CN104159293A - Indoor positioning method for high-speed unmanned rotor craft - Google Patents

Abstract

The invention discloses an indoor positioning method for a high-speed unmanned rotor craft. The indoor positioning method comprises the following steps: multiple wireless routers are mounted in an area providing a positioning service; the area is divided into grids, and WiFi signal intensity in every grid is measured; the WiFi signal intensity is continuously measured by the craft in fixed time intervals; the craft asks for positioning to a server and uploads measured data; the server calculates the position of the craft according to a measuring result, and sends the result to the craft; the craft receives the positioning result. The method can effectively improve the indoor positioning precision of the unmanned rotor craft moving at the high speed.

Description

Towards the indoor orientation method of high speed unmanned rotary wing aircraft

Technical field

The present invention relates to communication, technical field of navigation and positioning, particularly, relate to a kind of indoor orientation method towards high speed unmanned rotary wing aircraft.

Background technology

Universal along with unmanned vehicle, increasing unmanned vehicle navigation feature need positional information to support, i.e. so-called location-based information service (LBS).Therefore location technology develops into the key technology in unmanned vehicle navigation field with supportive.Outdoor, unmanned vehicle can use GPS technology to position, but GPS technology is extremely low in indoor position accuracy, has therefore limited unmanned vehicle and has navigated in indoor use.Along with unmanned rotary wing aircraft is universal in the utilization of large-scale indoor venue, the indoor positioning problem of unmanned rotary wing aircraft becomes increasingly conspicuous, and ripe business industry & solution without comparison.

From 21 century from the beginning of, a lot of colleges and universities and research institution have started the research for general user's indoor positioning technology, and have obtained larger breakthrough.Typical indoor locating system has: the Active Badges system that AT & T Cambridge develops, the RADAR system of utilizing WLAN (wireless local area network) to position, use the Cricket navigation system of ultrasonic wave location technology, SpotON system based on RFID etc.

Wherein, have benefited from the fast development of present mobile intelligent terminal and the extensive use of wireless local area network technology, the indoor positioning technology based on WiFi signal strength signal intensity is becoming the study hotspot of indoor positioning, navigation field in recent years.Indoor positioning technology based on WiFi signal strength signal intensity has the following advantages: 1) directly obtain signal strength signal intensity (Received Signal Strength, RSS) by intelligent terminal; 2) utilize signal strength signal intensity to realize location with the development scheme of pure application; 3) have a navigation system cost low, exploitation is convenient, can provide the advantages such as higher positioning accuracy simultaneously.

But, indoor orientation method based on WiFi can not directly apply on the unmanned rotary wing aircraft of high-speed mobile, reason is that the method needs aircraft stay for some time in original place (1-2 about second), within this period, unmanned vehicle may leave position originally, and therefore positioning precision has been subject to the restriction of WiFi signal strength measurement and aircraft movements speed.

Summary of the invention

For the technical problem existing in above-mentioned prior art, the invention provides a kind of indoor orientation method towards high speed unmanned rotary wing aircraft, by making unmanned rotary wing aircraft continuous collecting locator data in flight course, make location-server can obtain historical location data in the time carrying out location algorithm.By using kalman filter method, estimate the historical mobile alignment of aircraft, thereby revise up-to-date positioning result, improve positioning precision.

For achieving the above object, the technical solution adopted in the present invention is as follows:

Towards an indoor orientation method for high speed unmanned rotary wing aircraft, comprise the steps:

Step 1: first multiple wireless routers are set in the region that positioning service need to be provided;

Step 2: the square net that need to provide the region of positioning service to be divided into multiple length of side L rice, and measure the WiFi signal strength signal intensity that comes from each wireless router in each grid, and the result of measurement is uploaded to location-server;

Step 3: unmanned rotary wing aircraft test constantly in flight course comes from the WiFi signal strength signal intensity of each wireless router, and is stored in this locality;

Step 4: when aircraft need to be located, to server request location, and upload WiFi signal strength measurement data so far;

Step 5: the measurement data that server basis is uploaded, calculating aircraft position, and result is sent to aircraft;

Step 6: aircraft receives positioning result.

Preferably, the wireless router in described step 1 all normally works in SSID broadcast visible mode, sends Beacon broadcast singal with the fixed cycle.

Preferably, described step 2 comprises the steps:

Step 2.1: the WiFi signal strength test process in each grid is: measure respectively for each wireless router the WiFi signal radiation intensity that comes from wireless router for N time in each grid, and measurement result is uploaded to location-server;

Step 2.2: location-server, according to the measurement result of uploading, carries out following calculating:

If grid adds up to W in step 2, for grid w, calculate following a series of function:

$p_{\mathrm{wj}}\left(o\right)=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\frac{1}{\sqrt{2\mathrm{\π}}\mathrm{\σ}}\exp (-\frac{(o-o_{\mathrm{wji}})}{2{\mathrm{\σ}}^2}),j=\mathrm{1,2},...,M,w=\mathrm{1,2},...,W$

Wherein, p _wj() is illustrated in the signal strength signal intensity probability density function that comes from j router that grid w place receives, and N is the testing time in grid w, o _wjifor the value that the i time test of the signal strength signal intensity that comes from j router that receives at grid w place obtains, σ is constant, the sum that M is wireless router, and o represents received signal strength;

Location-server will calculate the function p of gained above _wj(o) be stored in database.

Preferably, described step 3 comprises the steps:

Step 3.1: unmanned rotary wing aircraft comes from the WiFi signal strength signal intensity of each wireless router in flight course with Fixed Time Interval T test constantly, each measurement can obtain one group of signal that comes from each wireless router t=T, 2T ....Wherein while being t for the time, from the signal strength signal intensity of j router.M is the sum of wireless router.The minimum value of time interval T equals unmanned vehicle, and to carry out the minimum of a WiFi signal strength measurement consuming time.

Step 3.2: described unmanned rotary wing aircraft is provided with Cellular Networks or WiFi port device, can connect location-server via Cellular Networks or WiFi.

Preferably, described step 5 comprises the steps:

Step 5.1: upload obtained all WiFi signal strength measurement data according to unmanned rotary wing aircraft, carry out following calculating:

For grid w, calculate following probable value:

$P_{\mathrm{wt}}=\underset{j=1,...,M}{\mathrm{\Π}}{p}_{\mathrm{wj}}\left(o_{\mathrm{jt}}\right),w=1,...,W;t=T,2T,...\mathrm{KT}$

Wherein P _wtthe probability of unmanned rotary wing aircraft in grid w during for time t, the sum that M is wireless router, p _wj() is gained function in step 2.2, o _jtfor mobile terminal in step 3 when the time t the measured signal strength signal intensity that comes from j wireless router, KT is the time of the last location of unmanned rotary wing aircraft.

Finally, for different t, from P _wt, w=1 ..., the corresponding grid w of one of the value of finding out the maximum or the maximum in W, this grid w be calculated in the time of time t the position at unmanned rotary wing aircraft place, be designated as $z_t=\left[\begin{array}{c}{x}_t^{\′}\\ y_t^{\′}\end{array}\right],$ t＝T,2T,...KT。Wherein, x ' _t, y ' _tfor horizontal stroke, the ordinate value of this grid element center.

Step 5.2: use Kalman filtering to z _tprocess, specific as follows:

Set unmanned rotary wing airplane motion state vector:

$s_t=\left[\begin{array}{c}{x}_t\\ y_t\\ v_{\mathrm{xt}}\\ v_{\mathrm{yt}}\end{array}\right]$

Wherein, x _t, y _tfor the position transverse and longitudinal coordinate figure of unmanned rotary wing aircraft when the time t, v _xt, v _ytfor the movement velocity vector of unmanned rotary wing aircraft when the time t is at component value horizontal, longitudinal direction.

Set unmanned rotary wing airplane motion model matrix:

$F=\left[\begin{array}{cccc}1& 0& T& 0\\ 0& 1& 0& T\\ 0& 0& 1& 0\\ 0& 0& 0& 1\end{array}\right]$

Set motion variance matrix:

$Q=\left[\begin{array}{cccc}{\mathrm{\σ}}_1^2& 0& 0& 0\\ 0& {\mathrm{\σ}}_1^2& 0& 0\\ 0& 0& {\mathrm{\σ}}_1^2& 0\\ 0& 0& 0& {\mathrm{\σ}}_1^2\end{array}\right]$

Wherein for motion variance, it is constant.

Setting measurement matrix of consequence:

$H=\left[\begin{array}{cccc}1& 0& 0& 0\\ 0& 1& 0& 0\end{array}\right]$

Setting measurement variance matrix:

$R=\left[\begin{array}{cc}{\mathrm{\σ}}_2^2& 0\\ 0& {\mathrm{\σ}}_2^2\end{array}\right]$

Wherein for measuring variance, it is constant.

Kalman filtering processing procedure is as follows: from t=T to t=KT, carry out following cycle calculations:

${\stackrel{\‾}{P}}_t={\mathrm{FP}}_{t-T}{F}^T+Q$

$K_t={\stackrel{\‾}{P}}_t{H}^T{(H{\stackrel{\‾}{P}}_t{H}^T+R)}^{-1}$

$P_t=(E-K_{t}H){\stackrel{\‾}{P}}_t$

Wherein P _tfiltering matrix during for time t, correction filtering matrix during for time t, K _tfiltering weighting matrix during for time t, E is unit matrix, filtering result during for time t.F ^t, H ^trepresent respectively the transposed matrix of F and H.The initial value of circulation is set to $P_0=\left[\begin{array}{cccc}0& 0& 0& 0\\ 0& 0& 0& 0\\ 0& 0& 0& 0\\ 0& 0& 0& 0\end{array}\right],$

Thus, can obtain Kalman filtering result get export as positioning result.

Indoor orientation method towards high speed unmanned rotary wing aircraft provided by the invention, adopt Kalman filtering, by the historical WiFi signal strength data that utilizes unmanned rotary wing aircraft to gather, by server, up-to-date positioning result is revised, thereby reduce the position error that WiFi signal jitter causes, effectively improved the indoor position accuracy of unmanned rotary wing aircraft.

Compared with prior art, the present invention has following beneficial effect:

The existing indoor positioning technology General Requirements user based on WiFi signal strength signal intensity takes multiple measurements WiFi signal strength signal intensity in same place, and required time is longer.If these technology are directly applied to unmanned rotary wing aircraft, aircraft cannot fly fast in the time carrying out continuous positioning.If aircraft flies fast, system will be used the last result of measuring as basis on location, thereby has seriously reduced the precision of location.With respect to this, the present invention has utilized the historical location data of aircraft, its motion circuit is estimated, thereby effectively revised up-to-date positioning result, has reduced position error.

Brief description of the drawings

By reading the detailed description of non-limiting example being done with reference to the following drawings, it is more obvious that other features, objects and advantages of the present invention will become:

Fig. 1 is flow chart of steps of the present invention;

Fig. 2 is execution architecture schematic diagram of the present invention.

In figure: 1 is location-server; 2 is unmanned rotary wing aircraft; 3 is wireless router.

Embodiment

Below in conjunction with specific embodiment, the present invention is described in detail.Following examples will contribute to those skilled in the art further to understand the present invention, but not limit in any form the present invention.It should be pointed out that to those skilled in the art, without departing from the inventive concept of the premise, can also make some distortion and improvement.These all belong to protection scope of the present invention.

Fig. 1 is the flow chart of a kind of indoor orientation method towards high speed unmanned rotary wing aircraft provided by the present invention, and Fig. 2 is the overall system design structure chart of the inventive method.Native system uses the indoor orientation method for unmanned rotary wing aircraft design provided by the invention, and this system is mainly made up of three parts, respectively: location-server, router and unmanned rotary wing aircraft.

In system, location-server connects by Cellular Networks or WLAN (wireless local area network) and unmanned rotary wing aircraft.The control system acquiescence of unmanned rotary wing aircraft has been installed application that native system provides and at least within the coverage of a certain intelligent router.In flight course, unmanned rotary wing aircraft is with Fixed Time Interval test constantly WiFi signal strength signal intensity.In the time that unmanned rotary wing aircraft need to position self, first user sends a Location Request by wireless connections to location-server, and uploads measurement data; Server, according to measurement result, estimates the position of aircraft most probable appearance and result is informed to this aircraft after calculating by a series of algorithm.

The present invention utilizes the historical WiFi signal strength measurement data of unmanned rotary wing aircraft in motion process, and up-to-date positioning result is revised, and has reduced the low problem of positioning precision causing due to WiFi signal strength measurement number of times deficiency.For the at present popular indoor locating system based on WiFi signal strength signal intensity, because most systems requires user shift position not in WiFi measuring process, be not therefore suitable for the unmanned rotary wing aircraft of high-speed mobile.But in our system, meeting test constantly WiFi signal strength signal intensity in unmanned rotary wing aircraft motion process, even therefore the last time WiFi measure in the situation of number of times deficiency, also can be by utilizing historical positioning result to estimate its course, thus reduce position error.

Below in conjunction with accompanying drawing, the present embodiment is described further.

As shown in Figure 1, the present embodiment comprises the steps:

Step 1: first multiple wireless routers are set in the region that positioning service need to be provided, and wherein, described wireless router all normally works in SSID broadcast visible mode, sends Beacon broadcast singal with the fixed cycle.

Step 2: the square net that need to provide the region of positioning service to be divided into multiple length of side L rice, and measure the WiFi signal strength signal intensity that comes from each wireless router in each grid.WiFi signal strength test process in each grid is: in each grid, measure respectively for each wireless router the WiFi signal radiation intensity that comes from wireless router for N time, and measurement result is uploaded to location-server.Location-server, according to the measurement result of uploading, carries out following calculating:

If grid adds up to W in step 2, for grid w, calculate following a series of function:

$p_{\mathrm{wj}}\left(o\right)=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\frac{1}{\sqrt{2\mathrm{\π}}\mathrm{\σ}}\exp (-\frac{(o-o_{\mathrm{wji}})}{2{\mathrm{\σ}}^2}),j=\mathrm{1,2},...,M,w=\mathrm{1,2},...,W$

Wherein, p _wj() is illustrated in the signal strength signal intensity probability density function that comes from j router that grid w place receives, and N is the testing time in grid w, o _wjifor the value that the i time test of the signal strength signal intensity that comes from j router that receives at grid w place obtains, σ is constant, the sum that M is wireless router, and o represents received signal strength;

Location-server will calculate the function p of gained above _wj(o) be stored in database.

Step 3: unmanned rotary wing aircraft comes from the WiFi signal strength signal intensity of each wireless router in flight course with Fixed Time Interval T test constantly, each measurement can obtain one group of signal that comes from each wireless router t=T, 2T ....Wherein while being t for the time, from the signal strength signal intensity of j router.M is the sum of wireless router.The minimum value of time interval T equals unmanned vehicle, and to carry out the minimum of a WiFi signal strength measurement consuming time.Measured all results are all stored in unmanned rotary wing aircraft this locality.Note, described unmanned rotary wing aircraft is provided with Cellular Networks or WiFi port device, can connect location-server via Cellular Networks or WiFi.

Step 4: when aircraft need to be located, to server request location, and upload WiFi signal strength measurement data so far;

Step 5: location-server is uploaded obtained all WiFi signal strength measurement data according to unmanned rotary wing aircraft, carries out following calculating:

For grid w, calculate following probable value:

$P_{\mathrm{wt}}=\underset{j=1,...,M}{\mathrm{\Π}}{p}_{\mathrm{wj}}\left(o_{\mathrm{jt}}\right),w=1,...,W;t=T,2T,...\mathrm{KT}$

Wherein P _wtthe probability of unmanned rotary wing aircraft in grid w during for time t, the sum that M is wireless router, p _wj() is gained function in step 2, o _jtfor mobile terminal in step 3 when the time t the measured signal strength signal intensity that comes from j wireless router, KT is the time of the last location of unmanned rotary wing aircraft.

Finally, for different t, from P _wt, w=1 ..., the corresponding grid w of one of the value of finding out the maximum or the maximum in W, this grid w be calculated in the time of time t the position at unmanned rotary wing aircraft place, be designated as $z_t=\left[\begin{array}{c}{x}_t^{\′}\\ y_t^{\′}\end{array}\right],$ t＝T,2T,...KT。Wherein, x ' _t, y ' _tfor horizontal stroke, the ordinate value of this grid element center.

,, use Kalman filtering to z thereafter _t, t=T, 2T ... KT processes, specific as follows:

Set unmanned rotary wing airplane motion state vector:

$s_t=\left[\begin{array}{c}{x}_t\\ y_t\\ v_{\mathrm{xt}}\\ v_{\mathrm{yt}}\end{array}\right]$

Wherein, x _t, y _tfor the position transverse and longitudinal coordinate figure of unmanned rotary wing aircraft when the time t, v _xt, v _ytfor the movement velocity vector of unmanned rotary wing aircraft when the time t is at component value horizontal, longitudinal direction.

Set unmanned rotary wing airplane motion model matrix:

$F=\left[\begin{array}{cccc}1& 0& T& 0\\ 0& 1& 0& T\\ 0& 0& 1& 0\\ 0& 0& 0& 1\end{array}\right]$

Set motion variance matrix:

$Q=\left[\begin{array}{cccc}{\mathrm{\σ}}_1^2& 0& 0& 0\\ 0& {\mathrm{\σ}}_1^2& 0& 0\\ 0& 0& {\mathrm{\σ}}_1^2& 0\\ 0& 0& 0& {\mathrm{\σ}}_1^2\end{array}\right]$

Wherein for motion variance, it is constant.

Setting measurement matrix of consequence:

$H=\left[\begin{array}{cccc}1& 0& 0& 0\\ 0& 1& 0& 0\end{array}\right]$

Setting measurement variance matrix:

$R=\left[\begin{array}{cc}{\mathrm{\σ}}_2^2& 0\\ 0& {\mathrm{\σ}}_2^2\end{array}\right]$

Wherein for measuring variance, it is constant.

Kalman filtering processing procedure is as follows: from t=T to t=KT, carry out following cycle calculations:

${\stackrel{\‾}{P}}_t={\mathrm{FP}}_{t-T}{F}^T+Q$

$K_t={\stackrel{\‾}{P}}_t{H}^T{(H{\stackrel{\‾}{P}}_t{H}^T+R)}^{-1}$

$P_t=(E-K_{t}H){\stackrel{\‾}{P}}_t$

Wherein P _tfiltering matrix during for time t, correction filtering matrix during for time t, K _tfiltering weighting matrix during for time t, E is unit matrix, filtering result during for time t.F ^t, H ^trepresent respectively the transposed matrix of F and H.The initial value of circulation is set to $P_0=\left[\begin{array}{cccc}0& 0& 0& 0\\ 0& 0& 0& 0\\ 0& 0& 0& 0\\ 0& 0& 0& 0\end{array}\right],$

Thus, can obtain Kalman filtering result get as positioning result output, and this result is sent it back to aircraft.

Step 6: aircraft receives positioning result.

Visible, native system is by Kalman filter, and the historical data that aircraft is gathered in flight course is processed, thereby has revised the result of last location, has reduced position error.

The environmental parameter of the present embodiment is:

Unmanned rotary wing aircraft: Parrot AR.Drone2.0power edition, control system, based on Android Jelly Bean (4.2), has WiFi function, can set up wireless connections by WiFi interface, or completes WiFi signal strength measurement.

Wireless router: use five TP-LINK TL-WR842N, network standard IEEE802.11n, frequency range: single-frequency (2.4-2.4835GHz).

Location-server: grand base 4930G notebook computer, Duo dual core processor, the internal memory of 2G, the dominant frequency of 2G.Server is connected with five Intelligent wireless routers by wired network, and can communicate by letter with unmanned rotary wing aircraft via the Internet, wireless network.

The present embodiment comprises following concrete steps:

Step 1: first staff is M=5 wireless routers of 1000 square metres of large-scale experiment indoor locations at an area.

Step 2: staff is divided into the ground region of this large-scale experiment chamber the square net of W=1000 length of side L=1 rice.WiFi signal strength test process in each grid is: in each grid, measure respectively the WiFi signal radiation intensity (having measured altogether 50 times) that comes from each Intelligent wireless router for N=10 time, and result is uploaded onto the server.Server, according to the measurement result of uploading, carries out following calculating:

If grid adds up to 1000, for the individual grid of w (1≤w≤1000), calculate following a series of function:

$p_{\mathrm{wj}}\left(o\right)=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\frac{1}{\sqrt{2\mathrm{\π}}\mathrm{\σ}}\exp (-\frac{(o-o_{\mathrm{wji}})}{2{\mathrm{\σ}}^2}),j=\mathrm{1,2},...,M,w=\mathrm{1,2},...,W$

Wherein, p _wj() is illustrated in the signal strength signal intensity probability density function that comes from 1st≤j≤5 router that grid w place receives.N=10 is the testing time in grid w place, o _wjifor the value that the i time test of the signal strength signal intensity that comes from j router that receives at grid w place obtains, σ is constant, σ=10.M=5 is the sum of wireless router, and o represents received signal strength; Location-server will calculate the function p of gained above _wj(o) be stored in database.

Step 3: unmanned rotary wing aircraft comes from the WiFi signal strength signal intensity of each wireless router in flight course with Fixed Time Interval T=2 test constantly second, each measurement can obtain one group of signal that comes from each wireless router t=T, 2T ....Wherein while being t for the time, from the signal strength signal intensity of j router.M is the sum of wireless router.Measured all results are all stored in unmanned rotary wing aircraft this locality.

Step 4: time when aircraft need to be located is t=50 × 2 second (K=50), to server request location, and uploads WiFi signal strength measurement data so far t=T, 2T ..., 50T.

Step 5: location-server is uploaded obtained all WiFi signal strength measurement data according to unmanned rotary wing aircraft, carries out following calculating:

For grid w, calculate following probable value:

$P_{\mathrm{wt}}=\underset{j=1,...,5}{\mathrm{\Π}}{p}_{\mathrm{wj}}\left(o_{\mathrm{jt}}\right),w=1,...,1000;t=T,2T,...50T$

Wherein P _wtthe probability of unmanned rotary wing aircraft in grid w during for time t, p _wj() is gained function in step 2, o _jtfor mobile terminal in step 3 when the time t the measured signal strength signal intensity that comes from j wireless router, 50T is the time of the last location of unmanned rotary wing aircraft.

Finally, for different t, from P _wt, w=1 ..., the corresponding grid w of one of the value of finding out the maximum or the maximum in 1000, this grid w be calculated in the time of time t the position at unmanned rotary wing aircraft place, be designated as $z_t=\left[\begin{array}{c}{x}_t^{\′}\\ y_t^{\′}\end{array}\right],$ t＝T,2T,...50T。Wherein, x ' _t, y ' _tfor horizontal stroke, the ordinate value of this grid element center.

,, use Kalman filtering to z thereafter _t, t=T, 2T ... 50T processes, specific as follows:

Set unmanned rotary wing airplane motion state vector:

$s_t=\left[\begin{array}{c}{x}_t\\ y_t\\ v_{\mathrm{xt}}\\ v_{\mathrm{yt}}\end{array}\right]$

Wherein, x _t, y _tfor the position transverse and longitudinal coordinate figure of unmanned rotary wing aircraft when the time t, v _xt, v _ytfor the movement velocity vector of unmanned rotary wing aircraft when the time t is at component value horizontal, longitudinal direction.

Set unmanned rotary wing airplane motion model matrix:

$F=\left[\begin{array}{cccc}1& 0& 2& 0\\ 0& 1& 0& 2\\ 0& 0& 1& 0\\ 0& 0& 0& 1\end{array}\right]$

Set motion variance matrix:

$Q=\left[\begin{array}{cccc}4& 0& 0& 0\\ 0& 4& 0& 0\\ 0& 0& 4& 0\\ 0& 0& 0& 4\end{array}\right]$

Setting measurement matrix of consequence:

$H=\left[\begin{array}{cccc}1& 0& 0& 0\\ 0& 1& 0& 0\end{array}\right]$

Setting measurement variance matrix:

$R=\left[\begin{array}{cc}10& 0\\ 0& 10\end{array}\right]$

Kalman filtering processing procedure is as follows: from t=T to t=50T, carry out following cycle calculations:

${\stackrel{\‾}{P}}_t={\mathrm{FP}}_{t-T}{F}^T+Q$

$K_t={\stackrel{\‾}{P}}_t{H}^T{(H{\stackrel{\‾}{P}}_t{H}^T+R)}^{-1}$

$P_t=(E-K_{t}H){\stackrel{\‾}{P}}_t$

Wherein P _tfiltering matrix during for time t, correction filtering matrix during for time t, K _tfiltering weighting matrix during for time t, E is unit matrix, filtering result during for time t.F ^t, H ^trepresent respectively the transposed matrix of F and H.The initial value of circulation is set to $P_0=\left[\begin{array}{cccc}0& 0& 0& 0\\ 0& 0& 0& 0\\ 0& 0& 0& 0\\ 0& 0& 0& 0\end{array}\right],$

Thus, can obtain Kalman filtering result get as positioning result output, and this result is sent it back to aircraft.

Step 6: aircraft receives positioning result.

Although content of the present invention has been done detailed introduction by above-described embodiment, will be appreciated that above-mentioned description should not be considered to limitation of the present invention.Read after foregoing those skilled in the art, for multiple amendment of the present invention and substitute will be all apparent.Therefore, protection scope of the present invention should be limited to the appended claims.

Claims (5)

1. towards an indoor orientation method for high speed unmanned rotary wing aircraft, it is characterized in that, comprise the steps:

Step 1: multiple wireless routers are set in the region that positioning service need to be provided;

Step 2: need to provide the region of positioning service to be divided into multiple square nets, and measure the WiFi signal strength signal intensity that comes from each wireless router in each grid, and the result of measurement is uploaded to location-server;

Step 3: unmanned rotary wing aircraft test constantly in flight course comes from the WiFi signal strength signal intensity of each wireless router, and is stored in this locality;

Step 4: when aircraft need to be located, to server request location, and upload WiFi signal strength measurement data so far;

Step 5: the measurement data that server basis is uploaded, calculating aircraft position, and result is sent to aircraft;

Step 6: aircraft receives positioning result.

2. the indoor orientation method towards high speed unmanned rotary wing aircraft according to claim 1, is characterized in that, the wireless router in described step 1 all normally works in SSID broadcast visible mode, sends Beacon broadcast singal with the fixed cycle.

3. the indoor orientation method towards high speed unmanned rotary wing aircraft according to claim 1, is characterized in that, described step 2 comprises the steps:

Step 2.1: the WiFi signal strength test process in each grid is: measure respectively for each wireless router the WiFi signal radiation intensity that comes from wireless router for N time in each grid, and measurement result is uploaded to location-server;

Step 2.2: location-server, according to the measurement result of uploading, carries out following calculating:

If grid adds up to W in step 2, for grid w, calculate following a series of function:

$p_{\mathrm{wj}}\left(o\right)=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\frac{1}{\sqrt{2\mathrm{\π}}\mathrm{\σ}}\exp (-\frac{(o-o_{\mathrm{wji}})}{2{\mathrm{\σ}}^2}),j=\mathrm{1,2},...,M,w=\mathrm{1,2},...,W$

Wherein, p _wj() is illustrated in the signal strength signal intensity probability density function that comes from j router that grid w place receives, and N is the testing time in grid w, o _wjifor the value that the i time test of the signal strength signal intensity that comes from j router that receives at grid w place obtains, σ is constant, the sum that M is wireless router, and o represents received signal strength;

Location-server will calculate the function p of gained above _wj(o) be stored in database.

4. the indoor orientation method towards high speed unmanned rotary wing aircraft according to claim 1, is characterized in that, described step 3 comprises the steps:

Step 3.1: unmanned rotary wing aircraft comes from the WiFi signal strength signal intensity of each wireless router in flight course with Fixed Time Interval T test constantly, each measurement can obtain one group of signal that comes from each wireless router t=T, 2T ..., wherein while the time being t, from the signal strength signal intensity of j router, the sum that M is wireless router, the minimum value of time interval T equals unmanned vehicle, and to carry out the minimum of a WiFi signal strength measurement consuming time;

Step 3.2: described unmanned rotary wing aircraft is provided with Cellular Networks or WiFi port device, can connect location-server via Cellular Networks or WiFi.

5. the indoor orientation method towards high speed unmanned rotary wing aircraft according to claim 3, is characterized in that, described step 5 comprises the steps:

Step 5.1: upload obtained all WiFi signal strength measurement data according to unmanned rotary wing aircraft, carry out following calculating:

For grid w, calculate following probable value:

$P_{\mathrm{wt}}=\underset{j=1,...,M}{\mathrm{\Π}}{p}_{\mathrm{wj}}\left(o_{\mathrm{jt}}\right),w=1,...,W;t=T,2T,...\mathrm{KT}$

Wherein P _wtthe probability of unmanned rotary wing aircraft in grid w during for time t, the sum that M is wireless router, p _wj() is gained function in step 2.2, o _jtfor mobile terminal in step 3 when the time t the measured signal strength signal intensity that comes from j wireless router, KT is the time of the last location of unmanned rotary wing aircraft;

Finally, for different t, from P _wt, w=1 ..., the corresponding grid w of one of the value of finding out the maximum or the maximum in W, this grid w be calculated in the time of time t the position at unmanned rotary wing aircraft place, be designated as $z_t=\left[\begin{array}{c}{x}_t^{\′}\\ y_t^{\′}\end{array}\right],$ T=T, 2T ... KT, wherein, x ' _t, y ' _tfor horizontal stroke, the ordinate value of this grid element center;

Step 5.2: use Kalman filtering to z _tprocess, specific as follows:

Set unmanned rotary wing airplane motion state vector:

$s_t=\left[\begin{array}{c}{x}_t\\ y_t\\ v_{\mathrm{xt}}\\ v_{\mathrm{yt}}\end{array}\right]$

Wherein, x _t, y _tfor the position transverse and longitudinal coordinate figure of unmanned rotary wing aircraft when the time t, v _xt, v _ytfor the movement velocity vector of unmanned rotary wing aircraft when the time t is at component value horizontal, longitudinal direction;

Set unmanned rotary wing airplane motion model matrix:

$F=\left[\begin{array}{cccc}1& 0& T& 0\\ 0& 1& 0& T\\ 0& 0& 1& 0\\ 0& 0& 0& 1\end{array}\right]$

Set motion variance matrix:

$Q=\left[\begin{array}{cccc}{\mathrm{\σ}}_1^2& 0& 0& 0\\ 0& {\mathrm{\σ}}_1^2& 0& 0\\ 0& 0& {\mathrm{\σ}}_1^2& 0\\ 0& 0& 0& {\mathrm{\σ}}_1^2\end{array}\right]$

Wherein for motion variance, it is constant;

Setting measurement matrix of consequence:

$H=\left[\begin{array}{cccc}1& 0& 0& 0\\ 0& 1& 0& 0\end{array}\right]$

Setting measurement variance matrix:

$R=\left[\begin{array}{cc}{\mathrm{\σ}}_2^2& 0\\ 0& {\mathrm{\σ}}_2^2\end{array}\right]$

Wherein for measuring variance, it is constant;

Kalman filtering processing procedure is as follows: from t=T to t=KT, carry out following cycle calculations:

${\stackrel{\‾}{P}}_t={\mathrm{FP}}_{t-T}{F}^T+Q$

$K_t={\stackrel{\‾}{P}}_t{H}^T{(H{\stackrel{\‾}{P}}_t{H}^T+R)}^{-1}$

$P_t=(E-K_{t}H){\stackrel{\‾}{P}}_t$

Wherein P _tfiltering matrix during for time t, correction filtering matrix during for time t, K _tfiltering weighting matrix during for time t, E is unit matrix, filtering result during for time t, F ^t, H ^trepresent respectively the transposed matrix of F and H, the initial value of circulation is set to $P_0=\left[\begin{array}{cccc}0& 0& 0& 0\\ 0& 0& 0& 0\\ 0& 0& 0& 0\\ 0& 0& 0& 0\end{array}\right],$

Thus, can obtain Kalman filtering result get export as positioning result.