WO2012016355A1

WO2012016355A1 - Method of and system for locating the position of user equipment

Info

Publication number: WO2012016355A1
Application number: PCT/CN2010/001188
Authority: WO
Inventors: Yang Zhang
Original assignee: Telefonakietolaget Lm Ericsson (Publ)
Priority date: 2010-08-05
Filing date: 2010-08-05
Publication date: 2012-02-09
Also published as: CN103270801A; CN103270801B

Abstract

The present invention provides a method and system for locating a position of user equipment in a telecommunication system comprising at least three base stations. The method comprising the step of: performing a first position estimation for the user equipment using a Quadratic Correction Least Square algorithm; constructing initially estimated positions based on a result of the first position estimation/and determining a finally estimated position of the user equipment. The system comprises a first position estimation module adapted to perform a first position estimation for the user equipment using a Quadratic Correction Least Square algorithm and to construct initially estimated positions of the user equipment based on a result of the first position estimation; and a determination module connected to the first position estimation module and adapted to determine a finally estimated position of the user equipment. The method and system provide a better viability in real network, especially in case of poor UE/BS geometry and poor TDOA measurements and gives better robustness and accuracy compare to the legacy solution.

Description

Method of and System for Locating the Position of user equipment

TECHNICAL FIELD

The present invention relates to the field of telecommunication networks. In particular , the present invention relates to a method of and a system for locating the position of user equipment in cellular telecommunication networks.

BACKGROUND

Wireless mobile communication devices continue to shape our world One of the benefits of using some mobile devices is that the location of the mobile device, and hopefully of the user, may be determined or otherwise estimated. For example, locating a user during an emergency would clearly be useful to those seeking to respond to the emergency.

Accordingly, mobile device positioning techniques have been implemented to determine or otherwise estimate the location of a mobile device.

As is known to a person skilled,in the art, time of arrival (TOA) and time-dif ference-of-arrival (TDOA) are widely used techniques for geolocation applications. These two methods play a fundamental role in both satellite-based Systems such as Global Positioning System (GPS) and Galileo, and terrestrial-radio-based systems such as Long Range Navigation (LORAN) and other wireless location systems . Location techniques based on TOAs are usually referred to as circular or spherical for 2-D or 3-D location, respectively, while those based on TDOAs are referred to as hyperbolic or hyperboloid.

Furthermore, as positioning techniques develop, Observed Time Difference of Arrival (OTDOA) has become one of the main positioning techniques in modern cellular telecommunications networks. The UE' s position can be figured out based on the measured parameters below:

1) TDOA measurements of downlink radio signals;

2) Actual relative time difference (RTD) between the transmissions of the base stations (BS) at the time when TDOA measurements were made; and

3) Geographical positions of the BSs that have had their signals measured .

Fig.l shows a scenario adopting OTDOA technique. As shown in Fig.l, in such a scenario, to locate the position of the UE 100, at least 3 BSs 101, 102 and 103, need to be involved since the UE's position can be determined by the intersection of at least two hyperbolas, as seen in Figurel. More TDOA measurement s bring better accuracy.

The accuracy of each of these measurements contributes to the overall accuracy of the position estimate. There are several approaches to determining the RTD. One is to synchronize the transmissions of BSs. In this case RTD are known constant values that may be entered in the database and used by the calculation function when making a position estimate. The synchronization should be done to a level of accuracy of the order of tens of nanoseconds, as 10 nanoseconds uncertainty contributes 3 meters error in the position estimate. Drift and jitter in the synchronization timing should also be well controlled as these also contribute to the uncertainty in the position estimate. Synchronization to this level of accuracy is currently only readily available through satellite based time-transfer techniques. Generally in the TDD operating mode, the BSs are synchroni zed .

Alternatively, BSs may be left to free run within some constraint of maximum frequency error . In thi s scenario , the RTD wi 11 change , in this case slowly, with time. The rate of change will depend on the frequency difference and jitter between BSs.

The OTDOA method may be operated in two modes: UE-assisted OTDOA and UE-based OTDOA. The two modes differ in where the actual position calculation is carried out. In UE-assisted mode, UE measures the TDOA of several cells and send the measurement results to the network, where the location server carries out the position calculation.

In UE-based mode, UE makes the measurements and also carries out the position calculation, and thus requires additional information. For example, the UE may require the position of the measured BSs and timing relation among them for the position calculation in UE-based mode.

OTDOA has already been standardized by 3GPP GERAN, wherein it is named E-OTD, and UTRAN, but not yet deployed in real networks. In E-UTRAN, reference signal for OTDOA has been considered in physical layer signal design, this makes OTDOA even more promising Some American operators have started the planning for LTE-OTDOA deployment which is scheduled in year 2010-2011. Moreover, it is also very clear that the OTDOA related protocol in E-UTRAN will be directly adopted by other standardization bodies e.g. OMA for User Plane positioning. So OTDOA is very likely to ramp up in the near future.

In essence, OTDOA positioning belongs to hyperbolic location, because the measurement is a set of time differences which corresponds to a set of hyperbolas. There have been extensive studies over related solutions/algorithms, among which two methods were most widely adopted and discussed:

Methodl: A combination of Taylor- series linearization and iterative Weighted Least Square (WLS) algorithm, originally proposed by W. H. Foy, Position-location solutions by Taylor-series estimation, IEEE Trans. Aerosp. Electron. Syst., vol. AES-12, pp. 187D194, Mar.1976, hereinafter referred to as the Taylor method.

Method2 : Quadratic Correction Least Square (QCLS), a . k . a . Chan algorithm proposed in "A simple and efficient estimator for hyperbolic location," IEEE Trans. Signal Processing, vol. 42, pp. 1905D1915, Aug.1994. This method is hereinafter referred to as the Chan method.

And the combination, e.g. feeding Methodl with the result of Method2, of the two methods can also be found in publications, such as an improved Taylor algorithm in TDOA subscriber position location, Communication Technology Proceedings , 2003. ICCT 2003 Volume 2, Issue, 9-11 April 2003 Page(s): 981 - 984 vol.2.

The Taylor method can give good and stable estimate even in case of poor TDOA measurement accuracy, e. g. due to low Signal-to Noise Ration (SNR) . But it is an iterative method so it requires an initial guess input as starting point. Sometimes a close to true starting point is needed to avoid local minima.

Chan method is in closed-form and computationally efficient, it can approximate Cramer-Rao Lower Bound (CRLB) in a high SNR case but is unfortunately less accurate than the Taylor method in case of low SNR. Further, when linear arrays are used, this method requires an extra algorithm branch.

Since bothmethods have pros and cons, an appropriate combination of them has attracted some studies. The output of the Chan method can be used as an initial guess input of the Taylor method, in order to achieve a better accuracy and robustness.

The Chan method can normally give very good estimate. This also matches with the simulation results . However, the simulated noise level, e. g. in terms of range, is normally very low, i.e. at a variance of 10^"3 to 10^"5, while the distances between BSs are about 4 to 5. These numbers are unitless, but it can be found that the Root-Mean-Square (RMS) error is only 0.06% to 0.8% of the BS distance. For an average BS distance of 1 km in real network, the corresponding measurement error is then in the range of 0.6~B meters, which is too optimistic to justify the algorithm's viability. Actually the reason for using such small error is possibly to justify that it can approach CRLB in high SNR case.

If the noise variance is increased to 0.03, the RMS error becomes roughly 45 meters, which error level is believed to be reasonable for the embodiment of this invention, taking multi-path and un-ideal timing effect into account. Simulation results in a simple geometry, reasonable since in real networks a UE normally can not hear very many BSs, are shown in Fig.2, with conditions below :

TDOA measurement error: Independent identically distributed Additive white Gaussian noise (AWGN) , converted to range, for each measurement. Variance= 0.03

Number of Runs: 5000

The true UE location 211 is [10, 6] . In Fig. 2 the hollow points 201, 203, 205 and 207 respectively represents the location of BS positions, and the solid point 211 represents the terminal actual position, and the dark area 209 are the Chan method results.

It should be noted that the coordinates and the origin are selected for the convenience of evaluation. The applicant's simulation verifies that there is no loss of generality. It can be found that the Chan method can also provide good estimates in this scenario. However, for some "non-surrounding" topology, the Chan method can not give even a close estimate. An example is shown in Fig3, which illustrates the same BS topology and simulation condition as above, but with different UE position. As illustrated in Fig. 3, points 301, 303, 305 and 307 represent the locations of base stations . Areas 309 and 311 are the estimated positions according to Chan method, whilst the actual position of the user equipment is at point 313.

Unfortunately, such "non-surrounding" cases as above are not negligible since there very likely exist such un-ideai environments in real network deployment , e g hilly area, building shadowing. Moreover, for a concatenated solution, i e a combination of the Chan and Taylor methods, simulation shows that such a bad initial guess input will in most cases lead the Taylor method to divergence, or wrongly converge to a local minimum. Final position estimate will therefore normally be unavailable or with very large error, OTDOA will then fail, e.g. because it can not pass sanity check based on cell-based positioning result. Simulations show that the Chan method gives unreasonable results for scenarios where SNR is very low. Fig3 is an example of such a scenario.

SUMMARY

Therefore, it is one object of embodiments of the present invention to address the above disadvantages by providing a method of and a system for locating a position of user equipment in a telecommunication environment.

According to a first embodiment of the present invention, the object is achieved by a method for locating or determining a position of a user equipment in a telecommunication system comprising at least three base stations, the method comprising the steps of: performing a first position estimation for the user equipment using a Quadratic Correction Least Square algorithm; constructing initially estimated positions based on the result of the first position estimation; and determining a finally estimated position of the user equipment. According to a further embodiment, the step of determining comprises calculating weighted discrepancies for the initially estimated positions, and selecting at least one of the initially estimated positions having a smallest weighted discrepancy as initial guess to perform a second position estimation using a combination of Taylor series linearization and an iterative Weighted Least Square algorithm; or applying all of the initially estimated positions to perform the second position estimation using a combination of Taylor series linearization and the iterative Weighted Least Square algorithm.

According to another embodiment, the step of selecting at least one of the initially estimated positions further comprises: if a difference between a smallest but one weighted discrepancy and the smallest weighted discrepancy is greater than a threshold or if the quotient of the smallest but one weighted discrepancy divided by the smallest weighted discrepancy is greater than another threshold, the initially estimated position corresponding to the smallest weighted discrepancy is selected, otherwise the initially estimated position corresponding to the smallest weighted discrepancy and the initially estimated position corresponding to the smallest but one weighted discrepancy are both selected.

According to yet a further embodiment, the step of determining further comprises: if one of the initially estimated position having the smallest weighted discrepancy is selected as initial guess position to perform the second position estimation, calculating a further weighted discrepancy for a further estimated position calculated from the second position estimation and determining one having the smallest weighted discrepancy from a group consisted of the previously selected position from the initially estimated positions and the further estimated position as the finally estimated position; if more than one of the initially estimated positions are selected as initial guess positions to perform the second position estimation, calculating further weighted discrepancies for further estimated positions calculated from the second position estimation and determining one having the smallest weighted discrepancy from a group consisted of the previously selected positions from the initially estimated positions and the further estimated positions as the finally estimated position.

According to the above described embodiments, the first position estimation is performed by adopting the following equation: z = (G^^"1G) G^T F^''h wherein z is an estimator,

Ψ = c²BQB

wherein Q is covariance matrix of TDOA measurements,

s t e square eng o vec or M is the number of measured base stations, c is the speed of light, and (*> y) are the positions of the i-th base station and terminal respectively, is the distance between the i-th base station and the user equipment, and , ^'·¹ , ^^'·¹ represent ^χ' ^X , -^¹ , r. - r,

and , respectively, i = 1 to . The first position estimation may further be performed by adopt ing the following equation:

wherein zi is a further estimator,

h^ (z - [x_{ , y„0]^T)²

Ψ₁=4Β₁(^β _βψ-^,β _βΓΒ^ _and

x - x_] 0

0 y - y, wherein G₀ is an intermediate matrix that can be obtained by us ing (x> ) i_n place of (^x,y^ in vector ^z , and wherein ^ '-^ is a preliminary estimate of

The initially estimated positions may be constructed with the following forms:

- s rt-z^) Λ wherein z_plc ( k=l , 2 , 3 , 4 ) represents the initially estimated positions, and the sqrt_z,(/) is determined using an equation having the form:

sqrt_z,C/) = V-z,(y) , if z,U) < 0,(/ = 1,2); sqrt_z,(y) =

1,2) , where zi ( j ) are vector elements of the estimator zj .

The step of calculating weighted discrepancies may be performed by using an equation having the form

or

M

Err(k) =∑ wherein:

'·' is the TDOA measurement that represents the time difference between the i-th base station and a first base station;

c is the speed of light;

Q, is the i-th diagonal element in a covariance matrix of TDOA measurements; and

abs (.) is an operator that calculates the absolute value; and '* is the distance between the k-th initially estimated position of the user equipment and the i-th base station.

According to a further embodiment of the invention, the step of determining further comprises: calculating weighted discrepancies for the initially estimated positions; and determining one of the initially estimated position having a smallest weighted discrepancy as the finally estimated position.

The embodiments of the present invention also provide a positioning system adapted to locate or determine a position of a user equipment in a telecommunication system comprising at least three base stations, the system comprising: a first position estimation module adapted to perform the first position estimation for the user equipment using a Quadratic Correction Least Square algorithm and to construct the initially estimated positions of the user equipment based on the result of the first position estimation; and a determination module connected to the first position estimation module and adapted to determine the finally estimated position of the user equipment. The determination module further comprises: a second position estimation module adapted to perform a second position estimation using a combination of Taylor series linearization and an iterative Weighted Least Square algorithm and to calculate or construct the one or more than one further estimated positions based on a result of the second position estimation; a calculation module connected to the first and second position estimation modules and adapted to calculate weighted discrepancies for the initially estimated positions and to calculate further weighted discrepancies for the one or more than one further estimated positions; and a decision unit, connected to the calculation module, the decision unit being configured to determine the one having the smallest weighted discrepancy from a group consisted of the previously selected positions from the initially estimated positions and the further estimated positions as a finally estimated position. The determination module may further comprise a selection module, connected to the calculation module and to the second position estimation module, the select ion module adapted to select at least one of the initially estimated positions ha ing the smallest weighted discrepancy as the initial guess position to perform the second position estimation. The selection module may be configured to select the initially estimated position corresponding to the smallest weighted discrepancy if the difference between the smallest but one weighted discrepancy and the smallest weighted discrepancy is greater than a threshold or if the quotient of the smallest but one weighted discrepancy divided by the smallest weighted discrepancy is greater than another threshold, and to otherwise select both the initially estimated position corresponding to the smallest weighted discrepancy and the initially estimated position corresponding to the smallest but one weighted discrepancy .

The embodiments of the present invention further provide a computer readable medium including logic for locating or determining a position of user equipment. The logic is operable to perform the steps as described in the above described embodiments of the present invention.

According to another embodiment of the invention a network node comprising the system as described above is provided.

According to yet another embodiment of the present invention there is provided an improved Serving Mobile Location Centre (SMLC) comprising the system as described above.

According to the method and system as described above, an improvement over the Chan method is proposed, which can eliminate limitations of the Chan method. A better viability in real networks is therefore provided, especially in cases of poor UE/BS geometry and poor TDOA measurements . Furthermore, a new solution based on "error metric" to combine the Chan and Taylor methods is proposed, and this solution gives better robustness and accuracy compared to conventional solutions. On the other hand, the computational complexity of the proposed solution is not high. Besides, this invention focuses on 2D cases only, but the idea can be easily extended to 3D cases by those skilled in the art .

BRIEF DESCRIPTION OF THE DRAWINGS The above and other aspects, features, and advantages of the present invention will be more apparent from the following more particular description thereof, presented in conjunction with the accompanying drawings, in which:

Fig.l shows an OTDOA- based scenario for locating the position of UE.

Fig.2 shows the simulation results of one example adopting the Chan method.

Fig.3 shows the simulation results of another example with non-surrounding base stations adopting the Chan method. Figs.4A to 4C illustrate simplified positioning system architecture according to the present application.

Figs. 5A to 5C show different positioning methods according to embodiments of the present invention;

Fig.6 shows the simulation results of the Chan method and the simulation results of the embodiment as proposed in Figs. 4A and 5A; and

Fig. 7 shows the simulation results of the embodiment as proposed in Figs. 4A and 5A and the simulation results of a embodiment as proposed in Figs. 4C and 5C. Corresponding reference characters indicate corresponding components throughout the several views of the drawings.

DETAILED DESCRIPTION

The embodiments set forth below represent the necessary information to enable those skilled in the art to practice the inve tion and illustrate the best mode of practicing the invention Upon reading the following description in light of the accompanying drawings, those skilled in the art should understand the concepts of the invention and recognize applications of these concepts not particularly addressed herein.

To address the above mentioned problems, the present invention provides positioning systems adapted to locate or determine the position of user equipment in a telecommunication system comprising at least three base stations, as shown in Fig. 4A to 4C. Furthermore, the present invention also provides methods for locating or determining a position of a user equipment in a telecommunication system comprising at least three base stations, as shown in Fig. 5A to 5C.

As shown in Fig.4A, 4B and 4C, the positioning system comprises a first posit ion est imat ion module 401 , and a determination module 413. The first position estimation module 401 is configured to perform a first position estimation for the user equipment and to construct initially estimated positions of the user equipment . The determination module 413 is configured to determine a finally estimated position of the user equipment.

As is shown in Fig. 4A, the determination module 413 comprises a calculation module 403 that is configured to calculate a respective weighted discrepancy for each of the initially estimated positions obtained from the first position estimation module. The dete rminat ion module 413 further compr i se s a deci s ion unit 409 that is configured to determine the one of the initially estimated positions having the smallest weighted discrepancy as the finally estimated position.

The system in Fig .4A operates as illustrated in Fig . 5A. Firstly, in step 501, the first position estimation module 401 performs the first position estimation. It should be noted that the first position estimation here is similar to the Chan method as mentioned above. The first position estimation of the present invention mainly includes:

a) obtaining TDOA measurements that relate a position (x, y) of a User Equipment (UE) to the position (xi,yi) of a first base station and the positions (xi,y ) of at least two neighboring base stations, wherein the first base station may be the base station that serves the UE;

b) determining a first estimate ( '> of the position of the UE and an estimate of a distance ri from the UE to the first base station using a covariance matrix Q of the TDOA measurements; c) determining estimates of the distances ri, i=2 to M, from the UE to the at least two neighboring base stations using the estimate (^Χ>Μ of the position of the UE and the estimate of the distance ri from the UE to the first base station, where M is the number of measured base stations including the first base station and the at least two neighboring base stations;

d) updating the first estimate of the position of the UE and the estimate of the distance r_x from the UE to the first base station using the covariance matrix Q of the TDOA measurements and the estimates of the distances r , i =2 to M , from the UE to the at least two neighboring base stations; e) determining a squared distance (χ-Χχ)² along an x-axis and a squared distance (y-yi)² along a y-axis between the position (x,y) of the User Equipment (UE) and the position (xi,yi) of the first base station using the updated first estimate ^^x'^ of the position of the UE.

To be particular, the first position estimation is performed in step 501 as follows.

The first sub-step in Step 501:

Let z = [x.j'.r,]' be the unknown vector, an estimator can then be z = (G^T F^"1G)-¹ G^^"1h _{( 1 )} wherein

Ψ = c BOB (2)

(3)

(4) , r^^-x)² +(y_i-y)² (5), and r²2, - (K₂ - K, )

h =

(6) , wherein Q is covariance matrix of TDOA measurements, s the squared length of vector , M is the number of measured base stations, c is the speed of light, ^x-'-^y') and (^x>^ are the positions of the i-th base station and terminal respectively, ri is the distance between the i-th base station

X V T

and the user equipment, and , ·' , '·' represent χ,-χ y,-y-. and ' ', respectively,

However, {^r2>^ri>->^ru ί , and thereby ^{B dia}8{^r >^r3>->^ru} _f j._s unknown since it requires the true terminal position, an approximated est imator z₀ =(G^TQ^"1G)^"'G^TQ^"1h

(7)

can then be used with z₀ = [x, y,

in a first step to get a preliminary estimate (x, y) , from which an approximation of ^can be calculated. Equation (1) can then be performed to estimate².

The second sub-step in Step 501: A second step is needed in order to further refine the position estimate .

(x-x,)²

Let (y - y be the unknown vector. An estimator can then be

(9) wherein

0

G,= 1

1

(10) h,=(z-[ _l,0]^y')²

(ID

Ψ,

(12), and 0 y - y

(13)

G₀ can be obtained by using in vector² which is defined by equation (1) to do backward substitution to update equation (4).

Ψ is defined by equations (2) and (3) . Then, the first position estimation module constructs four initially estimated positions in step 503 with the following forms :

sqrt-Z_j(l)

- sqrt-Z_j(2)

(16); and

-sqrt.z^l)

-sqrt.z^Z)

(17) ,

wherein z_pk ( k= 1 , 2 , 3 , 4 ) represents the initially estimated positions, and

the sqrt_z,( ) is determined using an equation having the form: sqrt__Zl(y) = V-z,0-) , if z,(y) < 0,(; - 1,2) ; sqrt_z₁(y^') = -/z₁(y) , if z,(y^') > 0,(j = 1,2) where Zi(j) are vector elements of the unknown vector zi.

So far, four initially estimated positions have been obtained. Then, the method proposed herein goes to step 509a. In step 509a, the calculation module 403 calculates the weighted discrepancy for each initially estimated position. The weighted discrepancy for each initially estimated position may be calculated using the following equation

or

wherein : ^'·¹ is the TDOA measurement that represents the time difference between the i-th base station and the first base station; ^c is the speed of light; is the i-th diagonal element in a covariance matrix of TDOA measurements; and

abs ( . ) is an operator that calculates the absolute value; and ^r,u = dist(z_fl , (x_j , y_i)) ^ _{which is the} distance between the k- th initially estimated position ζ_ρκ and the i-th base station, where dist (A, B) is an operator that calculates the 2D distance between A and B, and k refers to the k-th initially estimated position. At last, the decision unit 409 performs the step 509b and determines the initially estimated position having the smallest weighted discrepancy as the finally estimated position.

Fig. 4B shows a further embodiment of the present invention. Compared to Fig. 4A, the determination module 413 in Fig. 4B further comprises a second position estimation module 411, which is configured to perform the second position estimation for the user equipment. Fig. 5B shows the process of the operation of the system as shown in Fig.4B.

As is shown in Fig.4B and 5B, steps 501 and 503 are similar to those in Fig 5A. The difference is that the four initially estimated positions that are constructed based on the result of the first position estimation are fed, to the second position estimation module 411. Then the second position estimation module 411 performs the second position estimation four times, each time with a respective one of the constructed initially estimated positions as the initial guess position. The second position estimation adopts a combination of Taylor series linearization and an iterative Weighted Least Square algorithm.

Then the four further estimated positions calculated or constructed by the second position estimation module 411 are fed to the calculation module 403 to calculate a respective further weighted discrepancy for each of the further estimated positions calculated or constructed from the second position estimation. This is shown in step 509c. The further weighted discrepancies can here be calculated by using the formulas 18 or 19. At last, in step 509b, the decision unit 409 determines the one of the further estimated positions that has the smallest further weighted discrepancy as the finally estimated position.

Fig. 4C and 5C shows another embodiment of the present invention. Compared to the embodiment illustrated in Fig.4B and 5B, the system shown in Fig 4C further comprises a selection module 407. The selection module 407 is adapted to select at least one of the initially estimated positions having a smallest weighted discrepancy of the weighted discrepancies as the initial guess position to perform the second position estimation . Tobespecific, the selection module 407 is configured to select the initially estimated position corresponding to the smallest weighted discrepancy if the difference between a smallest but one weighted discrepancy and the smallest weighted discrepancy is greater than a threshold or if the quotient of the smallest but one weighted discrepancy divided by the smallest weighted discrepancy is greater than another threshold, and to otherwise select both a initially estimated position corresponding to the smallest weighted discrepancy and another initially estimated position corresponding to the smallest but one weighted discrepancy.

The operation of the system in Fig.4C runs as illustrated in Fig.5C. First, steps 501 and 503 are performed similarly to Fig. 4A and 4B. The differences appear in the following steps. As shown in Fig.5C, when the initially estimated positions are constructed in step 503, then the calculation module 403 calculates the weighted discrepancies for all the initially estimated positions using formulas 18 or 19. This is shown in step 511. Then the selection module 407 selects at least one of the initially estimated positions having a smallest weighted discrepancy of the weighted discrepancies. The selection step 513 can be implemented by sorting and comparing the weighted discrepancies. The selection module 407 will choose one corresponding to the smallest weighted discrepancy from the initially estimated positions as the initial guess position in the second position estimation if the smallest but one of the weighted discrepancies is greater than the product of a constant and the smallest one of the weighted discrepancies, otherwise the selection module 407 will choose one estimated position corresponding to the smallest one of the weighted discrepancies from the initially estimated positions and another estimated position corresponding to the smallest but one of the weighted discrepancies from the initially estimated positions as the initial guess positions in the second position estimation. The selected estimated position (s) will be further sent to a second position estimation module 411 for further position estimation. Meanwhile, these selected estimated position(s) together with their discrepancy or discrepancies are also sent to decision unit 409 for later decisions. Then the second posit ion estimat ion module 411 performs the second position estimation for the user eguipment, as shown in step 515. In case the selection module 407 selects only the initially estimated position having the smallest weighted discrepancy as initial guess position for the second position estimation, . This is illustrated in step 517. The calculation module 403 calculates a further weighted discrepancy for a further estimated position calculated from the second position estimation. Then decision unit 409 compares the discrepancy of the further estimated position calculated from the second position estimation with the discrepancy of the previously selected position from the initially estimated positions, wherein the previously selected position is the one selected in step 513 and sent to decision unit 409, as mentioned above.

In case the selection module 407 selects two initially estimated positions having the smallest two discrepancies as initial guess positions for the second position estimation, the calculation module 403 calculates the further weighted discrepancies for the two further estimated positions that are calculated or constructed by the second position estimation module 411 from the second position estimation . Then, the one having the smallest weighted discrepancy from a group consisted of the previously selected positions from the initially estimated positions and the further estimated positions as the finally estimated position is determined as the finally estimated position, as is shown in step 521. The steps described above could be embodied as follows:

Sorting, e g upwards, z_pk according to their error metric Err(k), k = 1..4. Let the sorted positions and error me trie be: z'_pk,A: = 1..4

Err _ sorted(k), k - 1..4 Then performing the flow: <START> <· Err _sorted(2) > Thresh* Err _sorted(\) _j> z'

use ^pl as Methodl ' s initial guess position and get position estimate , say, Tay; calculate the error metric of the two available results: { Tay, ^Z' }; select the one with minimum error metric as position estimate; else z'

use ^pl as Methodl ' s initial guess and get position estimate , say, Tayl; use z'^p2 as Methodl' s initial guess position and get position estimate , say, Tay2 ; calculate the error metric of the four available results: / Tayl, Tay2 , ^Z'^fi , ^Z' } ; select the one with minimum error metric as position estimate; end

<END>

Where "Thresh" is a constant or threshold that is empirically determined. Thresh=100 is a good tradeoff between computational complexity and accuracy of the finally estimated position. In this flow, sanity check is also helpful for excluding unreasonable fixes, i e estimated positions. A method for sanity check can be cell-based positioning or enhanced cell-based positioning, e.g. cell-based positioning in combination with Angle of Arrival determination (Cell+AoA) result, which methods are known to the person skilled in the art and will not be further discussed in this application.

These steps will be clearer to those skilled in the art from the following detailed example: Example: z' k = \ 4

For ^pk ' ^" with sorted error metric:

Err__l = [0.1932, 44.2392, 178.5950, 271.7369]; z'

Taylor method need to be performed only once, i.e. use ^pl as Taylor method's initial guess position. Because 0.1932 is z'

remarkably lower than 44.2392, ^pl has high reliability.

However, for another z'_pk,& = 1..4 with sorted error metric:

Err_2= [55.3358, 55.4399, 124.3261, 351.2183] ;

Because it is obvious that first two metrics are close, two rounds of independent Taylor calculation is needed, fed by z'_pl and z'_p2 respectively. Then the two Taylor method outputs will be compared in terms of error metric, the best one will be selected as the finally estimated position.

Fig. 6 shows the simulation results of the prior art Chan method and the simulation results of the embodiment as proposed in Fig. 4A and 5A. From Fig. 6, it is apparent that the simulation results of the present application provide a more accurate estimation. In Fig. 6, the coordinates of the true position 615 is [8, -1] and the noise variance is 0.03. The light-black area 615 is the simulation results according to the embodiment as illustrated in Figs. 4A and 5A; the dark-black area 609 is the simulation results according to the prior art.

Fig. 7 shows the simulation results of the method as proposed in the embodiment as illustrated in Figs. 4A and 5A and the simulation results of the embodiment as illustrated in Figs. 4C and 5C. In Fig. 7, the coordinate the true position is [8, -1] and the noise variance is 0.03. The light-black area 711 indicates the simulation results according to the embodiment as illustrated in Figs. A and 5A; the dark-black area 709 indicate the simulation results according to the embodiment as illustrated in Figs. 4C and 5C . The methods described above could be embodied in a computer readable medium. The system described above could be embodied in a network node. The system described above could also be embodied in an improved Serving Mobile Location Center (SMLC) .

Throughout the description and claims of this specification, the words "comprise", "include", and variations of the words, for example "comprising" and "comprises", means "including but not limited to", and is not intended to (and does not) exclude other components, integers or steps.

Throughout the description and claims of this specification, the singular encompasses the plural unless the context otherwise requires. In particular, where the indefinite article is used, the specification is to be understood as contemplating plurality as well as singularity, unless the context requires otherwise.

It will be understood that the foregoing description of the embodiments of the invention has been presented for purposes of illustration and description. This description is not exhaustive and does not limit the claimed invention to the precise forms disclosed. Modifications and variations are possible in light of the above description or may be acquired from practicing the invention. The claims and their equivalents define the scope of the invention.

Claims

1. A method for locating a position of a user equipment in a telecommunication system comprising at least three base stations, the method comprising the step of:

performing (501) a first position estimation for the user equipment using a Quadratic Correction Least Square algorithm; constructing (503) initially estimated positions based on a result of the first position estimation; and

determining (509) a finally estimated position of the user equipment .

2. The method of claim 1 , wherein the step of determining comprises calculating (511) weighted discrepancies for the initially estimated positions, and selecting (513) at least one of the initially estimated positions having a smallest weighted discrepancy as initial guess to perform (515) a second position estimation using a combination of Taylor series linearization and an iterative Weighted Least Square algorithm; or

applying (505) all of the initially estimated positions to per form the second position estimation using a combination of Taylor series linearization and the iterative Weighted Least Square algorithm .

3. The method of claim 2, wherein the step of selecting (513) at least one of the initially estimated positions further comprises :

if the difference between a smallest but one weighted discrepancy and the smallest weighted discrepancy is greater than a threshold or if the quotient of the smallest but one weighted discrepancy divided by the smallest weighted discrepancy is greater than another threshold, the initially estimated position corresponding to the smallest weighted discrepancy is selected, otherwise the initially estimated position corresponding to the smallest weighted discrepancy and the initially estimated position corresponding to the smallest but one weighted discrepancy are both selected.

4. The method of claim 3, wherein the step of determining (509) further comprises:

if one of the initially estimated position having the smallest weighted discrepancy is selected as initial guess position to perform the second position estimation, calculating (517) a further weighted discrepancy for a further estimated position calculated from the second position estimation and determining (523) one having the smallest weighted discrepancy from a group consisted of the previously selected position from the initially estimated positions and the further estimated position as the finally estimated position; if more than one of the initially estimated positions are selected as initial guess positions to perform the second position estimation, calculating (519) further weighted discrepancies for further estimated positions calculated from the second position estimation and determining (521) one having the smallest weighted discrepancy from a group consisted of the previously selected positions from the initially estimated positions and the further estimated positions as the finally estimated position .

5. The method of any one of the preceding claims, wherein the first position estimation is performed by adopting the following equation : ζ = (^βτψ^"16)^"1β^τΨ^"1Η wherein z is an estimator,

Ψ = c²BQB

K = diag{r₂,r₃ ,..., r_M }

, and

h =

wherein Q is covariance matrix of TDOA measurements,

K i— - x^xi ² + y, ² _ _s foQ squared length of vector

_F M ±_s the number of measured base stations, c is the speed of light, ^'^^and^'^ are the positions of the i-th base station and terminal respectively, ri is the distance between the i-th base station and the user equipment, and

r _re_p„r_re_s„e_n_nt_1- *,· - , _ y, y, and r. - r, , respectively, i = 1 to M.

6. The method of claim 5, wherein the first position estimation is further performed by adopting the following equation:

wherein ζχ is a further estimator,

Ψ₍=4Β₁(α₀ψ-^,6₀)-'Β_{1? and} x - x,

0 y - y_} wherein G₀ is an intermediate matrix that can be obtained by using in place of in vector ^z , and wherein (^x,y^ is a preliminary estimate of

7. The method of any one of claims 5 to 6, wherein the initially estimated positions are constructed with the following forms:

-sqrt-z^l)

sqrt-z₁(2)

wherein z_pk ( k=l , 2 , 3 , 4 ) represents the initially estimated positions, and

the sqrt_z,(y) is determined using an equation having the form: sqrt_z,(j) = J-z,(j) , if z,(./)<0,(y = l,2); sqrt_z,(y) = Vz77), if z,0^")≥ ,(j = 1,2) .

8. The method of any one of claims 5 to 7 wherein the step of calculating (511) weighted discrepancies is performed by using an equation having the form:

or

wherein :

'^■' is the TDOA measurement that represents the time difference between the i-th base station and a first base station;

c is the speed of light; is the i-th diagonal element in a covariance matrix of TDOA measurements and

abs ( . ) is an operator that calculates the absolute value; and ,^k is the distance between the k-th initially estimated position of the user equipment and the i-th base station.

9. The method of claim 1, wherein the step of determining (509) further comprises: calculating weighted discrepancies for the initially estimated positions (509a/509c); and

determining an initially estimated position having a smallest weighted discrepancy as the finally estimated position (509b) .

10. A positioning system adapted to locate a position of a user equipment in a telecommunication system comprising at least three base stations, the system comprising:

a first position estimation module (401) adapted to perform a first position estimation for the user equipment using a Quadratic Correction Least Square algorithm and to construct initially estimated positions of the user equipment based on a result of the first position estimation; and

a determination module (413) connected to the first position estimation module and adapted to determine a finally estimated position of the user equipment.

11. The system of claim 10, wherein the determination module (413) further comprises:

a second position estimation module (411) adapted to perform a second position estimation using a combination of Taylor series linearization and an iterative Weighted Least Square algorithm and to calculate one or more than one further estimated positions based on a result of the second position estimation;

a calculation module (403) adapted to calculate weighted discrepancies for the initially estimated positions and to calculate further weighted discrepancies for the one or more than one further estimated positions; and

a decision unit (409) configured to determine one having the smallest weighted discrepancy from a group consisted of the previously selected positions from the initially estimated positions and the further estimated positions as a finally estimated position.

12. The system of claim 11, wherein the determination module (413) further comprises a selection module (407 ) , connected to the calculation module, to the decision unit and to the second position estimation module, adapted to select at least one of the initially estimated positions having the smallest weighted discrepancy as initial guess position to perform the second position estimation.

13. The system of claim 12, wherein the selection module (407) is configured to select the initially estimated position corresponding to the smallest weighted discrepancy if the difference between a smallest but one weighted discrepancy and the smallest weighted discrepancy is greater than a threshold or if the quotient of the smallest but one weighted discrepancy divided by the smallest weighted discrepancy is greater than another threshold, and to otherwise select both the initially estimated position corresponding to the smallest weighted discrepancy and the initially estimated position corresponding to the smallest but one weighted discrepancy.

14. The system of any one of claims 10 to 13, wherein the first position estimation is performed by adopting the following equation:

z = (G^^"1G)^" G^{T ",}h wherein z is an estimator, Ψ = ί²Β0Β

B = diag{r₂ , r₃ ,..., r_M }

wherein Q is covariance matrix of TDOA measurements, is the squared length of vector

i _t jyj i_s the number of measured base stations, c is the speed of light, ^''^•''^and^'^ are the positions of the i-th base station and terminal respectively, ri is the distance between the i-th base station and the user equipment, and '·' , '·' , '·' represent ' ¹ , ' , and ' ', respectively, i = 1 to M.

15. The s s tem of claim 14, wherein the first position estimation is further performed by adopting the following equation:

wherein ζχ is a further estimator, 1 0

G, = 0 1

1 1

Ψ₁=4Β,(6₀ψ-¹6₀ΓΒ_{1ί and} x - x. 0

wherein G₀ is an intermediate matrix that can be obtained by using (^x> y) i_n place of (^χ'·^ in vector ^z , and wherein (^χ'·^ is a preliminary estimate of .

16. The systemof any one of claims 14 to 15, wherein the initially estimated positions are constructed with the following forms:

sqrt_z,(l)

^Zp3 = +

- sqrt_z₁(2)

; and

the sqrt_z,( ) is determined using an equation having the form: sqrt_z₁ ) = -²iO) . i^f z,( )<0,(y = l,2);

17. The systemofanyoneof claims 14 to 16, wherein the calculation module (403) is adapted to use an equation having the following form to calculate the weighted discrepancies for the initially estimated positions:

or

wherein:

^'^•'is the TDOA measurement that represents the time difference between the i-th base station and a first base station;

c is the speed of light;

^'is the i-th diagonal element in a covariance matrix of TDOA measurements;

abs ( . ) is an operator that calculates the absolute value; and ^r,k is the distance between the k-th initially estimated position of the user equipment and the i-th base station.

18. A computer readable medium including logic for locating a position of a user equipment, the logic being operable to perform the steps as defined in claims 1 to 9.

19. A network node comprising the system as claimed in one of claims 10 to 17.

20. An improved Serving Mobile Location Center comprising the system as claimed in one of claims 10 to 17.