obtains to the TOA value and the systematic survey of dominant base according to the travelling carriage that calculates is to arriving the TOA of each adjacent base station without the TDOA calculating travelling carriage of rectification between dominant base and the adjacent base station again.

4. location estimation method according to claim 3, it is characterized in that, in step a3, further comprise the steps: after calculating the TOA of dominant base, to carry out again, calculate the TOA value of each adjacent base station greater than the mean value of 1 TOA value and the travelling carriage that systematic survey obtains to the TDOA between dominant base and the adjacent base station without rectification according to what obtain greater than 1 time calculating.

5. location estimation method according to claim 3 is characterized in that step b further comprises:

B1. determine to obtain by NLOS identification by step a3, need carry out the dominant base that zero-mean corrects and the TOA value of adjacent base station, and utilize NLOS error profile parameter to determine the average of NLOS error in the TOA value of the dominant base obtained by step a3, need carry out the zero-mean rectification and adjacent base station;

B2. the average of the NLOS error in described that obtained by step a3, the TOA value that need carry out dominant base that zero-mean corrects and adjacent base station is carried out the zero-mean rectification.

6. location estimation method according to claim 5 is characterized in that, described NLOS identification utilizes from the power time delay distribution that radiation source obtains selects the size of the sample coefficient of dispersion of one group of most powerful path to realize.

7. location estimation method according to claim 5 is characterized in that, described NLOS identification utilizes between the footpath of the power time delay of obtaining from radiation source on distributing between difference power or footpath amplitude difference to realize.

8. location estimation method according to claim 1 and 2 is characterized in that step c further comprises:

C1. the mode that is set to the covariance sum of the rectification residual error of TOA time delay estimation error covariance and TOA under the sighting distance LOS environment of the leading diagonal element by matrix is determined the weighting matrix form;

Each covariance value of rectification residual error that comprises the pairing TOA of channel of NLOS error of channel that c2. will be used for calculating TOA is made as zero;

C3. measure to estimate by under the LOS channel circumstance system being carried out TOA, or by system emulation, or TOA time delay estimation error covariance under the LOS environment that needs to adjust in the weighting matrix of the mode determining step c1 that adds up by the TDOA measure error; Judge that the NLOS error that TOA comprises is discrete form or conitnuous forms, if discrete form, the TOA that then needs in the weighting matrix of the probability density function determining step c1 by the discrete shape NLOS error among NLOS error profile parameter and the TOA to adjust corrects the covariance of residual error; If be conitnuous forms, then obtain the probability density function of the continuous shape NLOS error of TDOA by NLOS error profile parameter, the probability density function of the continuous shape NLOS error by TDOA needing in the weighting matrix of probability density function determining step c1 of continuous shape NLOS error of TOA to obtain the TOA that adjusts to correct the covariance of residual error then.

9. location estimation method according to claim 1 and 2 is characterized in that, in step a by the GDOP minimum criteria or exist LOS channel criterion to determine the TDOA measuring amount.

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