CN102890263A - Self-adaptive positioning method and system based on resonance gradient method of received signal strength indicator (RSSI) - Google Patents

Abstract

The invention discloses a self-adaptive positioning method based on a resonance gradient method of a received signal strength indicator (RSSI). The method comprises the following steps of: firstly, performing Gauss processing on a measured RSSI value to eliminate the influence of environmental factors; secondly, weighting the measured RSSI value; and finally, searching an optimal unknown node coordinate through the resonance gradient method. A positioning system automatically detects the RSSI value at fixed time, so positioning parameters can be automatically adjusted and repositioned. The positioning system comprises at least three reference node modules, a to-be-positioned module, a gateway node module and a personal computer (PC), wherein the reference node modules are arranged in a detection area and used for providing position information; the gateway node module is connected with the to-be-positioned module and used for establishing a network and transmitting information; and the PC is connected with the gateway node module and used for determining the position of the to-be-positioned module according to the information which is sent by the gateway node module.

Description

Resonance gradient method adaptive location method and system based on RSSI

Technical field

The present invention relates to a kind of positioning field, particularly a kind of resonance gradient method adaptive location method and system based on RSSI.

Background technology

In recent years, along with MEMS (micro electro mechanical system) (Micro-Electro-Mechanism System, MEMS), SOC (system on a chip) (SOC, System on Chip), the develop rapidly of radio communication and low-power-consumption embedded technology, be pregnant with wireless sensor network (Wireless Sensor Networks, and a change that has brought information Perception with its low-power consumption, low cost, characteristics distributed and self-organization WSN).Nowadays node locating technique has become an important research direction and research topic as one of gordian technique of radio sensing network.

According to whether needs obtain distance or angle information between node to be positioned and reference mode, the location algorithm that uses at present in the radio sensing network mainly is divided into two large classes on principle, based on location algorithm and the location algorithm that need not ranging technology (Range-free) of ranging technology (Range-based).Comparatively speaking, the former positional accuracy is higher, and the latter only determines according to the information such as connectedness of network whether node to be positioned exists near certain reference mode, implements simply, but can only provide general positional information.

Wireless Sensor Network Located Algorithm based on ranging technology comprises based on time of arrival (toa) (Time of Arrival, ToA), time of arrival poor (Time Difference of Arrival, TDoA), arrive angle (Angle of Arrival, AoA) and received signal strength indicator (RSSI) method.ToA and TDoA ranging technology utilize the velocity of propagation of signal and transmission time to calculate distance as input, require high-precision clock to realize synchronously, and its advantage is that positional accuracy is high; The AoA ranging technology needs the extra hardware device such as aerial array with the angle of line and reference line formation between witness mark node and node to be positioned; The RSSI ranging technology is to utilize theory or empirical model, and signal propagation losses is mapped as propagation distance, thereby realizes the location.Under the restriction of the multiple conditions such as algorithm expense, network struction cost and positional accuracy, the location algorithms that adopt based on RSSI more.

Find through the document retrieval to prior art, the Chinese patent name is called " based on the indoor orientation method of BP neural network and improvement centroid algorithm ", application publication number is CN 102413564, this patent proposes to adopt the BP neural network that the received signal strength RSSI of reality and distance are trained, thereby obtain the corresponding relation of received signal strength and distance, utilize at last the improvement centroid algorithm to position.The deficiency of this algorithm is: if external environment changes, then need to re-start a large amount of neural metwork trainings; This algorithm does not have good adaptive location function in addition.

Find that through the document retrieval to prior art the Chinese patent name is called " human body fall detection and warning device ", the patent No. is 102136180A, and this patent proposes to utilize the GPS module to position, and utilizes gsm module to carry out data transmission.The deficiency of this system is: GPS can't realize indoor, inferior occasion location, have considerable restraint for practical application; Adopt gsm module to carry out communication, can produce expense, and depend on the GSM network.

In a word, present detection and location system exists such or such deficiency, and some aspect is needed badly and improved or improve, furtherly:

(1) do not have good adaptive location function, when the T-Ring border changes outside, need to re-start a large amount of neural metwork trainings;

(2) can not realize indoor, the location of inferior occasion, have very large restriction for practical application.

Summary of the invention

The invention provides a kind of resonance gradient method adaptive location method based on RSSI, may further comprise the steps:

(1) arranges the position of at least 3 reference modes in zone to be detected, dispose the location parameter of each reference mode by PC, and form radio sensing network with node MANET to be positioned;

(2) measure between each reference mode and each reference mode and node to be positioned between the RSSI value;

(3) all RSSI values that record are carried out Gauss's processing, obtain some groups of accurately RSSI values;

(4) calculate respectively each reference mode to the distance between the node to be positioned;

(5) for the distance value of each reference mode that obtains to node to be positioned, in conjunction with the coordinate of reference mode self, adopt the resonance gradient method, by the method for continuous iteration, seek the optimum coordinates of node to be positioned.

When carrying out above-mentioned steps regularly or trigger-type detect RSSI value between the reference mode, above predefined thresholding, then re-start step (2) if there is the variation of RSSI value.

The invention provides a kind of resonance gradient method adaptive location system based on RSSI, comprise at least three reference mode modules, module to be positioned, gateway node module and PC; The reference mode module is arranged in the surveyed area, is used for providing positional information; The gateway node module links to each other with module to be positioned, is used for setting up network and transmission information; PC links to each other with the gateway node module, is used for determining module position to be positioned according to the information that the gateway node module is sent.

Preferably, described node module to be positioned, gateway node module, reference mode module all adopt the CC2430 chip based on the radio sensing network Zigbee protocol.

Preferably, described reference mode module comprises wireless radio frequency modules, micro controller module, power module.

Preferably, described node module to be positioned comprises wireless radio frequency modules, micro controller module, power module.

Preferably, described wireless radio frequency modules is used for carrying out wireless telecommunications; Described micro controller module is used for the control whole system; Described power module links to each other with described micro controller module with described wireless radio frequency modules, is used for providing electric power.

Preferably, described gateway node module comprises that wireless radio frequency modules, micro controller module, serial ports turn USB module, power module.

Preferably, described wireless radio frequency modules is used for carrying out wireless telecommunications; Described micro controller module is used for the control whole system; Described serial ports turns the USB module and links to each other with described controller and described PC respectively, is used for rs 232 serial interface signal is converted to the USB level signal; Described power module turns the USB module with described wireless radio frequency modules, described controller and described serial ports respectively and links to each other, and is used for providing electric power.

Compared with prior art, the present invention has the following advantages:

1, the characteristics that have adaptive location.Along with the conversion of surrounding environment, system can identify automatically, and again positions computing.

2, fixed range and the corresponding RSSI value of reference mode have been utilized.So just, need not to carry out a large amount of neural metwork trainings.

3, adopt the resonance gradient method to seek optimum unknown node coordinate, locating speed is faster, and it is more accurate to locate.

Certainly, implement arbitrary product of the present invention and might not need to reach simultaneously above-described all advantages.

Description of drawings

Fig. 1 is an embodiment Organization Chart that the present invention is based on the resonance gradient method adaptive location system of RSSI;

Fig. 2 is the reference mode that the present invention is based on radio sensing network agreement ZigBee, node structure synoptic diagram to be positioned;

Fig. 3 is the gateway node structural representation that the present invention is based on radio sensing network agreement ZigBee;

Fig. 4 is desired position illustraton of model of the present invention;

Fig. 5 is actual location illustraton of model of the present invention;

Fig. 6 is a specific embodiment process flow diagram that the present invention is based on the resonance gradient method adaptive location method of RSSI.

Embodiment

Such as Fig. 1, it is an embodiment Organization Chart that the present invention is based on the resonance gradient method adaptive location system of RSSI, and it comprises module 1 to be positioned, 4 reference mode modules 2, gateway node module 3 and PCs 4; 4 reference mode modules 2 are arranged in the surveyed area, are used for providing positional information; Gateway node module 3 links to each other with module 1 to be positioned, is used for setting up network and transmission information, and PC 4 links to each other with gateway node module 3, is used for determining module to be positioned 1 position according to the information that gateway node module 3 is sent.

Such as Fig. 2, reference mode module 2 and node module to be positioned 1 all comprise wireless radio frequency modules 7, micro controller module 6, power module 8; Wireless radio frequency modules 7 is used for carrying out wireless telecommunications, and micro controller module 6 is used for the control whole system, and power module 8 links to each other with micro controller module 6 with wireless radio frequency modules 7, is used for providing electric power.

Such as Fig. 3, gateway node module 3 comprises that wireless radio frequency modules 7, micro controller module 6, serial ports turn USB module 9, power module 8; Wireless radio frequency modules 7 is used for carrying out wireless telecommunications, micro controller module 6 is used for the control whole system, serial ports turns USB module 9 and links to each other with controller 6 and PC 4 respectively, be used for rs 232 serial interface signal is converted to the USB level signal, power module 8 turns USB module 9 with wireless radio frequency modules 7, controller 6 and serial ports respectively and links to each other, and is used for providing electric power.

Fig. 6 is a specific embodiment that the present invention is based on the resonance gradient method adaptive location method of RSSI, in indoor 4m * 4m scope, place 4 reference modes, each reference mode position distribution is even and fixing, in this zone, place in addition a node to be positioned, MANET forms radio sensing network, carries out following work after the networking success.

(1) measures 6 groups of RSSI values and 4 groups of RSSI values between reference mode and the node to be positioned, totally 10 groups of RSSI values between four reference modes.Measure 200 times for every group, it is in 200 the array that the RSSI value that records is stored in 10 length.

(2) successively these 10 groups of data are carried out Gauss's processing, weed out probability and be lower than 5% small probability event, namely the data of serious distortion.Then remaining data are carried out average value processing, so just obtain 10 more accurately RSSI values.

This Gauss's transaction module is: for a fixed range, repeatedly measure the RSSI value, make RSSI value X _iThe obedience average is that m, variance are σ ²Gaussian distribution, its probability density function is:

$f\left(x\right)=\frac{1}{\mathrm{\σ}\sqrt{2\mathrm{\π}}}{e}^{-\frac{x-m}{2{\mathrm{\σ}}^2}}---\left(1\right)$

Wherein, expectation value m has determined its position, and standard deviation sigma has determined the amplitude that distributes.Expectation value and variance are determined by formula (2) and formula (3).

$m=\frac{{\mathrm{\Σ}}_{i=1}^k{x}_i}{k}---\left(2\right)$

${\mathrm{\σ}}^2=\frac{{\mathrm{\Σ}}_{i=1}^k{(X_i-m)}^2}{-1}---\left(3\right)$

In the formula: X _iBe i RSSI value, k is the rssi measurement number of times.

The principle of Gaussian distribution deal with data: a beaconing nodes is received k RSSI value at same position, wherein certainly existing small probability event, the RSSI value of serious distortion namely, the present invention chooses the RSSI value that is in (m-1.96 σ .m+1.96 σ) scope by Gauss model, be lower than 5% small probability event thereby remove probability.

(3) according to RSSI range finding improved model, obtain each reference mode to the distance between the node to be positioned; Have in positioning system under the condition of four reference modes, measure each reference mode to 3 distance values of node to be positioned; These 3 distance values are weighted processing, just obtain each reference mode to 1 of node to be positioned distance value more accurately.

The algorithmic procedure of this distance is:

With the logarithm in the fixed range substitution radio path loss model between the RSSI value that obtains and the reference mode-normal distribution model, neglect random numbers of Gaussian distribution are X _σ, obtain following computing formula.

${\mathrm{RSSI}}_{\mathrm{am}}={\mathrm{RSSI}}_{\mathrm{ab}}-10\×n\×\lg \left(\frac{d_{\mathrm{am}\left(b\right)}}{d_{\mathrm{ab}}}\right)---\left(4\right)$

${\mathrm{RSSI}}_{\mathrm{am}}={\mathrm{RSSI}}_{\mathrm{ab}}-10\×n\×\lg \left(\frac{d_{\mathrm{am}\left(c\right)}}{d_{\mathrm{ac}}}\right)---\left(5\right)$

${\mathrm{RSSI}}_{\mathrm{am}}={\mathrm{RSSI}}_{\mathrm{ab}}-10\×n\×\lg \left(\frac{d_{\mathrm{am}\left(d\right)}}{d_{\mathrm{ad}}}\right)---\left(6\right)$

Wherein a, b, c, d are described 4 reference modes, RSSI _Ab, RSSI _Ac, RSSI _AdBe respectively reference mode a that step (3) method calculates and the RSSI value between b, a and c, a and the d, the distance between reference mode a and b, a and c, a and the d is d _Ab, d _Ac, d _AdFixing and known, RSSI _AmBe the RSSI value of reference mode a between the node m to be positioned, d _AmBe distance value to be asked, d _{Am (b)}, d _{Am (c)}, d _{Am (d)}Be respectively and utilize the reference mode a of reference mode b, c, d calculating to arrive the distance of node m to be positioned,

Definition reference mode b, c, the d d that adjusts the distance _AmWeighting factor be respectively

Then apart from d _AmComputing formula be:

$d_{\mathrm{am}}=\frac{\frac{1}{d_{\mathrm{ab}}}\×d_{\mathrm{am}\left(b\right)}+\frac{1}{d_{\mathrm{ac}}}\×d_{\mathrm{am}\left(c\right)}+\frac{1}{d_{\mathrm{ad}}}\×d_{\mathrm{am}\left(d\right)}}{\frac{1}{d_{\mathrm{ab}}}+\frac{1}{d_{\mathrm{ac}}}+\frac{1}{d_{\mathrm{ad}}}}---\left(7\right)$

D in the formula _AmThe distance value that obtains by weighting algorithm exactly.

According to above-mentioned similar method, record respectively reference mode b, c, d to node m to be positioned apart from d _Bm, d _Cm, d _Dm

(4) be respectively desired position illustraton of model and actual location illustraton of model such as Fig. 4 and Fig. 5.For the distance value of each reference mode that obtains to node to be positioned, in conjunction with the coordinate of reference mode self, adopt the resonance gradient method, by the method for continuous iteration, seek the optimum coordinates of node to be positioned;

The computation process of this optimum coordinates is:

f＝(d _am ²-d _am′ ²) ²+(d _bm ²-d _bm′ ²) ²+(d _cm ²-d _cm′ ²) ²+(d _dm ²-d _dm′ ²) ²＝((x-x ₁) ²+(y-y ₁) ²-d _am ²) ²+((x-x ₂) ²+(y-y ₂) ²-d _bm ²) ²+((x-x ₃) ²+(y-y ₃) ²-d _cm ²) ²+((x-x ₄) ²+(y-y ₄) ²-d _dm ²) ²(8)

By the minimum value of search f, can find this not such as the optimum coordinates of passing through of node.This method then retrains the multivariable nonlinearity planning problem with this problem conversion for a nothing.

The present invention adopts the resonance gradient method to solve this nonlinear programming problem, basic thought is with resonance qualitly and direction of steepest descent combination, utilize the gradient direction at known iterative point place to construct one group of resonance direction, and search for along this direction, obtain the minimal point of function:

At first ask for function f to the partial derivative of quantitative x, y, with partial derivative all as gradient

Choose (x ₁, y ₁) as initial point h ₁, get initial search direction and be

Thereby just can ask for S ₁The optimal step size λ of direction ₁ ^*Try to achieve h by following formula ₂

h ₂＝h ₁+λ ₁ ^*S ₁ (9)

Put i=2, turn to next step; Ask Order:

$S_i=-\▿f\left(h_i\right)+\left(\frac{{|\▿f\left(h_i\right)|}^2}{{|\▿f\left(h_{i-1}\right)|}^2}\right)S_{i-1}---\left(10\right)$

Then ask for S _iThe optimal step size λ of direction _i ^*, and a h that looks for novelty _I+1

Excellent h _I+1=h _i+ λ _i ^*S _i(11)

Check point h _I+1Whether, then stop such as optimum, otherwise i=i+1 gets back to formula (11) and continues computing.

Regular or trigger-type detects the RSSI value between the reference mode, changes if there is the RSSI value to surpass predefined thresholding, then re-starts step (2).

More than the disclosed preferred embodiment of the present invention just be used for helping to set forth the present invention.Preferred embodiment does not have all details of detailed descriptionthe, does not limit this invention yet and only is described embodiment.Obviously, according to the content of this instructions, can make a lot of modification and deciding.These embodiment are chosen and specifically described to this instructions, is in order to explain better principle of the present invention and practical application, thereby the technical field technician can understand and utilize the present invention well under making.The present invention only is subjected to the restriction of claims and four corner and equivalent.

Claims (12)

1. the resonance gradient method adaptive location method based on RSSI is characterized in that, may further comprise the steps:

(1) arrange the reference mode of at least 3 diverse locations in zone to be detected, dispose the location parameter of each reference mode by PC, and form radio sensing network with node MANET to be positioned:

(2) repeatedly record between each reference mode and each reference mode and node to be positioned between the RSSI value;

(3) all RSSI values that record are carried out Gauss's processing, obtain some groups of accurately RSSI values;

(4) calculate respectively each reference mode to the distance between the node to be positioned;

(5) for the distance value of each reference mode that obtains to node to be positioned, in conjunction with the coordinate of reference mode self, adopt the resonance gradient method, by the method for continuous iteration, seek the optimum coordinates of node to be positioned.

2. the method for claim 1 is characterized in that, also comprises an automatic detecting step:

Regular or trigger-type detects the RSSI value between the reference mode, changes if there is the RSSI value to surpass predefined thresholding, then re-starts step (2).

3. the resonance gradient method adaptive location method based on RSSI as claimed in claim 1, its characteristics be, all RSSI values that step (2) is measured are carried out the model that Gauss processes and are:

Make measured RSSI value X _iThe obedience average is that m, variance are σ ²Gaussian distribution, its probability density function is:

Wherein, expectation value m has determined its position, and standard deviation sigma has determined the amplitude that distributes, and expectation value and variance are determined by formula (2) and formula (3);

In the formula: X _iBe i RSSI value, k is the total measurement number of times of RSSI;

The principle of Gaussian distribution deal with data: a beaconing nodes is received k RSSI value at same position, is wherein certainly existing small probability event, namely the RSSI value of serious distortion; The present invention chooses the RSSI value that is in (m-1.96 σ, m+1.96 σ) scope by Gauss model, be lower than 5% small probability event thereby remove probability.

4. the resonance gradient method adaptive location method based on RSSI as claimed in claim 1, its characteristics be, calculates described each reference mode to the method for the distance between the described node to be positioned to be:

Logarithm between the RSSI value that Gauss was processed and each reference mode in the fixed range substitution radio path loss model-normal distribution model neglects random numbers of Gaussian distribution are X _σImpact, obtain following computing formula:

Wherein a, b, c, d are 4 reference modes, RSSI _Ab, RSSI _Ac, RSSI _AdBe respectively reference mode a that step (3) method calculates and the RSSI value between b, a and c, a and the d, the distance between reference mode a and b, a and c, a and the d is d _Ab, d _Ac, d _AdFixing and known, RSSI _AmBe the RSSI value of reference mode a between the node m to be positioned, d _AmBe distance value to be asked, d _{Am (b)}, d _{Am (c)}, d _{Am (d)}Be respectively and utilize reference mode a that reference mode b, c, d calculate to the distance of node m to be positioned, definition reference mode b, c, the d d that adjusts the distance _AmWeighting factor be respectively

Then apart from d _AmComputing formula be:

D in the formula _AmThe distance value that obtains by weighting algorithm exactly;

According to above-mentioned method, calculate respectively reference mode b, c, d to node m to be positioned apart from d _Bm, d _Cm, d _Dm

5. the resonance gradient method adaptive location method based on RSSI as claimed in claim 1, its characteristics are that the computing method of the optimum coordinates of described node to be positioned are:

By finding the solution

f＝(d _am ²-d _am′ ²) ²+(d _bm ²-d _bm′ ²) ²+(d _cm ²-d _cm′ ²) ²+(d _dm ²-d _dm′ ²) ²＝((x-x ₁) ²+(y-y ₁) ²-d _am ²) ²+((x-x ₂) ²+(y-y ₂) ²-d _bm ²) ²+((x-x ₃) ²+(y-y ₃) ²-d _cm ²) ²+((x-x ₄) ²+(y-y ₄) ²d _dm ²) ²(8)

The minimum value of middle f can find the optimum coordinates of this unknown node, this method with this problem conversion for one without the many quantitatively nonlinear programming problems of constraint; Described resonance gradient method adaptive location method based on RSSI adopts the resonance gradient method to solve this nonlinear programming problem, basic thought is with resonance qualitly and direction of steepest descent combination, utilize the gradient direction at known iterative point place to construct one group of resonance direction, and search for along this direction, obtain the minimal point of function:

At first ask for function f to the partial derivative of quantitative x, y, with partial derivative all as gradient

Choose (x ₁, y ₁) as initial point h ₁, get initial search direction and be Thereby just can ask for the optimal step size λ of SX direction ₁ ^*, try to achieve h by following formula ₂

h ₂＝h ₁+λ ₁* ^S ₁ (9)

Put i=2, turn to next step; Ask

Order:

Then ask for S _iThe optimal step size λ of direction _i ^*, and a h that looks for novelty _I+1

h _i+1＝h _i+λ _i ^*S _i (11)

Check point h _I+1Whether optimum, then stop such as optimum, otherwise i=i+1 gets back to the interrupted computing of formula (10).

6. the resonance gradient method adaptive location system based on RSSI is characterized in that, comprises at least three reference mode modules, a module to be positioned, a gateway node module and a PC,

Described each reference mode module is arranged in the surveyed area, is used for providing its positional information;

Described gateway node module links to each other with described module to be positioned, is used for setting up network and transmission information:

Described PC links to each other with described gateway node module, is used for determining described module position to be positioned according to the information that described gateway node module is sent.

7. the resonance gradient method adaptive location system based on RSSI as claimed in claim 6 is characterized in that described node module to be positioned, gateway node module and each reference mode module all adopt the CC2430 chip based on the radio sensing network Zigbee protocol.

8. the resonance gradient method adaptive location system based on RSSI as claimed in claim 6 is characterized in that described reference mode module comprises wireless radio frequency modules, micro controller module, power module.

9. the resonance gradient method adaptive location system based on RSSI as claimed in claim 6 is characterized in that described node module to be positioned comprises wireless radio frequency modules, micro controller module, power module.

10. the resonance gradient method adaptive location system based on RSSI as claimed in claim 6 is characterized in that, described wireless radio frequency modules is used for carrying out wireless telecommunications; Described micro controller module is used for the control whole system; Described power module links to each other with described micro controller module with described wireless radio frequency modules, is used for providing electric power.

11. the resonance gradient method adaptive location system based on RSSI as claimed in claim 6 is characterized in that described gateway node module comprises that wireless radio frequency modules, micro controller module, serial ports turn USB module, power module.

12. the resonance gradient method adaptive location system based on RSSI as claimed in claim 6 is characterized in that, described wireless radio frequency modules is used for carrying out wireless telecommunications; Described micro controller module is used for the control whole system; Described serial ports turns the USB module and links to each other with described controller and described PC respectively, is used for rs 232 serial interface signal is converted to the USB level signal; Described power module turns the USB module with described wireless radio frequency modules, described controller and described serial ports respectively and links to each other, and is used for providing electric power.

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