CN104090984A - Design method for heterogeneous fingerprint database used for indoor positioning - Google Patents

Abstract

The invention belongs to the technical field of indoor positioning, and relates to a design method for a heterogeneous fingerprint database used for indoor positioning. The design method is suitable for currently existing multi-system communication systems and mobile terminals integrating multiple sensors. According to the design method, the multi-freedom-degree fingerprint database is built by using the intensity value of signals emitted by indoor WiFi nodes, the intensity value of signals emitted by a NodeB, the intensity value of signals emitted by an ENodeB, the intensity value of signals emitted by an indoor FemtoCell and a pressure value collected by an atmospheric pressure sensor, wherein the intensity values and the pressure value are received by a user terminal at prior positions, and three-dimensional indoor positioning is finished through the built fingerprint database. The heterogeneous fingerprint database respectively includes position information (L Position), WiFi fingerprint information (RWiFi), NodeB fingerprint information (RNodeB), ENodeB fingerprint information (RENodeB), FemtoCell fingerprint information (RFemtoCell) and atmospheric pressure fingerprint information (PAtmospheric).

Description

A kind of isomery fingerprint database method for designing for indoor positioning

Technical field

The present invention relates to a kind of isomery fingerprint database method for designing, belong to indoor positioning technical field.

Background technology

The most ripe indoor positioning technology is based on WLAN (Wireless Local Area Network at present, WLAN (wireless local area network)) indoor positioning scheme, the fingerprint image database that the signal intensity of the WiFi node that first this kind of targeting scheme receive in priori position forms, then utilizes the WiFi signal intensity that user terminal receives to mate location with fingerprint database.But the indoor positioning solution based on WLAN, it has used the strength information of WiFi signal to set up fingerprint image database, the degree of freedom of database is low, therefore the poor robustness of this targeting scheme, when WiFi node cannot normally work or environment occur change cause WiFi signal intensity change time, positioning performance can sharply worsen.

But along with the development of wireless communication technology is with ripe, has at present increasing communication system and deposit, and these and the communication system of depositing just in time can provide the more fingerprint image information of multidimensional for indoor positioning.And the at present widespread use of the 3G technology standard in mobile communication, and LTE (Long Term Evolution, Long Term Evolution) fast development of technical standard and LTE-A technical standard, user terminal is in receiving indoor WiFi signal, also can receive the signal of NodeB (base station in 3G mobile communication system) and ENodeB (base station in LTE mobile communication system) transmitting and the signal of indoor FemtoCell (being arranged in the indoor cellular basestation that flies in LTE and LTE-A) transmitting, the signal intensity of the communication system of multiple different systems can be used for setting up the fingerprint image of isomery, this not only can improve the positioning precision of indoor locating system, can also improve the robustness of system.In addition, along with the quick update of mobile terminal, the sensors such as baroceptor have also been integrated in mobile terminal gradually, and the information that these sensors gather also can be integrated in fingerprint image, and the information of extra dimension is further provided.

Summary of the invention

The object of this invention is to provide a kind of isomery fingerprint database method for designing for indoor positioning, with the communication system of the multiple system that is applicable to existing at present and the integrated mobile terminal of multiple sensors, to solve the problem of poor robustness of the low and system of the positioning accurate of existing indoor locating system.

The present invention solves the problems of the technologies described above the technical scheme of taking to be:

For an isomery fingerprint database method for designing for indoor positioning, suppose that indoor environment can receive N simultaneously _wiFithe signal of individual WiFi node transmitting, N _nodeBthe signal of individual NodeB transmitting, N _eNodeBthe signal of individual ENodeB transmitting, N _femtoCellthe signal of individual FemtoCell transmitting, and Earth Surface Atmosphere pressure values is P ₀.The terminal using when hypothesis builds fingerprint database simultaneously comprises WiFi radio-frequency module, 3G module (receiving the signal of NodeB transmitting), 4G module (receiving the signal of ENodeB and FemtoCell transmitting) and baroceptor simultaneously;

Isomery fingerprint database establishment stage mainly contains following several step:

Step 1: area to be targeted reference point is determined.Suppose to exist in area to be targeted N reference point, first will measure the three-dimensional coordinate information of these reference point, the coordinate information measuring for i reference point is (x _i, y _i, z _i).Because the indoor positioning scheme based on isomery fingerprint image aims to provide three-dimensional indoor positioning, so need the three-dimensional coordinate information of reference point.

Step 2: the finger print data at reference point locations place is measured.The intensity r of the WiFi signal receiving in reference point place measuring terminals respectively ^wiFi, NodeB the intensity r of signal ^nodeB, ENodeB the intensity r of signal ^eNodeB, FemtoCell the intensity r of signal ^femtoCellwith atmospheric pressure value P, every kind of finger print data is measured certain number of times.Since the 1st reference point, repeating step 2a to 2e, until N reference point finishes.

Step 2a: at i reference point place, measure N _wiFithe signal strength information of individual WiFi node transmitting, measures K altogether _wiFiinferior, then the signal intensity of each WiFi node is averaged, finally obtain WiFi signal intensity finger print information herein:

$r_i^{\mathrm{WiFi}}={[r_1^{\mathrm{WiFi}},\·\·\·,r_j^{\mathrm{WiFi}},\·\·\·,r_{N_{\mathrm{WiFi}}}^{\mathrm{WiFi}}]}^T---\left(1\right)$

$r_j^{\mathrm{WiFi}}=\frac{1}{K_{\mathrm{WiFi}}}\underset{k=1}{\overset{K_{\mathrm{WiFi}}}{\mathrm{\Σ}}}{r}_{j,k}^{\mathrm{WiFi}}---\left(2\right)$

Its its, be letter successively of j the WiFi letter surveyed for the k time by force.

Step 2b: at i reference point place, measure N _nodeBthe signal strength information of individual NodeB node transmitting, measures K altogether _nodeBinferior, then the signal intensity of each NodeB node is averaged, finally obtain NodeB signal intensity finger print information herein:

$r_i^{\mathrm{NodeB}}={[r_1^{\mathrm{NodeB}},\·\·\·,r_j^{\mathrm{NodeB}},\·\·\·,r_{N_{\mathrm{NodeB}}}^{\mathrm{NodeB}}]}^T---\left(3\right)$

$r_j^{\mathrm{NodeB}}=\frac{1}{K_{\mathrm{NodeB}}}\underset{k=1}{\overset{K_{\mathrm{NodeB}}}{\mathrm{\Σ}}}{r}_{j,k}^{\mathrm{NodeB}}---\left(4\right)$

Its its, be letter successively of j the NodeB letter surveyed for the k time by force.

Step 2c: at i reference point place, measure N _eNodeBthe signal strength information of individual ENodeB node transmitting, measures K altogether _eNodeBinferior, then the signal intensity of each ENodeB node is averaged, finally obtain ENodeB signal intensity finger print information herein:

$r_i^{\mathrm{ENodeB}}={[r_1^{\mathrm{ENodeB}},\·\·\·,r_j^{\mathrm{ENodeB}},\·\·\·,r_{N_{\mathrm{ENodeB}}}^{\mathrm{ENodeB}}]}^T---\left(5\right)$

$r_j^{\mathrm{ENodeB}}=\frac{1}{K_{\mathrm{ENodeB}}}\underset{k=1}{\overset{K_{\mathrm{ENodeB}}}{\mathrm{\Σ}}}{r}_{j,k}^{\mathrm{ENodeB}}---\left(6\right)$

Its its, be j ENodeB surveying for the k time successively strong by force.

Step 2d: at i reference point place, measure N _femtoCellthe signal strength information of individual FemtoCell node transmitting, measures K altogether _femtoCellinferior, then the signal intensity of each FemtoCell node is averaged, finally obtain FemtoCell signal intensity finger print information herein:

$r_i^{\mathrm{FemtoCell}}={[r_1^{\mathrm{FemtoCell}},\·\·\·,r_j^{\mathrm{FemtoCell}},\·\·\·,r_{N_{\mathrm{FemtoCell}}}^{\mathrm{FemtoCell}}]}^T---\left(7\right)$

$r_j^{\mathrm{FemtoCell}}=\frac{1}{K_{\mathrm{FemtoCell}}}\underset{k=1}{\overset{K_{\mathrm{FemtoCell}}}{\mathrm{\Σ}}}{r}_{j,k}^{\mathrm{FemtoCell}}---\left(8\right)$

Its its, be j FemtoCell surveying for the k time successively strong by force.

Step 2e: at i reference point place, measure atmospheric pressure information, measure altogether K _atmosphericinferior, then all atmospheric pressures are averaged, then utilize mean value to deduct surface air pressure values P ₀, finally obtain atmospheric pressure finger print information herein:

$P_i^{\mathrm{Atmospheric}}=\frac{1}{K_{\mathrm{Atmospheric}}}\underset{k=1}{\overset{K_{\mathrm{Atmospheric}}}{\mathrm{\Σ}}}{p}_k^{\mathrm{Atmospheric}}-P_0---\left(9\right)$

Wherein, it is the atmospheric pressure value recording for the k time.

Step 3: the finger print information and the reference point locations information architecture isomery fingerprint database that utilize step 2a to 2e to record, isomery fingerprint database comprises 6 kinds of information about firms, respectively:

1) positional information L _position:

L _Position＝[x y z]

x＝[x ₁,x ₂,…x _N-1,x _N] ^T (10)

y＝[y ₁,y ₂,…y _N-1,y _N] ^T

z＝[z ₁,z ₂,…z _N-1,z _N] ^T

2) WiFi finger print information R _wiFi:

$R_{\mathrm{WiFi}}={\left[\begin{array}{ccccc}{r}_1^{\mathrm{WiFi}}& r_2^{\mathrm{WiFi}}& \·\·\·& r_{N-1}^{\mathrm{WiFi}}& r_N^{\mathrm{WiFi}}\end{array}\right]}^T---\left(11\right)$

3) NodeB finger print information R _nodeB:

$R_{\mathrm{NodeB}}={\left[\begin{array}{ccccc}{r}_1^{\mathrm{NodeB}}& r_2^{\mathrm{NodeB}}& \·\·\·& r_{N-1}^{\mathrm{NodeB}}& r_N^{\mathrm{NodeB}}\end{array}\right]}^T---\left(12\right)$

4) ENodeB finger print information R _eNodeB:

$R_{\mathrm{ENodeB}}={\left[\begin{array}{ccccc}{r}_1^{\mathrm{ENodeB}}& r_2^{\mathrm{ENodeB}}& \·\·\·& r_{N-1}^{\mathrm{ENodeB}}& r_N^{\mathrm{ENodeB}}\end{array}\right]}^T---\left(13\right)$

5) FemtoCell finger print information R _femtoCell:

$R_{\mathrm{FemtoCell}}={\left[\begin{array}{ccccc}{r}_1^{\mathrm{FemtoCell}}& r_2^{\mathrm{FemtoCell}}& \·\·\·& r_{N-1}^{\mathrm{FemtoCell}}& r_N^{\mathrm{FemtoCell}}\end{array}\right]}^T---\left(14\right)$

6) atmospheric pressure finger print information P _atmospheric:

$P_{\mathrm{Atmospheric}}={\left[\begin{array}{ccccc}{P}_1^{\mathrm{Atmospheric}}& P_2^{\mathrm{Atmospheric}}& \·\·\·& P_{N-1}^{\mathrm{Atmospheric}}& P_N^{\mathrm{Atmospheric}}\end{array}\right]}^T---\left(15\right)$

Step 4: the final isomery fingerprint database forming is:

D _Fingerprint＝[L _Position R _WiFi R _NodeB R _EnodeB R _FemtoCell P _Atmospheric] (16)

It is that size is N × (3+N _wiFi+ N _nodeB+ N _eNodeB+ N _femtoCell+ 1) two-dimensional array.

The invention has the beneficial effects as follows:

The present invention profit user terminal receives the signal of intensity level, the ENodeB transmitting of the signal of intensity level, the NodeB transmitting of the signal of indoor WiFi node transmitting intensity level, the signal strength values of indoor FemtoCell transmitting and the atmospheric pressure value of baroceptor collection in priori position build multivariant fingerprint database, and utilize the fingerprint database building to complete indoor three-dimensional localization.The fingerprint database that utilizes the inventive method to build can improve positioning precision and the robustness of indoor locating system.The signal intensity that traditional indoor positioning mode based on fingerprint database has only been applied indoor WiFi node builds fingerprint database, and its degree of freedom is low, poor stability, and can only complete two-dimensional localization.And utilize isomery fingerprint database, and can not only improve the stability of positioning system, can also improve positioning precision, and record atmospheric pressure value by baroceptor and complete three-dimensional localization.

Brief description of the drawings

Fig. 1 is the structure process flow diagram of isomery fingerprint database of the present invention.

Embodiment (referring to Fig. 1):

Suppose that indoor environment can receive the signal of 5 WiFi node transmittings simultaneously, the signal of 5 NodeB node transmittings, the signal of 5 ENodeB node transmittings, the signal of 5 FemtoCell node transmittings, and Earth Surface Atmosphere pressure values is P ₀.The terminal using when hypothesis builds fingerprint database simultaneously comprises WiFi radio-frequency module, 3G module (receiving the signal of NodeB transmitting), 4G module (receiving the signal of ENodeB and FemtoCell transmitting) and baroceptor simultaneously.

Isomery fingerprint database establishment stage mainly contains following several step:

Step 1: area to be targeted reference point is determined.Suppose to have 100 reference point in area to be targeted, first will measure the three-dimensional coordinate information of these reference point, the coordinate information measuring for i reference point is (x _i, y _i, z _i).Because the indoor positioning scheme based on isomery fingerprint image aims to provide three-dimensional indoor positioning, so need the three-dimensional coordinate information of reference point.

Step 2: the finger print data at reference point locations place is measured.The intensity r of the WiFi signal receiving in reference point place measuring terminals respectively ^wiFi, NodeB the intensity r of signal ^nodeB, ENodeB the intensity r of signal ^eNodeB, FemtoCell the intensity r of signal ^femtoCellwith atmospheric pressure value P, every kind of finger print data is measured certain number of times.Since the 1st reference point, repeating step 2a to 2e, until the 100th reference point finishes.

Step 2a: at i reference point place, measure the signal strength information of 5 WiFi node transmittings, measure altogether 20 times, then the signal intensity of each WiFi node is averaged, finally obtain WiFi signal intensity finger print information herein:

$r_i^{\mathrm{WiFi}}={[r_1^{\mathrm{WiFi}},\·\·\·,r_j^{\mathrm{WiFi}},\·\·\·,r_5^{\mathrm{WiFi}}]}^T---\left(1\right)$

$r_j^{\mathrm{WiFi}}=\frac{1}{20}\underset{k=1}{\overset{20}{\mathrm{\Σ}}}{r}_{j,k}^{\mathrm{WiFi}}---\left(2\right)$

Its its, be j WiFi surveying for the k time successively strong by force.

Step 2b: at i reference point place, measure the signal strength information of 5 NodeB node transmittings, measure altogether 20 times, then the signal intensity of each NodeB node is averaged, finally obtain NodeB signal intensity finger print information herein:

$r_i^{\mathrm{NodeB}}={[r_1^{\mathrm{NodeB}},\·\·\·,r_j^{\mathrm{NodeB}},\·\·\·,r_5^{\mathrm{NodeB}}]}^T---\left(3\right)$

$r_j^{\mathrm{NodeB}}=\frac{1}{20}\underset{k=1}{\overset{20}{\mathrm{\Σ}}}{r}_{j,k}^{\mathrm{NodeB}}---\left(4\right)$

Its its, be j NodeB surveying for the k time successively strong by force.

Step 2c: at i reference point place, measure the signal strength information of 5 ENodeB node transmittings, measure altogether 20 times, then the signal intensity of each ENodeB node is averaged, finally obtain ENodeB signal intensity finger print information herein:

$r_i^{\mathrm{ENodeB}}={[r_1^{\mathrm{ENodeB}},\·\·\·,r_j^{\mathrm{ENodeB}},\·\·\·,r_5^{\mathrm{ENodeB}}]}^T---\left(5\right)$

$r_j^{\mathrm{ENodeB}}=\frac{1}{20}\underset{k=1}{\overset{20}{\mathrm{\Σ}}}{r}_{j,k}^{\mathrm{ENodeB}}---\left(6\right)$

Its its, be j ENodeB surveying for the k time successively strong by force.

Step 2d: at i reference point place, measure the signal strength information of 5 FemtoCell node transmittings, measure altogether 20 times, then the signal intensity of each FemtoCell node is averaged, finally obtain FemtoCell signal intensity finger print information herein:

$r_i^{\mathrm{FemtoCell}}={[r_1^{\mathrm{FemtoCell}},\·\·\·,r_j^{\mathrm{FemtoCell}},\·\·\·,r_5^{\mathrm{FemtoCell}}]}^T---\left(7\right)$

$r_j^{\mathrm{FemtoCell}}=\frac{1}{20}\underset{k=1}{\overset{20}{\mathrm{\Σ}}}{r}_{j,k}^{\mathrm{FemtoCell}}---\left(8\right)$

Its its, be j FemtoCell surveying for the k time successively strong by force.

Step 2e: at i reference point place, measure atmospheric pressure information, measure altogether 20 times, then all atmospheric pressures are averaged, then utilize mean value to deduct surface air pressure values P ₀, finally obtain atmospheric pressure finger print information herein:

$P_i^{\mathrm{Atmospheric}}=\frac{1}{20}\underset{k=1}{\overset{20}{\mathrm{\Σ}}}{p}_k^{\mathrm{Atmospheric}}-P_0---\left(9\right)$

Wherein, it is the atmospheric pressure value recording for the k time.

Step 3: the finger print information and the reference point locations information architecture isomery fingerprint database that utilize step 2a to 2e to record, isomery fingerprint database comprises 6 kinds of information about firms, respectively:

1) positional information L _position:

L _Position＝[x y z]

x＝[x ₁,x ₂,…x ₉₉,x ₁₀₀] ^T (10)

y＝[y ₁,y ₂,…y ₉₉,y ₁₀₀] ^T

z＝[z ₁,z ₂,…z ₉₉,z ₁₀₀] ^T

2) WiFi finger print information R _wiFi:

$R_{\mathrm{WiFi}}={\left[\begin{array}{ccccc}{r}_1^{\mathrm{WiFi}}& r_2^{\mathrm{WiFi}}& \·\·\·& r_{99}^{\mathrm{WiFi}}& r_{100}^{\mathrm{WiFi}}\end{array}\right]}^T---\left(11\right)$

3) NodeB finger print information R _nodeB:

$R_{\mathrm{NodeB}}={\left[\begin{array}{ccccc}{r}_1^{\mathrm{NodeB}}& r_2^{\mathrm{NodeB}}& \·\·\·& r_{99}^{\mathrm{NodeB}}& r_{100}^{\mathrm{NodeB}}\end{array}\right]}^T---\left(12\right)$

4) ENodeB finger print information R _eNodeB:

$R_{\mathrm{ENodeB}}={\left[\begin{array}{ccccc}{r}_1^{\mathrm{ENodeB}}& r_2^{\mathrm{ENodeB}}& \·\·\·& r_{99}^{\mathrm{ENodeB}}& r_{100}^{\mathrm{ENodeB}}\end{array}\right]}^T---\left(13\right)$

5) FemtoCell finger print information R _femtoCell:

$R_{\mathrm{FemtoCell}}={\left[\begin{array}{ccccc}{r}_1^{\mathrm{FemtoCell}}& r_2^{\mathrm{FemtoCell}}& \·\·\·& r_{99}^{\mathrm{FemtoCell}}& r_{100}^{\mathrm{FemtoCell}}\end{array}\right]}^T---\left(14\right)$

6) atmospheric pressure finger print information P _atmospheric:

$P_{\mathrm{Atmospheric}}={\left[\begin{array}{ccccc}{P}_1^{\mathrm{Atmospheric}}& P_2^{\mathrm{Atmospheric}}& \·\·\·& P_{99}^{\mathrm{Atmospheric}}& P_{100}^{\mathrm{Atmospheric}}\end{array}\right]}^T---\left(15\right)$

Step 4: the final isomery fingerprint database forming is:

D _Fingerprint＝[L _Position R _WiFi R _NodeB R _EnodeB R _FemtoCell P _Atmospheric] (16)

Its two-dimensional array that is 100 × 24 for size.

Claims (3)

1. for an isomery fingerprint database method for designing for indoor positioning, establish indoor environment and can receive N simultaneously _wiFisignal, the N of individual WiFi node transmitting _nodeBsignal, the N of individual NodeB transmitting _eNodeBsignal and the N of individual ENodeB transmitting _femtoCellthe signal of individual FemtoCell transmitting, and Earth Surface Atmosphere pressure values is P ₀; The terminal using when hypothesis builds fingerprint database simultaneously comprises WiFi radio-frequency module, 3G module, 4G module and baroceptor simultaneously;

It is characterized in that: the implementation procedure of described isomery fingerprint database method for designing is:

Step 1: area to be targeted reference point determine: suppose to exist in area to be targeted N reference point, first will measure the three-dimensional coordinate information of these reference point, the coordinate information measuring for i reference point is (x _i, y _i, z _i);

Step 2: the finger print data collection at reference point locations place: the intensity r of the WiFi signal receiving in reference point place measuring terminals respectively ^wiFi, NodeB the intensity r of signal ^nodeB, ENodeB the intensity r of signal ^eNodeB, FemtoCell the intensity r of signal ^femtoCellwith atmospheric pressure value P, every kind of finger print data is measured certain number of times.Since the 1st reference point, repeating step 2a to 2e, until N reference point finishes;

Step 2a: at i reference point place, measure N _wiFithe signal strength information of individual WiFi node transmitting, measures K altogether _wiFiinferior, then the signal intensity of each WiFi node is averaged, finally obtain WiFi signal intensity finger print information herein:

$r_i^{\mathrm{WiFi}}={[r_1^{\mathrm{WiFi}},\·\·\·,r_j^{\mathrm{WiFi}},\·\·\·,r_{N_{\mathrm{WiFi}}}^{\mathrm{WiFi}}]}^T---\left(1\right)$

$r_j^{\mathrm{WiFi}}=\frac{1}{K_{\mathrm{WiFi}}}\underset{k=1}{\overset{K_{\mathrm{WiFi}}}{\mathrm{\Σ}}}{r}_{j,k}^{\mathrm{WiFi}}---\left(2\right)$

Wherein, it is the signal intensity of j WiFi node recording for the k time;

Step 2b: at i reference point place, measure N _nodeBthe signal strength information of individual NodeB node transmitting, measures K altogether _nodeBinferior, then the signal intensity of each NodeB node is averaged, finally obtain NodeB signal intensity finger print information herein:

$r_i^{\mathrm{NodeB}}={[r_1^{\mathrm{NodeB}},\·\·\·,r_j^{\mathrm{NodeB}},\·\·\·,r_{N_{\mathrm{NodeB}}}^{\mathrm{NodeB}}]}^T---\left(3\right)$

$r_j^{\mathrm{NodeB}}=\frac{1}{K_{\mathrm{NodeB}}}\underset{k=1}{\overset{K_{\mathrm{NodeB}}}{\mathrm{\Σ}}}{r}_{j,k}^{\mathrm{NodeB}}---\left(4\right)$

Wherein, it is the signal intensity of j NodeB node recording for the k time;

Step 2c: at i reference point place, measure N _eNodeBthe signal strength information of individual ENodeB node transmitting, measures K altogether _eNodeBinferior, then the signal intensity of each ENodeB node is averaged, finally obtain ENodeB signal intensity finger print information herein:

$r_i^{\mathrm{ENodeB}}={[r_1^{\mathrm{ENodeB}},\·\·\·,r_j^{\mathrm{ENodeB}},\·\·\·,r_{N_{\mathrm{ENodeB}}}^{\mathrm{ENodeB}}]}^T---\left(5\right)$

$r_j^{\mathrm{ENodeB}}=\frac{1}{K_{\mathrm{ENodeB}}}\underset{k=1}{\overset{K_{\mathrm{ENodeB}}}{\mathrm{\Σ}}}{r}_{j,k}^{\mathrm{ENodeB}}---\left(6\right)$

Wherein, it is the signal intensity of j ENodeB node recording for the k time;

Step 2d: at i reference point place, measure N _femtoCellthe signal strength information of individual FemtoCell node transmitting, measures K altogether _femtoCellinferior, then the signal intensity of each FemtoCell node is averaged, finally obtain FemtoCell signal intensity finger print information herein:

$r_i^{\mathrm{FemtoCell}}={[r_1^{\mathrm{FemtoCell}},\·\·\·,r_j^{\mathrm{FemtoCell}},\·\·\·,r_{N_{\mathrm{FemtoCell}}}^{\mathrm{FemtoCell}}]}^T---\left(7\right)$

$r_j^{\mathrm{FemtoCell}}=\frac{1}{K_{\mathrm{FemtoCell}}}\underset{k=1}{\overset{K_{\mathrm{FemtoCell}}}{\mathrm{\Σ}}}{r}_{j,k}^{\mathrm{FemtoCell}}---\left(8\right)$

Wherein, it is the signal intensity of j FemtoCell node recording for the k time;

Step 2e: at i reference point place, measure atmospheric pressure information, measure altogether K _atmosphericinferior, then all atmospheric pressures are averaged, then utilize mean value to deduct surface air pressure values P ₀, finally obtain atmospheric pressure finger print information herein:

$P_i^{\mathrm{Atmospheric}}=\frac{1}{K_{\mathrm{Atmospheric}}}\underset{k=1}{\overset{K_{\mathrm{Atmospheric}}}{\mathrm{\Σ}}}{p}_k^{\mathrm{Atmospheric}}-P_0---\left(9\right)$

Wherein, it is the atmospheric pressure value recording for the k time;

Step 3: the finger print information and the reference point locations information architecture isomery fingerprint database that utilize step 2a to 2e to record, isomery fingerprint database comprises 6 kinds of information about firms, respectively:

1) positional information L _position:

L _Position＝[x y z]

x＝[x ₁,x ₂,…x _N-1,x _N] ^T (10)

y＝[y ₁,y ₂,…y _N-1,y _N] ^T

z＝[z ₁,z ₂,…z _N-1,z _N] ^T

2) WiFi finger print information R _wiFi:

$R_{\mathrm{WiFi}}={\left[\begin{array}{ccccc}{r}_1^{\mathrm{WiFi}}& r_2^{\mathrm{WiFi}}& \·\·\·& r_{N-1}^{\mathrm{WiFi}}& r_N^{\mathrm{WiFi}}\end{array}\right]}^T---\left(11\right)$

3) NodeB finger print information R _nodeB:

$R_{\mathrm{NodeB}}={\left[\begin{array}{ccccc}{r}_1^{\mathrm{NodeB}}& r_2^{\mathrm{NodeB}}& \·\·\·& r_{N-1}^{\mathrm{NodeB}}& r_N^{\mathrm{NodeB}}\end{array}\right]}^T---\left(12\right)$

4) ENodeB finger print information R _eNodeB:

$R_{\mathrm{ENodeB}}={\left[\begin{array}{ccccc}{r}_1^{\mathrm{ENodeB}}& r_2^{\mathrm{ENodeB}}& \·\·\·& r_{N-1}^{\mathrm{ENodeB}}& r_N^{\mathrm{ENodeB}}\end{array}\right]}^T---\left(13\right)$

5) FemtoCell finger print information R _femtoCell:

$R_{\mathrm{FemtoCell}}={\left[\begin{array}{ccccc}{r}_1^{\mathrm{FemtoCell}}& r_2^{\mathrm{FemtoCell}}& \·\·\·& r_{N-1}^{\mathrm{FemtoCell}}& r_N^{\mathrm{FemtoCell}}\end{array}\right]}^T---\left(14\right)$

6) atmospheric pressure finger print information P _atmospheric:

$P_{\mathrm{Atmospheric}}={\left[\begin{array}{ccccc}{P}_1^{\mathrm{Atmospheric}}& P_2^{\mathrm{Atmospheric}}& \·\·\·& P_{N-1}^{\mathrm{Atmospheric}}& P_N^{\mathrm{Atmospheric}}\end{array}\right]}^T---\left(15\right)$

Step 4: the final isomery fingerprint database forming is:

D _Fingerprint＝[L _Position R _WiFi R _NodeB R _EnodeB R _FemtoCell P _Atmospheric] (16)

It is that size is N × (3+N _wiFi+ N _nodeB+ N _eNodeB+ N _femtoCell+ 1) two-dimensional array.

2. a kind of isomery fingerprint database method for designing for indoor positioning according to claim 1, is characterized in that: described 3G module is for receiving the signal of NodeB transmitting.

3. a kind of isomery fingerprint database method for designing for indoor positioning according to claim 2, is characterized in that: described 4G module is for receiving the signal of ENodeB and FemtoCell transmitting.