CN106371626A - Virtual three-dimensional scene controller - Google Patents

Abstract

The invention discloses a virtual three-dimensional scene controller. The virtual three-dimensional scene controller comprises a control gun and a receiver, wherein the control gun comprises a gun body; a trigger type key and a navigation direction key are arranged on the gun body; the trigger type key is located in the front of the gun body; the navigation direction key is located at the rear end of the gun body; a first circuit board and a lithium battery are mounted in the inner cavity of the gun body; a three-axis gyroscope, a tri-axial accelerometer module, a first MCU and a wireless transmission module are mounted on the first circuit board; and the three-axis gyroscope, the tri-axial accelerometer module and the wireless transmission module are respectively connected with the first MCU. The virtual three-dimensional scene controller integrates the functions of a wireless mouse and a keyboard and is capable of randomly carrying out 360-degree operation without dead angles, so that the omnibearing control and reaming of virtual spaces are realized; the gun type design accords with the handholding habit, so that operation can be carried out by both the left hand and the right hand; and the virtual three-dimensional scene controller has an effective distance of 20 m, supports gesture control, supports large-scale motion sensing games and satisfies the operation requirements of 3D virtual scenes.

Description

A kind of virtual three-dimensional scene controller

Technical field

The invention belongs to virtual device technology field, more particularly, to a kind of virtual three-dimensional scene controller.

Background technology

At present, virtual game, software industry flourish, and the operation that conventional mouse cannot meet 3d virtual scene will Ask.

In the conventional technology, conventional infrared mouse must be used on the table by infrared or laser.Also have only with 2 axles The air mouse of gyroscope.Both devices can only realize x-axis, the two dimensional surface of y-axis controls, for three-dimensional three-dimensional z-axis then Do not enable to control, be not suitable for the virtual three-dimensional scene program of current trend.The operation meeting 3d virtual scene requires, and adopts Corresponding adjustment must also be done by keyboard alternate function key with the air mouse of 2 axle gyroscopes, user is felt very simultaneously Inconvenient.

Content of the invention

It is an object of the invention to provide a kind of virtual three-dimensional scene controller is it is intended to solve conventional mouse using not side Just it is impossible to meet the problem of the operation requirement of 3d virtual scene.

The present invention is achieved in that a kind of virtual three-dimensional scene controller, and described virtual three-dimensional scene controller includes Control rifle and receptor, described control rifle includes gun body, described gun body is provided with trigger-type button and navigation direction key, described pull Machine formula button is located at the front of gun body, and described navigation direction key mapping is in the rearward end of gun body；

First circuit board and lithium battery are installed in described gun body inner chamber, described first circuit board is provided with three axis accelerometer Instrument, three axis accelerometer module, a mcu, wireless transmitter module, described three-axis gyroscope, three axis accelerometer module and nothing Line transmitter module is connected with a described mcu respectively；

Described receptor includes wireless receiving module, and one end of described wireless receiving module is provided with usb interface, described wireless Second circuit board is installed in receiver module inner chamber, described second circuit board is provided with the 2nd mcu and coupled wireless Receiver module；

Described gun body being provided with, positioned at small of the stock lower end, the extension being connected with it, described lithium battery be arranged on described in prolong In the inner chamber of extending portion, described lithium battery inline power management module, described lithium battery is through described power management module and described the One mcu is connected；The rear end of extension is provided with charging inlet, and described charging inlet is connected with described power management module；

Described wireless receiving module is provided with display lamp, and described display lamp is connected with described 2nd mcu；

Navigation direction key is rolling ball structure, is controlled with thumb, realize respectively up and down, left and right 4 directions press Directly press totally 5 keypress functions with 1 to control.

Further, described wireless transmitter module is provided with wireless mesh network node trust value computing unit, described wireless The trust value computing method of the wireless mesh network node of mesh network node trust value computing unit comprises the following steps:

Step one, the interaction times of different time piece between acquisition node, according to the data setup time sequence obtaining, pass through Third index flatness predicting between node the interaction times of next timeslice, by the phase of interaction times predictive value and actual value To error as node direct trust value；The concrete calculation procedure of direct trust value is:

The interaction times of n timeslice between collection network observations node i and node j:

Choose intervals t as an observation time piece, with observer nodes i and tested node j in 1 timeslice Interior interaction times as observation index, true interaction times, be denoted as y_t, record the y of n timeslice successively_n, and preserved In the communications records table of node i；

The interaction times of (n+1)th timeslice of prediction:

According to the interaction times setup time sequence of the n timeslice collecting, using under third index flatness prediction Interaction times between one timeslice n+1 interior nodes i and j, predict interaction times, are denoted asComputing formula is as follows:

${\hat{y}}_{n+1}=a_n+b_n+c_n$

Predictive coefficient a_n、b_n、c_nValue can be calculated by equation below:

$a_n=3{\hat{y}}_{n+1}^{\left(1\right)}-3{\hat{y}}_{n+1}^{\left(2\right)}+{\hat{y}}_{n+1}^{\left(3\right)}$

$b_n=\frac{\mathrm{\α}}{2{(1-\mathrm{\α})}^2}\[(6-5\mathrm{\α}){\hat{y}}_{n+1}^{\left(1\right)}-2(5-4\mathrm{\α}){\hat{y}}_{n+1}^{\left(2\right)}+(4-3\mathrm{\α}){\hat{y}}_{n+1}^{\left(3\right)}\]$

$c_n=\frac{{\mathrm{\α}}^2}{2{(1-\mathrm{\α})}^2}\[{\hat{y}}_{n+1}^{\left(1\right)}-2{\hat{y}}_{n+1}^{\left(2\right)}+{\hat{y}}_{n+1}^{\left(3\right)}\]$

Wherein:Be respectively once, secondary, Three-exponential Smoothing number, calculated by equation below Arrive:

${\hat{y}}_{n+1}^{\left(1\right)}=\mathrm{\α}\×y_n+(1-\mathrm{\α})\×{\hat{y}}_n^{\left(1\right)}$

${\hat{y}}_{n+1}^{\left(2\right)}=\mathrm{\α}\×{\hat{y}}_{n+1}^{\left(1\right)}+(1-\mathrm{\α})\×{\hat{y}}_n^{\left(2\right)}$

${\hat{y}}_{n+1}^{\left(3\right)}=\mathrm{\α}\×{\hat{y}}_{n+1}^{\left(2\right)}+(1-\mathrm{\α})\×{\hat{y}}_n^{\left(3\right)}$

It is the initial value of third index flatness, its value is

${\hat{y}}_0^{\left(1\right)}={\hat{y}}_0^{\left(2\right)}={\hat{y}}_0^{\left(3\right)}=\frac{y_1+y_2+y_3}{3}$

α is smoothing factor (0 ＜ α ＜ 1), embodies the time attenuation characteristic trusted, that is, from predictive value more close to timeslice y_tWeight is bigger, from predictive value more away from timeslice y_tWeight is less；Usually, if data fluctuations are larger, and long-term trend Amplitude of variation is larger, assumes α when substantially rapidly rising or falling trend and should take higher value (0.6～0.8), can increase in the recent period Data is to the impact predicting the outcome；When data has fluctuation, but when long-term trend change is little, α is in 0.1～0.4 value；If number According to smooth fluctuations, α takes 0.05～0.20；

Calculating direct trust value:

Direct trust value td of node j_ijFor predicting interaction timesWith true interaction times y_n+1Relative error,

Step 2, calculates indirect trust values using calculating formula obtained from multipath trust recommendation mode；Using multipath The concrete calculation procedure that calculating formula obtained from trust recommendation mode calculates indirect trust values is:

The collection direct trust value to node j for the trusted node:

Node i meets td to all_ikThe credible associated nodes of≤φ inquire its direct trust value to node j, wherein φ For the believability threshold of recommended node, according to the precision prescribed of credibility, the span of φ is 0～0.4；

Calculating indirect trust values:

Trust value collected by COMPREHENSIVE CALCULATING, obtains indirect trust values tr of node j_ij,

Wherein, had with j node in the associated nodes for observer nodes i for the set (i) and interacted And its direct trust value meets td_ikThe node set of≤φ；

Step 3, draws comprehensive trust value by direct trust value and indirect trust values conformity calculation；Concrete calculation procedure is:

Comprehensive trust value (t_ij) computing formula as follows: t_ij=β td_ij+(1-β)tr_ij, wherein β (0≤β≤1) expression is directly Connect the weight of trust value, when β=0, node i and node j do not have a direct interaction relation, the calculating of comprehensive trust value directly from In indirect trust values, it is more objective to judge；When β=1, node i is to the comprehensive trust value of node j all from directly trusting Value, in this case, judges more subjective, Practical Calculation determines the value of β as needed.

Further, described wireless transmitter module is provided with signal negentropy computing module, described signal negentropy computing module Signal negentropy computational methods include:

$\hat{j}\left(x\right)\≈\underset{j=1}{\overset{n}{\σ}}{k}_j{\[e\left\{g_j\left(x\right)\right\}-e\left\{g_j\left(m\right)\right\}\]}^2;$

Wherein, k_jFor some normal numbers, m is the gaussian variable with zero-mean, unit variance, function g_jFor non-secondary letter Number；

As all of g_jDuring=g, approximate expression becomes:

j_g(x)≈c[e{g(x)}-e{g(m)}]²；

Wherein, g is arbitrarily non-quadratic function, and c is a constant.

Further, described wireless transmitter module is provided with wireless network distributed module, the distributed mould of described wireless network The distributed method of block includes:

Step one, the neighbours of node si, the point position of covering, default network life l, the marking class of battery life bi, si Type upd, ii=1；

Step 2, judges whether ii ＜ l/l, if so, then directly carries out next step, no, then type, is labeled as lab Optimum working time of node arrange, then terminate；

Step 3, calculates maximum additionally effectively cover time and pro-jobs degree, and to neighbours broadcast mes (i, null, upd,δp_i)；

Step 4, judgesIf being, s_iδ p_iWhether maximum, if s in neighbours_iδ p_i Neighbours are maximum, s_iLabelling oneself is lab and broadcasts mes (i, lab, sch, δ p to neighbours_i)d_i=d_i-b_i, s_iexits.；If s_iδ p_iNeighbours are not maximum, judge s_iWhether receive neighbours s_kMes (k, lab, sch, δ p_k)；If s_iIt is to receive neighbours s_kMes (k, lab, sch, δ p_k), then s_iMore new neighbor s_kInformation, recalculateδ p_i, and broadcast mes (i, upd, null, δ p to neighbours_i)；If s_iIt is not received by neighbours s_kMes (k, lab, sch, δ p_k), then, judge s_iWhether receive neighbours s_kMes (k, upd, null, δ p_k), if s_iIt is to receive neighbours s_kMes (k, upd,null,δp_k), s_iMore new neighbor s_kPro-jobs degree；If s_iIt is not received by neighbours s_kMes (k, upd, null, δ p_k), then return and judge

Further, described wireless receiving module is provided with radio net cooperative spectrum sensing module, described radio net The radio net cooperation frequency spectrum sensing method that frequency spectrum sensing module is made in complexation includes:

Step one, the node participating in cooperative sensing proceeds by the cycle for τ_sFrequency spectrum detection process, obtain primary user frequency The feature of spectrum resource；Feature according to primary user's signal and channel fading coefficient, calculate each local sensing node cr_iReceive Signal y_i(n)；According to energy measuring principle, obtain sensing node cr_iStatistic v of the signal energy at place_i, when sampling quantity is enough When big, v_iApproximate Gaussian distributed；

Step 2, normal sensing node and malice sensing node pass through orthogonal CCCH to data fusion center Carry out the report of perception information；Normal sensing node is faithfully reported the perception information of oneself, and malicious node then adopts false-alarm Attack mode is reported: when signal energy statistic v_iMore than attacking threshold value η, then faithfully report the sensing results of oneself； Otherwise will be with Probability p_aOffensive attack, sends a higher energy value to reach the mesh of malicious attack to data fusion center 's；

Step 3, data fusion center carries out data fusion, and the evil according to malicious node to the perception information collected Meaning attack mode calculates the false-alarm probability of the overall situation；Signal to noise ratio γ according to each node_iParticipate in the secondary of cooperative sensing for each Level user cr_i, i=1 ... k one weight of designThen signal energy statistic u collection being obtained_iCarry out Linear weighted function obtains the statistic of final signal energyAnalysis false-alarm malicious attack pattern is made to frequency spectrum perception The impact becoming, obtains overall false-alarm probability p_fWith attack Probability p_a, attack threshold value η, the function expression between attack strength δ such as Under:

$p_f=q(\frac{\sqrt{{\mathrm{\σ}}_{i=1}^k{{\mathrm{\ω}}_i}^2(1+2{\mathrm{\γ}}_i)}}{q\left(p_d\right)}+\sqrt{{\mathrm{\τ}}_s{f}_s}({\mathrm{\σ}}_{i=1}^k{\mathrm{\ω}}_i{\mathrm{\γ}}_i+\frac{c_1-c_0}{{\mathrm{\σ}}_u^2}\left)\right)$

Wherein:

Step 4, secondary user's and primary user's share spectrum resources, if primary user is detected to be in not busy state, will be with High-power transmission signal, otherwise will be with small-power transmission signal；Secondary user's transmitter su-tx determining according to data fusion center Plan result adjusts the signal transmission power of oneself, if primary user pu is detected to be in not busy state, will be with high-power p₀Transmitting Signal；If it is primary user pu with power p that primary user pu is in busy condition_pTransmission signal, secondary user's transmitter su-tx will With small-power p₁Transmission signal；Therefore the average throughput in a time frame in secondary network is written as form:

$\begin{array}{c}r=e\{\frac{t-{\mathrm{\τ}}_s}{t}\[(1-p_f)p\left(h_0\right){\log }_2(1+\frac{g_{ss}{p}_0}{{\mathrm{\σ}}_u^2})+p_{f}p\left(h_0\right){\log }_2(1+\frac{g_{ss}{p}_1}{{\mathrm{\σ}}_u^2})\\ +\left(1-p_d\right)p\left(h_1\right){\log }_2\left(1+\frac{g_{ss}{p}_0}{h_k{p}_p+{\mathrm{\σ}}_u^2}\right)+p_{d}p\left(h_1\right){\log }_2\left(1+\frac{g_{ss}{p}_1}{h_k{p}_p+{\mathrm{\σ}}_u^2}\right)\]\}\end{array}---\left(1\right)$

Wherein: h_k,g_ssIt is primary user transmitter pu-tx respectively to secondary user's transmitter su-tx secondary user's transmitter Channel fading coefficient between su-tx, secondary user's transmitter su-tx to secondary user's receiver su-rx, p (h₀) and p (h₁) Represent that primary user pu is practically in the probability of not busy state and busy condition respectively；

Step 5, builds Optimized model, determines the constraints about transmission power and detecting period, and solution is set up Optimization problem, obtains so that the handling capacity of the secondary network maximum perception cycle of cooperative sensing and the signal of secondary user's are sent out Penetrate power；Ensure that secondary user's network can work long hours, the transmission power of secondary user's need to be limited it is ensured that secondary use The average emitted power of family network is less than limit value:

e{α₀p₀+α₁p₁+β₀p₀+β₁p₁}≤p_av(2)

P in formula_avThe maximum average emitted power of secondary user's transmitter su-tx, this averagely refer to fading channel system Number h_i,g_ss,g_spThe expectation of stochastic variable；The top priority of cognitive radio networks is the service quality of protection primary user's network, Therefore the jamming power of network is limited；According to the spectrum sharing network model based on cooperative sensing it is known that interference only exists Primary user pu is in and occurs during busy condition, so average interference power constraint is written as form:

e{g_sp(β₀p₀+β₁p₁)}≤q_av(3)

Guarantee that the detection probability at each node and the whole detection probability of network are not less than respective target detection respectively Probability, the restrictive condition with regard to detection probability is as follows:

p_d≥p_th,p_di≥p_th, i=1,2 ... k (4)

According under above-mentioned restrictive condition, set up the optimization with the average throughput maximizing secondary network as object function Problem:

$\begin{array}{cc}\underset{\left\{{\mathrm{\τ}}_s,\mathrm{\ϵ},\left\{{\mathrm{\ϵ}}_i\right\},p_0,p_1\right\}}{\max }& r\\ subjectto& \left(2\right),\left(3\right),\left(4\right),p_0\&greaterequal;0,p_1\&greaterequal;0\\ & 0\≤{\mathrm{\τ}}_s\≤t\end{array}---\left(\mathrm{pr}oblem1\right)$

Solve set up optimization problem, select to make the perception week of the cooperative sensing of handling capacity maximum of secondary network The signal transmission power of phase and secondary user's is as the perceptual parameters of this frequency spectrum perception model；

Step 6, repetitive cycling step one is to step 5, until completing emulation experiment 1000 times, to the optimum obtaining every time Solution is averaged, using meansigma methodss as the perceptual parameters of frequency spectrum perception model.

The virtual three-dimensional scene controller that the present invention provides, integrates wireless mouse, keypad function, 360 degree of no dead angles Arbitrarily it is achieved that the comprehensive control in Virtual Space and roaming, pistol type design meets holds custom, can be exchanged with right-hand man for manipulation Operation, 20 meters of coverage, support gesture manipulation, support large-scale somatic sensation television game, the operation meeting 3d virtual scene requires.This The trust value computing method of the wireless mesh network node that invention provides, comprising: direct trust value calculates, indirect trust values calculate With comprehensive trust value computing three phases, the interaction times of different time piece, root between direct trust value calculating acquisition node first According to the data setup time sequence obtaining, then predict the interaction of next timeslice between node by third index flatness Number of times, using the relative error of interaction times predictive value and actual value as node direct trust value, the calculating of indirect trust values Formula adopts obtained from multipath trust recommendation mode, and comprehensive trust value is by direct trust value and indirect trust values conformity calculation Draw；The present invention provides a method that for node trust value computing, according to the concrete condition of network, adaptable put down may be selected Sliding factor alpha, believability threshold φ, the value of direct trust value weight beta it is ensured that the time attenuation characteristic of trust value and objectivity, Objectively and accurately describe the credibility of node, computation complexity is low and communication cost is little, be applicable to wireless mesh network, have Stronger popularization and using value.The present invention is in always effective cover time, network life, average weighted incident detection rate and network Residue energy of node uniformity aspect, the performance of modified hydrothermal process has been above the performance of original algorithm and random algorithm.

Brief description

Fig. 1 is virtual three-dimensional scene controller architecture schematic diagram provided in an embodiment of the present invention；

Fig. 2 is receiver architecture schematic diagram provided in an embodiment of the present invention；

In figure: 1, gun body；2nd, first circuit board；3rd, charging inlet；4th, lithium battery；5th, trigger-type button；6th, navigation direction Key；11st, housing；13rd, second circuit board；15th, usb interface；16th, display lamp.

Specific embodiment

In order that the objects, technical solutions and advantages of the present invention become more apparent, with reference to embodiments, to the present invention It is further elaborated.It should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not used to Limit the present invention.

Below in conjunction with the accompanying drawings the application principle of the present invention is explained in detail.

Refer to the virtual three-dimensional scene controller of Fig. 1 to Fig. 2: the embodiment of the present invention, including control rifle and receptor, institute State control rifle and include gun body 1, described gun body 1 is provided with trigger-type button 5 and navigation direction key 6, and described trigger-type button 5 is located at The front of gun body 1, described navigation direction key 6 is located at the rearward end of gun body 1；

First circuit board 2 and lithium battery 4 are installed in described gun body 1 inner chamber, described first circuit board 2 is provided with three Axle gyroscope, three axis accelerometer module, a mcu, wireless transmitter module, described three-axis gyroscope, three axis accelerometer mould Block and wireless transmitter module are connected with a described mcu respectively；

Described receptor includes wireless receiving module, and one end of described wireless receiving module is provided with usb interface 15, described nothing Second circuit board 13 is installed in line receiver module inner chamber, described second circuit board 13 is provided with the 2nd mcu12 and with its phase Wireless receiving module even；

Described three-axis gyroscope, three axis accelerometer module collection x, y, z axle in virtual three-dimensional space scene for the mouse Position signalling and export to a described mcu, a described mcu passes through described wireless transmitter module and wireless receiving module will Mouse positional data transmits to described 2nd mcu, and described 2nd mcu passes through described usb interface 15 again, and mouse positional data is defeated Go out to computer.

Described gun body 1 is being provided with, positioned at small of the stock lower end, the extension being connected with it, and described lithium battery 4 is arranged on described In the inner chamber of extension, described lithium battery 4 inline power management module, described lithium battery 4 is through described power management module and institute State a mcu to be connected；The rear end of extension is provided with charging inlet 3, and described charging inlet 3 is connected with described power management module.

Described wireless receiving module is provided with display lamp 16, and described display lamp 16 is connected with described 2nd mcu.

Described three-axis gyroscope, three axis accelerometer module adopt the module of model mpu6050, described wireless transmit mould Block and wireless receiving module all adopt the 2.4g wireless module of model rf2401.

Three-axis gyroscope, three axis accelerometer module adopt the module of model mpu6050, and this module embeds three axis accelerometer Instrument and three axis accelerometer, the i2c bus using standard carries out reading and writing data and control.Three-axis gyroscope can distinguish aware space The change of the anglec of rotation in x-axis, y-axis and z-axis, the rotation angle information of detection, through a mcu algorithm calculation process, controls meter Left and right in calculation machine virtual 3d scene, rotate up and down and in front and back.And three axis accelerometer can distinguish aware space x-axis, y-axis and z The change of translational speed on axle, the displacement information of detection, through a mcu algorithm calculation process, control computer virtual 3d field Left and right in scape, upper and lower and movable.Correspondingly to virtual 3d scene after summary angle and change in displacement information The adjustment at interior visual angle, can facilitate and carry out spatial roaming control in virtual 3d scene.

The angular velocity full lattice sensing range of mpu-6050 is ± 250, ± 500, ± 1000 and ± 2000 °/sec (dps), can Accurately follow the trail of quick with action at a slow speed, the accelerator full lattice sensing range that user's programmable controls be ± 2g, ± 4g ± 8g with ± 16g.Product transmission can pass through the ic being up to 400khz.Mpu-6000 can be in different operating at voltages, and vdd supply voltage is situated between and is 2.5v ± 5%, 3.0v ± 5% or 3.3v ± 5%, logic interfacing vvdio is powered as 1.8v ± 5% (mpu6000 only use vdd), The agitator that other features include built-in temperature-sensitive sticker and only have ± 1% variation in environment of operation.

Wireless transmitter module is using general 2.4g wireless transmitter module rf2401 in the world, 50 meters of transmission range, transmission speed Rate 1m/s, has that communication frequency is high, and capacity is big, the features such as long transmission distance.

First mcu adopt 32 arm processor chips, built-in modulus translation interface, can be by gyroscope, acceleration analysis The information such as information and trigger-type button 5, handss navigation direction key 6 is entered line algorithm conversion and is encoded, and is launched by wireless module Go.

Lithium battery 4 adopts high capacity polymer lithium battery group, has stable performance, small volume, its embedded power management Module can prevent the faults such as lithium battery external short circuit, overcharge, over-discharge, provides long-time stable power supply for device.

Trigger-type button 5 forefinger control, is defined as mouse function left button in computer operating system.

Navigation direction key 6 is rolling ball structure, is controlled with thumb, realize respectively up and down, left and right 4 directions press Directly press totally 5 keypress functions with 1 to control.4 arrow buttons define according to software requirement, directly descend button definition to be computer Mouse function right button in operating system.

Charging inlet 3 provides interface for lithium cell charging, can be using special charger it is also possible to pass through data switch line Access computer usb interface and electricity is adopted from computer.

Receptor, using standard usb2.0 communication standard, is coupled together with computer by usb interface 15 by this interface, There is provided power supply data coffret for receptor.

Wireless receiving module is using general 2.4g wireless module rf2401 in the world, low based on ieee802.15.4 standard Power consumption Personal Area Network agreement, has that communication frequency is high, and capacity is big, the features such as long transmission distance.Matched by address, can prevent empty here The interfering of other wireless devices interior.

Using the virtual three-dimensional scene controller of the present invention, directly control rifle can be held with left hand or the right hand, in the air Tilt, rotation or movement are adjusted to the picture of virtual 3d scene, by trigger-type button 5 and navigation direction key 6 to void The behavior intended in 3d scene is controlled.

First usb interface 15 and computer are coupled together, three-axis gyroscope and triaxial accelerometer are placed on home position, point to Its original orientation, home position p (x, y, z), r (rx, ry, rz) can make a reservation for.Then, 4 directionkeys are controlled with thumb, respectively Scene is controlled to move to four direction；Control rifle forwards, backwards both direction move, in 3d scene drive scene advance and after Move back；Control rifle four direction skew vertically and horizontally, control scene to do left rotation and right rotation with current point and rotate up and down respectively；With When, trigger-type button 5 is controlled with forefinger, navigation direction key 6 is controlled with middle finger, realizes the left and right key operation of mouse.

Further, described wireless transmitter module is provided with wireless mesh network node trust value computing unit, described wireless The trust value computing method of the wireless mesh network node of mesh network node trust value computing unit comprises the following steps:

Step one, the interaction times of different time piece between acquisition node, according to the data setup time sequence obtaining, pass through Third index flatness predicting between node the interaction times of next timeslice, by the phase of interaction times predictive value and actual value To error as node direct trust value；The concrete calculation procedure of direct trust value is:

The interaction times of n timeslice between collection network observations node i and node j:

Choose intervals t as an observation time piece, with observer nodes i and tested node j in 1 timeslice Interior interaction times as observation index, true interaction times, be denoted as y_t, record the y of n timeslice successively_n, and preserved In the communications records table of node i；

The interaction times of (n+1)th timeslice of prediction:

According to the interaction times setup time sequence of the n timeslice collecting, using under third index flatness prediction Interaction times between one timeslice n+1 interior nodes i and j, predict interaction times, are denoted asComputing formula is as follows:

${\hat{y}}_{n+1}=a_n+b_n+c_n$

Predictive coefficient a_n、b_n、c_nValue can be calculated by equation below:

$a_n=3{\hat{y}}_{n+1}^{\left(1\right)}-3{\hat{y}}_{n+1}^{\left(2\right)}+{\hat{y}}_{n+1}^{\left(3\right)}$

$b_n=\frac{\mathrm{\α}}{2{(1-\mathrm{\α})}^2}\[(6-5\mathrm{\α}){\hat{y}}_{n+1}^{\left(1\right)}-2(5-4\mathrm{\α}){\hat{y}}_{n+1}^{\left(2\right)}+(4-3\mathrm{\α}){\hat{y}}_{n+1}^{\left(3\right)}\]$

$c_n=\frac{{\mathrm{\α}}^2}{2{(1-\mathrm{\α})}^2}\[{\hat{y}}_{n+1}^{\left(1\right)}-2{\hat{y}}_{n+1}^{\left(2\right)}+{\hat{y}}_{n+1}^{\left(3\right)}\]$

Wherein:Be respectively once, secondary, Three-exponential Smoothing number, calculated by equation below Arrive:

${\hat{y}}_{n+1}^{\left(1\right)}=\mathrm{\α}\×y_n+(1-\mathrm{\α})\×{\hat{y}}_n^{\left(1\right)}$

${\hat{y}}_{n+1}^{\left(2\right)}=\mathrm{\α}\×{\hat{y}}_{n+1}^{\left(1\right)}+(1-\mathrm{\α})\×{\hat{y}}_n^{\left(2\right)}$

${\hat{y}}_{n+1}^{\left(3\right)}=\mathrm{\α}\×{\hat{y}}_{n+1}^{\left(2\right)}+(1-\mathrm{\α})\×{\hat{y}}_n^{\left(3\right)}$

It is the initial value of third index flatness, its value is

${\hat{y}}_0^{\left(1\right)}={\hat{y}}_0^{\left(2\right)}={\hat{y}}_0^{\left(3\right)}=\frac{y_1+y_2+y_3}{3}$

α is smoothing factor (0 ＜ α ＜ 1), embodies the time attenuation characteristic trusted, that is, from predictive value more close to timeslice y_tWeight is bigger, from predictive value more away from timeslice y_tWeight is less；Usually, if data fluctuations are larger, and long-term trend Amplitude of variation is larger, assumes α when substantially rapidly rising or falling trend and should take higher value (0.6～0.8), can increase in the recent period Data is to the impact predicting the outcome；When data has fluctuation, but long-term trend change little when, α can between 0.1～0.4 value； If steadily, α should take smaller value (0.05～0.20) to data fluctuations；

Calculating direct trust value:

Direct trust value td of node j_ijFor predicting interaction timesWith true interaction times y_n+1Relative error,

Step 2, calculates indirect trust values using calculating formula obtained from multipath trust recommendation mode；Using multipath The concrete calculation procedure that calculating formula obtained from trust recommendation mode calculates indirect trust values is:

The collection direct trust value to node j for the trusted node:

Node i meets td to all_ikThe credible associated nodes of≤φ inquire its direct trust value to node j, wherein φ For the believability threshold of recommended node, according to the precision prescribed of credibility, the span of φ is 0～0.4；

Calculating indirect trust values:

Trust value collected by COMPREHENSIVE CALCULATING, obtains indirect trust values tr of node j_ij,

Wherein, had with j node in the associated nodes for observer nodes i for the set (i) and interacted And its direct trust value meets td_ikThe node set of≤φ；

Step 3, draws comprehensive trust value by direct trust value and indirect trust values conformity calculation；Concrete calculation procedure is:

Comprehensive trust value (t_ij) computing formula as follows: t_ij=β td_ij+(1-β)tr_ij, wherein β (0≤β≤1) expression is directly Connect the weight of trust value, when β=0, node i and node j do not have a direct interaction relation, the calculating of comprehensive trust value directly from In indirect trust values, it is more objective to judge；When β=1, node i is to the comprehensive trust value of node j all from directly trusting Value, in this case, judges more subjective, Practical Calculation can determine the value of β as needed.

Further, described wireless transmitter module is provided with signal negentropy computing module, described signal negentropy computing module Signal negentropy computational methods include:

$\hat{j}\left(x\right)\≈\underset{j=1}{\overset{n}{\σ}}{k}_j{\[e\left\{g_j\left(x\right)\right\}-e\left\{g_j\left(m\right)\right\}\]}^2;$

Wherein, k_jFor some normal numbers, m is the gaussian variable with zero-mean, unit variance, function g_jFor non-secondary letter Number；

As all of g_jDuring=g, approximate expression becomes:

j_g(x)≈c[e{g(x)}-e{g(m)}]²；

Wherein, g is arbitrarily non-quadratic function, and c is a constant.

Further, described wireless transmitter module is provided with wireless network distributed module, the distributed mould of described wireless network The distributed method of block includes:

Step one, the neighbours of node si, the point position of covering, default network life l, the marking class of battery life bi, si Type upd, ii=1；

Step 2, judges whether ii ＜ l/l, if so, then directly carries out next step, no, then type, is labeled as lab Optimum working time of node arrange, then terminate；

Step 3, calculates maximum additionally effectively cover time and pro-jobs degree, and to neighbours broadcast mes (i, null, upd,δp_i)；

Step 4, judgesIf being, s_iδ p_iWhether maximum, if s in neighbours_iδ p_i Neighbours are maximum, s_iLabelling oneself is lab and broadcasts mes (i, lab, sch, δ p to neighbours_i)d_i=d_i-b_i, s_iexits.；If s_iδ p_iNeighbours are not maximum, judge s_iWhether receive neighbours s_kMes (k, lab, sch, δ p_k)；If s_iIt is to receive neighbours s_kMes (k, lab, sch, δ p_k), then s_iMore new neighbor s_kInformation, recalculate δp_i, and broadcast mes (i, upd, null, δ p to neighbours_i)；If s_iIt is not received by neighbours s_kMes (k, lab, sch, δ p_k), then, judge s_iWhether receive neighbours s_kMes (k, upd, null, δ p_k), if s_iIt is to receive neighbours s_kMes (k, upd,null,δp_k), s_iMore new neighbor s_kPro-jobs degree；If s_iIt is not received by neighbours s_kMes (k, upd, null, δ p_k), then return and judge

Further, described wireless receiving module is provided with radio net cooperative spectrum sensing module, described radio net The radio net cooperation frequency spectrum sensing method that frequency spectrum sensing module is made in complexation includes:

Step one, the node participating in cooperative sensing proceeds by the cycle for τ_sFrequency spectrum detection process, obtain primary user frequency The feature of spectrum resource；Feature according to primary user's signal and channel fading coefficient, calculate each local sensing node cr_iReceive Signal y_i(n)；According to energy measuring principle, obtain sensing node cr_iStatistic v of the signal energy at place_i, when sampling quantity is enough When big, v_iApproximate Gaussian distributed；

Step 2, normal sensing node and malice sensing node pass through orthogonal CCCH to data fusion center Carry out the report of perception information；Normal sensing node is faithfully reported the perception information of oneself, and malicious node then adopts false-alarm Attack mode is reported: when signal energy statistic v_iMore than attacking threshold value η, then faithfully report the sensing results of oneself； Otherwise will be with Probability p_aOffensive attack, sends a higher energy value to reach the mesh of malicious attack to data fusion center 's；

Step 3, data fusion center carries out data fusion, and the evil according to malicious node to the perception information collected Meaning attack mode calculates the false-alarm probability of the overall situation；Signal to noise ratio γ according to each node_iParticipate in the secondary of cooperative sensing for each Level user cr_i, i=1 ... k one weight of designThen signal energy statistic u collection being obtained_iEnter Row linear weighted function obtains the statistic of final signal energyAnalysis false-alarm malicious attack pattern is to frequency spectrum perception The impact causing, obtains overall false-alarm probability p_fWith attack Probability p_a, attack threshold value η, the function expression between attack strength δ As follows:

$p_f=q(\frac{\sqrt{{\mathrm{\σ}}_{i=1}^k{{\mathrm{\ω}}_i}^2(1+2{\mathrm{\γ}}_i)}}{q\left(p_d\right)}+\sqrt{{\mathrm{\τ}}_s{f}_s}({\mathrm{\σ}}_{i=1}^k{\mathrm{\ω}}_i{\mathrm{\γ}}_i+\frac{c_1-c_0}{{\mathrm{\σ}}_u^2}\left)\right)$

Wherein:

Step 4, secondary user's and primary user's share spectrum resources, if primary user is detected to be in not busy state, will be with High-power transmission signal, otherwise will be with small-power transmission signal；Secondary user's transmitter su-tx determining according to data fusion center Plan result adjusts the signal transmission power of oneself, if primary user pu is detected to be in not busy state, will be with high-power p₀Transmitting Signal；If it is primary user pu with power p that primary user pu is in busy condition_pTransmission signal, secondary user's transmitter su-tx will With small-power p₁Transmission signal；Therefore the average throughput in a time frame in secondary network is written as form:

$\begin{array}{c}r=e\{\frac{t-{\mathrm{\τ}}_s}{t}\[(1-p_f)p\left(h_0\right){\log }_2(1+\frac{g_{ss}{p}_0}{{\mathrm{\σ}}_u^2})+p_{f}p\left(h_0\right){\log }_2(1+\frac{g_{ss}{p}_1}{{\mathrm{\σ}}_u^2})\\ +\left(1-p_d\right)p\left(h_1\right){\log }_2\left(1+\frac{g_{ss}{p}_0}{h_k{p}_p+{\mathrm{\σ}}_u^2}\right)+p_{d}p\left(h_1\right){\log }_2\left(1+\frac{g_{ss}{p}_1}{h_k{p}_p+{\mathrm{\σ}}_u^2}\right)\]\}\end{array}---\left(1\right)$

Wherein: h_k,g_ssIt is primary user transmitter pu-tx respectively to secondary user's transmitter su-tx secondary user's transmitter Channel fading coefficient between su-tx, secondary user's transmitter su-tx to secondary user's receiver su-rx, p (h₀) and p (h₁) Represent that primary user pu is practically in the probability of not busy state and busy condition respectively；

Step 5, builds Optimized model, determines the constraints about transmission power and detecting period, and solution is set up Optimization problem, obtains so that the handling capacity of the secondary network maximum perception cycle of cooperative sensing and the signal of secondary user's are sent out Penetrate power；Ensure that secondary user's network can work long hours, the transmission power of secondary user's need to be limited it is ensured that secondary use The average emitted power of family network is less than limit value:

e{α₀p₀+α₁p₁+β₀p₀+β₁p₁}≤p_av(2)

P in formula_avThe maximum average emitted power of secondary user's transmitter su-tx, this averagely refer to fading channel system Number h_i,g_ss,g_spThe expectation of stochastic variable；The top priority of cognitive radio networks is the service quality of protection primary user's network, Therefore the jamming power of network is limited；According to the spectrum sharing network model based on cooperative sensing it is known that interference only exists Primary user pu is in and occurs during busy condition, so average interference power constraint is written as form:

e{g_sp(β₀p₀+β₁p₁)}≤q_av(3)

Guarantee that the detection probability at each node and the whole detection probability of network are not less than respective target detection respectively Probability, the restrictive condition with regard to detection probability is as follows:

p_d≥p_th,p_di≥p_th, i=1,2 ... k (4)

According under above-mentioned restrictive condition, set up the optimization with the average throughput maximizing secondary network as object function Problem:

$\begin{array}{cc}\underset{\left\{{\mathrm{\τ}}_s,\mathrm{\ϵ},\left\{{\mathrm{\ϵ}}_i\right\},p_0,p_1\right\}}{\max }& r\\ subjectto& \left(2\right),\left(3\right),\left(4\right),p_0\&greaterequal;0,p_1\&greaterequal;0\\ & 0\≤{\mathrm{\τ}}_s\≤t\end{array}---\left(\mathrm{pr}oblem1\right)$

Solve set up optimization problem, select to make the perception week of the cooperative sensing of handling capacity maximum of secondary network The signal transmission power of phase and secondary user's is as the perceptual parameters of this frequency spectrum perception model；

Step 6, repetitive cycling step one is to step 5, until completing emulation experiment 1000 times, to the optimum obtaining every time Solution is averaged, using meansigma methodss as the perceptual parameters of frequency spectrum perception model.

User only needs to move control rifle in the air, vertically and horizontally four direction translation, on screen mouse then press than Example is with mobile it is possible to be rapidly converted to onscreen cursor control.Rifle operation is controlled to press the will precise control of user completely, Hand-held comfortable feel, convenient, control sensitivity height.

Using virtual three-dimensional scene controller of the present invention, by computer operating system drive software, both can calculate Three-dimensional scenic picture is realized on machine control it is also possible to realize two-dimensional picture control on computers as common air mouse.

The virtual three-dimensional scene controller of the present invention integrates wireless mouse, keypad function, and 360 degree of no dead angles are any It is achieved that the comprehensive control in Virtual Space and roaming, pistol type design meets holds custom, can exchange behaviour with right-hand man for manipulation Make, 20 meters of coverage, support gesture manipulation, support large-scale somatic sensation television game, the operation meeting 3d virtual scene requires.

The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all essences in the present invention Any modification, equivalent and improvement made within god and principle etc., should be included within the scope of the present invention.

Claims (5)

1. a kind of virtual three-dimensional scene controller is it is characterised in that described virtual three-dimensional scene controller includes controlling rifle and connects Receive device, described control rifle includes gun body, and described gun body is provided with trigger-type button and navigation direction key, described trigger-type presses key mapping In the front of gun body, described navigation direction key mapping is in the rearward end of gun body；

First circuit board and lithium battery are installed in described gun body inner chamber, described first circuit board is provided with three-axis gyroscope, Three axis accelerometer module, a mcu, wireless transmitter module, described three-axis gyroscope, three axis accelerometer module and wireless Penetrate module to be connected with a described mcu respectively；

Described receptor includes wireless receiving module, and one end of described wireless receiving module is provided with usb interface, described wireless receiving Second circuit board is installed in module internal cavity, described second circuit board is provided with the 2nd mcu and coupled wireless receiving Module；

Described gun body is being provided with, positioned at small of the stock lower end, the extension being connected with it, and described lithium battery is arranged on described extension Inner chamber in, described lithium battery inline power management module, described lithium battery is through described power management module and a described mcu It is connected；The rear end of extension is provided with charging inlet, and described charging inlet is connected with described power management module；

Described wireless receiving module is provided with display lamp, and described display lamp is connected with described 2nd mcu；

Navigation direction key is rolling ball structure, is controlled with thumb, realize respectively up and down, left and right 4 directions press and 1 Directly press totally 5 keypress functions to control.

2. virtual three-dimensional scene controller as claimed in claim 1 is it is characterised in that the setting of described wireless transmitter module has or not Line mesh network node trust value computing unit, the wireless mesh network of described wireless mesh network node trust value computing unit The trust value computing method of node comprises the following steps:

Step one, the interaction times of different time piece between acquisition node, according to the data setup time sequence obtaining, by three times Exponential smoothing, to predict the interaction times of next timeslice between node, misses relative with actual value for interaction times predictive value Difference is as the direct trust value of node；The concrete calculation procedure of direct trust value is:

The interaction times of n timeslice between collection network observations node i and node j:

Choose intervals t as an observation time piece, with observer nodes i and tested node j in 1 timeslice Interaction times as observation index, true interaction times, be denoted as y_t, record the y of n timeslice successively_n, and save it in section In the communications records table of point i；

The interaction times of (n+1)th timeslice of prediction:

According to the interaction times setup time sequence of the n timeslice collecting, predicted next using third index flatness Interaction times between timeslice n+1 interior nodes i and j, predict interaction times, are denoted asComputing formula is as follows:

${\hat{y}}_{n+1}=a_n+b_n+c_n$

Predictive coefficient a_n、b_n、c_nValue can be calculated by equation below:

$a_n=3{\hat{y}}_{n+1}^{\left(1\right)}-3{\hat{y}}_{n+1}^{\left(2\right)}+{\hat{y}}_{n+1}^{\left(3\right)}$

$b_n=\frac{\mathrm{\α}}{2{(1-\mathrm{\α})}^2}\[(6-5\mathrm{\α}){\hat{y}}_{n+1}^{\left(1\right)}-2(5-4\mathrm{\α}){\hat{y}}_{n+1}^{\left(2\right)}+(4-3\mathrm{\α}){\hat{y}}_{n+1}^{\left(3\right)}\]$

$c_n=\frac{{\mathrm{\α}}^2}{2{(1-\mathrm{\α})}^2}\[{\hat{y}}_{n+1}^{\left(1\right)}-2{\hat{y}}_{n+1}^{\left(2\right)}+{\hat{y}}_{n+1}^{\left(3\right)}\]$

Wherein:Be respectively once, secondary, Three-exponential Smoothing number, be calculated by equation below:

${\hat{y}}_{n+1}^{\left(1\right)}=\mathrm{\α}\×y_n+(1-\mathrm{\α})\×{\hat{y}}_n^{\left(1\right)}$

${\hat{y}}_{n+1}^{\left(2\right)}=\mathrm{\α}\×{\hat{y}}_{n+1}^{\left(1\right)}+(1-\mathrm{\α})\×{\hat{y}}_n^{\left(2\right)}$

${\hat{y}}_{n+1}^{\left(3\right)}=\mathrm{\α}\×{\hat{y}}_{n+1}^{\left(2\right)}+(1-\mathrm{\α})\×{\hat{y}}_n^{\left(3\right)}$

It is the initial value of third index flatness, its value is

${\hat{y}}_0^{\left(1\right)}={\hat{y}}_0^{\left(2\right)}={\hat{y}}_0^{\left(3\right)}=\frac{y_1+y_2+y_3}{3}$

α is smoothing factor (0 ＜ α ＜ 1), embody trust time attenuation characteristic, that is, from predictive value more close to timeslice y_tWeight Bigger, from predictive value more away from timeslice y_tWeight is less；Usually, if data fluctuations are larger, and long-term trend change Amplitude is larger, assumes α when substantially rapidly rising or falling trend and should take higher value (0.6～0.8), can increase Recent data To the impact predicting the outcome；When data has fluctuation, but when long-term trend change is little, α is in 0.1～0.4 value；If data wave Dynamic steady, α takes 0.05～0.20；

Calculating direct trust value:

Direct trust value td of node j_ijFor predicting interaction timesWith true interaction times y_n+1Relative error,

Step 2, calculates indirect trust values using calculating formula obtained from multipath trust recommendation mode；Trusted using multipath The concrete calculation procedure that calculating formula obtained from the way of recommendation calculates indirect trust values is:

The collection direct trust value to node j for the trusted node:

Node i meets td to all_ikThe credible associated nodes of≤φ inquire its direct trust value to node j, and wherein φ is to push away Recommend the believability threshold of node, according to the precision prescribed of credibility, the span of φ is 0～0.4；

Calculating indirect trust values:

Trust value collected by COMPREHENSIVE CALCULATING, obtains indirect trust values tr of node j_ij,Its In, set (i) be observer nodes i associated nodes in had with j node and to interact and its direct trust value meets td_ikThe section of≤φ Point set；

Step 3, draws comprehensive trust value by direct trust value and indirect trust values conformity calculation；Concrete calculation procedure is:

Comprehensive trust value (t_ij) computing formula as follows: t_ij=β td_ij+(1-β)tr_ij, wherein β (0≤β≤1) expression directly letter Appoint the weight of value, when β=0, node i and node j do not have direct interaction relation, between the calculating of comprehensive trust value arises directly from Connect trust value, it is more objective to judge；When β=1, node i to the comprehensive trust value of node j all from direct trust value, In this case, judge more subjective, Practical Calculation determines the value of β as needed.

3. virtual three-dimensional scene controller as claimed in claim 1 is it is characterised in that described wireless transmitter module is provided with letter Number negentropy computing module, the signal negentropy computational methods of described signal negentropy computing module include:

$\hat{j}\left(x\right)\≈\underset{j=1}{\overset{n}{\σ}}{k}_j{\[e\left\{g_j\left(x\right)\right\}-e\left\{g_j\left(m\right)\right\}\]}^2;$

Wherein, k_jFor some normal numbers, m is the gaussian variable with zero-mean, unit variance, function g_jFor non-quadratic function；

As all of g_jDuring=g, approximate expression becomes:

j_g(x)≈c[e{g(x)}-e{g(m)}]²；

Wherein, g is arbitrarily non-quadratic function, and c is a constant.

4. virtual three-dimensional scene controller as claimed in claim 1 is it is characterised in that the setting of described wireless transmitter module has or not Gauze network distributed module, the distributed method of described wireless network distributed module includes:

Step one, the neighbours of node si, the point position of covering, default network life l, the type of battery life bi, si Upd, ii=1；

Step 2, judges whether ii ＜ l/l, if so, then directly carries out next step, no, then type, is labeled as the section of lab The optimum working time of point arranges, and then terminates；

Step 3, calculates maximum additionally effective cover time and pro-jobs degree, and broadcasts mes (i, null, upd, δ to neighbours p_i)；

Step 4, judgesIf being, s_iδ p_iWhether maximum, if s in neighbours_iδ p_iIn neighbour It is maximum between two parties, s_iLabelling oneself is lab and broadcasts mes (i, lab, sch, δ p to neighbours_i)d_i=d_i-b_i,s_iexits.；If s_iδ p_iNeighbours are not maximum, judge s_iWhether receive neighbours s_kMes (k, lab, sch, δ p_k)；If s_iIt is to receive To neighbours s_kMes (k, lab, sch, δ p_k), then s_iMore new neighbor s_kInformation, recalculateδp_i, and to neighbours Broadcast mes (i, upd, null, δ p_i)；If s_iIt is not received by neighbours s_kMes (k, lab, sch, δ p_k), then, judge s_iIt is No receive neighbours s_kMes (k, upd, null, δ p_k), if s_iIt is to receive neighbours s_kMes (k, upd, null, δ p_k), s_iMore new neighbor s_kPro-jobs degree；If s_iIt is not received by neighbours s_kMes (k, upd, null, δ p_k), then return and judge

5. virtual three-dimensional scene controller as claimed in claim 1 is it is characterised in that the setting of described wireless receiving module has or not Line electric network cooperative spectrum sensing module, the radio net cooperation frequency spectrum sense of described radio net cooperative spectrum sensing module Perception method includes:

Step one, the node participating in cooperative sensing proceeds by the cycle for τ_sFrequency spectrum detection process, obtain primary user's frequency spectrum resource Feature；Feature according to primary user's signal and channel fading coefficient, calculate each local sensing node cr_iThe signal y receiving_i (n)；According to energy measuring principle, obtain sensing node cr_iStatistic v of the signal energy at place_i, when sampling quantity is sufficiently large, v_i Approximate Gaussian distributed；

Step 2, normal sensing node and malice sensing node are carried out to data fusion center by orthogonal CCCH The report of perception information；Normal sensing node is faithfully reported the perception information of oneself, and malicious node then adopts false-alarm to attack Pattern is reported: when signal energy statistic v_iMore than attacking threshold value η, then faithfully report the sensing results of oneself；Otherwise Will be with Probability p_aOffensive attack, sends a higher energy value to reach the purpose of malicious attack to data fusion center；

Step 3, data fusion center carries out data fusion to the perception information collected, and attacks according to the malice of malicious node Blow mode calculates the false-alarm probability of the overall situation；Signal to noise ratio γ according to each node_iThe secondary participating in cooperative sensing for each is used Family cr_i, i=1 ... k one weight of designThen signal energy statistic u collection being obtained_iEnter line Property weighting obtain the statistic of final signal energyAnalysis false-alarm malicious attack pattern causes to frequency spectrum perception Impact, obtain overall false-alarm probability p_fWith attack Probability p_a, attack threshold value η, the function expression between attack strength δ such as Under:

$p_f=q(\frac{\sqrt{{\σ}_{i=1}^k{{\mathrm{\ω}}_i}^2(1+2{\mathrm{\γ}}_i)}}{q\left(p_d\right)}+\sqrt{{\mathrm{\τ}}_s{f}_s}({\σ}_{i=1}^k{\mathrm{\ω}}_i{\mathrm{\γ}}_i+\frac{c_1-c_0}{{\mathrm{\σ}}_u^2}\left)\right)$

Wherein:

Step 4, secondary user's and primary user's share spectrum resources, if primary user is detected to be in not busy state, will be with big work( Rate transmission signal, otherwise will be with small-power transmission signal；Secondary user's transmitter su-tx is according to the decision-making knot of data fusion center Fruit adjusts the signal transmission power of oneself, if primary user pu is detected to be in not busy state, will be with high-power p₀Transmission signal； If it is primary user pu with power p that primary user pu is in busy condition_pTransmission signal, secondary user's transmitter su-tx will be with little Power p₁Transmission signal；Therefore the average throughput in a time frame in secondary network is written as form:

$\begin{array}{c}r=e\{\frac{t-{\mathrm{\τ}}_s}{t}\[(1-p_f)p\left(h_0\right){\log }_2(1+\frac{g_{ss}{p}_0}{{\mathrm{\σ}}_u^2})+p_{f}p\left(h_0\right){\log }_2(1+\frac{g_{ss}{p}_1}{{\mathrm{\σ}}_u^2})\\ +(1-p_d)p\left(h_1\right){\log }_2(1+\frac{g_{ss}{p}_0}{h_k{p}_p+{\mathrm{\σ}}_u^2})+p_{d}p\left(h_1\right){\log }_2(1+\frac{g_{ss}{p}_1}{h_k{p}_p+{\mathrm{\σ}}_u^2})\]\}\end{array};$

Wherein: h_k,g_ssIt is primary user transmitter pu-tx to secondary user's transmitter su-tx secondary user's transmitter su- respectively Channel fading coefficient between tx, secondary user's transmitter su-tx to secondary user's receiver su-rx, p (h₀) and p (h₁) respectively Represent that primary user pu is practically in the probability of not busy state and busy condition；

Step 5, builds Optimized model, determines the constraints about transmission power and detecting period, solves set up optimum Change problem, obtains making the perception cycle of cooperative sensing of handling capacity maximum of secondary network and the signal transmitting work(of secondary user's Rate；Ensure that secondary user's network can work long hours, the transmission power of secondary user's need to be limited it is ensured that secondary user's net The average emitted power of network is less than limit value:

e{α₀p₀+α₁p₁+β₀p₀+β₁p₁}≤p_av；

P in formula_avThe maximum average emitted power of secondary user's transmitter su-tx, this averagely refer to channel fading coefficient h_i, g_ss,g_spThe expectation of stochastic variable；The top priority of cognitive radio networks is the service quality of protection primary user's network, therefore right The jamming power of network is limited；According to the spectrum sharing network model based on cooperative sensing it is known that interference is only primary Family pu is in and occurs during busy condition, so average interference power constraint is written as form:

e{g_sp(β₀p₀+β₁p₁)}≤q_av；

Guarantee that the detection probability at each node and the whole detection probability of network are not less than respective target detection probability respectively, Restrictive condition with regard to detection probability is as follows:

p_d≥p_th,p_di≥p_th, i=1,2 ... k；

Ask according under above-mentioned restrictive condition, setting up the optimization with the average throughput maximizing secondary network as object function Topic:

$\underset{\{{\mathrm{\τ}}_s,\mathrm{\ϵ},\{{\mathrm{\ϵ}}_i\},p_0,p_1\}}{max}r$

subject to(2),(3),(4),p₀≥0,p₁≥0；

0≤τ_s≤t

Solve set up optimization problem, select to make secondary network handling capacity maximum perception cycle of cooperative sensing and The signal transmission power of secondary user's is as the perceptual parameters of this frequency spectrum perception model；

Step 6, repetitive cycling step one, to step 5, until completing emulation experiment 1000 times, takes to the optimal solution obtaining every time Averagely, using meansigma methodss as the perceptual parameters of frequency spectrum perception model.