CN107951491A

CN107951491A - A kind of novel visual motion tracking training system

Info

Publication number: CN107951491A
Application number: CN201710879402.7A
Authority: CN
Inventors: 王欣
Original assignee: Qilu Normal University
Current assignee: Qilu Normal University
Priority date: 2017-09-26
Filing date: 2017-09-26
Publication date: 2018-04-24

Abstract

The invention belongs to visual exercise technical field, discloses a kind of novel visual motion tracking training system and is provided with for detection data and the main control module for training equipment to be controlled；For catching the eye motion capture module of eye motion data message by eye tracker；Eye test module for the eyesight number of degrees that user is obtained by vision tester for eyesight；For the display module for showing the data message of eye motion capture module, the acquisition of eye test module；For training equipment to carry out the trick brain training module to trick brain progress exercise for coordination by trick brain.Exercise for coordination can be carried out to the hand, eye, brain of user by trick brain training module, strengthen training effect；The eyesight of user can be checked by eye test module at the same time, and training program is formulated according to eyesight, there is specific aim, can be with fast lifting training effect.

Description

A kind of novel visual motion tracking training system

Technical field

The invention belongs to visual exercise technical field, more particularly to a kind of novel visual motion tracking training system.

Background technology

Vision is one of the important channel in the human cognitive world, and the 80% of mankind's acquisition external information comes from vision system. Computation vision is exactly on the basis of human vision is understood, and human sight apparatus is replaced with imaging system, and human brain is replaced with level is calculated Complete the processing and understanding to input picture.Meanwhile with the development of information technology and intelligence science, computer vision is artificial One of smart field Hot subject and one of thing network sensing layer important technology.However, existing visual movement training system, instruction Practice single to visual exercise, it is impossible to carry out exercise for coordination with hand brain, training overall effect is poor；At the same time without to user's The eyesight number of degrees are detected, and cause eyesight strong identical with weak-eyed training program, poor without specific aim, training effect.

In conclusion problem existing in the prior art is：Existing visual movement training system, single of training is to vision Training, it is impossible to carry out exercise for coordination with hand brain, training overall effect is poor；Examined at the same time without the eyesight number of degrees to user Survey, cause eyesight strong identical with weak-eyed training program, it is poor without specific aim, training effect.

The content of the invention

In view of the problems of the existing technology, the present invention provides a kind of novel visual motion tracking training system.

The present invention is achieved in that a kind of novel visual motion tracking training system, the novel visual motion tracking Training system is provided with the main control module for being controlled to detection data and training equipment；

The main control module is to frequency-hopping mixing signal time-frequency domain matrixLocated in advance Reason, specifically includes following two step：

The first step is rightCarry out low energy to pre-process, i.e., in each sampling instant P, willValue of the amplitude less than thresholding ε is set to 0, and is obtainedThe setting of thresholding ε can be determined according to the average energy for receiving signal；

Second step, finds out the time-frequency numeric field data of p moment (p=0,1,2 ... P-1) non-zero, usesRepresent, whereinRepresent the response of p moment time-frequencyCorresponding frequency indices when non-zero, normalize these non-zeros and pre-process, obtain Pretreated vector b (p, q)=[b₁(p, q), b₂(p, q) ..., b_M(p, q)]^T, wherein

The main control module splices the time-frequency domain frequency hopping source signal between different frequency hopping points, comprises the following steps that：

The first step, estimation l jump correspondingA incident angle, is usedRepresent l jump n-th of source signal it is corresponding enter Firing angle degree,Calculation formula it is as follows：

Represent that l jumps n-th of hybrid matrix column vector that estimation obtainsM-th of element, c represent light Speed, i.e. v_c=3 × 10⁸Meter per second；

Second step, judges that l (l=2,3 ...) jumps the source signal of estimation and first and jumps pair between the source signal of estimation It should be related to, judgment formula is as follows：

Wherein m_n ^(l)Represent that l jumps the m of estimation_n ^(l)A signal and first n-th of signal for jumping estimation belong to same source Signal；

3rd step, by different frequency hopping point estimation to the signal for belonging to same source signal be stitched together, as final Time-frequency domain source signal estimation, use Y_n(p, q) represents time-frequency domain estimate of n-th of source signal in time frequency point (p, q), p= 0,1,2 ..., P, q=0,1,2 ..., N_fft-1：

The main control module docks the Short Time Fourier Transform that received LFM signals do linear domain of holomorphy, obtains linearly just The then Short Time Fourier Transform spectrum in domain；

The Short Time Fourier Transform for receiving the linear domain of holomorphy of LFM signals is carried out as follows：

1.1) LFM signal models are expressed as：

Wherein, A₀For amplitude, t is the time；f₀For original frequency, k is frequency modulation rate, and j is imaginary unit；

1.2) Short Time Fourier Transform of the linear domain of holomorphy of LFM signal f (t) is defined as follows：

L_A(t, f)=∫ f (t+ τ) h* (τ) K_A(f, τ) d τ；

Wherein, (t, f) is the point on time-frequency domain,For the parameter of linear canonical transform, and ad-bc=1, h (t) it is window function, uses Gaussian window in the present invention, h* (t) is the conjugation of h (t), and τ is substitution of variable；In addition, also have：

1.3) it is as follows to define Gauss function h (t)：

Wherein, α is the parameter for controlling window width, and window function substitutes into：

Then：

It can obtain：

So as to obtain the Short Time Fourier Transform of the linear domain of holomorphy of signal spectrum：

Hough transform is done to the Short Time Fourier Transform spectrum of the linear domain of holomorphy of obtained LFM signals, obtains Hough changes Change matrix；Hough transform is done to the Short Time Fourier Transform spectrum of the linear domain of holomorphy of signal to be carried out as follows：

First, polar equation is ρ=tcos θ+fsin θ, wherein, (t, f) is the point on time-frequency domain, and ρ is the point to original Point distance, θ was the point and origin straight line and the angle of x-axis, and polar coordinate space (ρ, θ) is quantified as (ρ_u, θ_v), u=1 ..., M, v=1 ..., N, obtain the two-dimensional matrix M (ρ, θ) of M × N, and M (ρ, θ) is an accumulator, initial value 0；

Then each point (t, f) on time-frequency domain is corresponded to, its spectral amplitude is | L_A(t, f) |², to improve calculating speed, setting When the spectral amplitude of some point is more than the maximum of the spectral amplitude of all the pointsWhen then carry out Hough transform, otherwise neglect this Point；

Finally to meeting that spectral amplitude is more than the maximum of the spectral amplitude of all the pointsPoint (t, f), by all quantized values of θ Polar equation is substituted into, obtains corresponding ρ, and accumulator is added | L_A(t, f) |², i.e. M (ρ, θ)=M (ρ, θ)+| L_A(t, f) |², obtain Hough transform matrix M (ρ, θ)；

The matrix after Hough transform is traveled through using two-dimentional sliding window, and energy accumulation is done in window, so as to obtain Test statistics；The matrix after Hough transform is traveled through with two-dimentional sliding window, and do in window energy accumulation by below into OK：

The length for setting two dimension sliding window P (m, n) first is L, width K, wherein, m and n represent two-dimentional sliding window respectively Abscissa and ordinate；The length that the Hough transform matrix obtained in setting steps S2 is M (ρ, θ) is M, width N, then Hough transform matrix is divided intoBlock, whereinRepresent downward rounding；

Then calculating Hough transform matrix points respectively is（L, K), (2L, K) ..., (pL, K),（L, 2K), (2L, 2K) ...,（PL, 2K) ..., the energy of (pL, qK) place window P (m, n) and, obtain the test statistics Q (m, n) of p × q, its count Calculation method is as follows：

M=1,2 ..., p, n=1,2 ..., q；

Optimal decision threshold is obtained according to recipient's operating characteristic curve (ROC), by by optimal decision threshold with Obtained test statistics is compared, and LFM signals are detected；

It is electrically connected with main control module, the eye motion for catching eye motion data message by eye tracker catches mould Block；

The eye motion capture module estimates the jumping moment of each jump using clustering algorithm and respectively jumps corresponding return When the one hybrid matrix column vector changed, Hopping frequencies, comprise the following steps：

The first step, p (p=0,1,2 ... P-1) moment, it is rightThe frequency values of expression are clustered, in obtained cluster Heart numberRepresent carrier frequency number existing for the p moment,A cluster centre then represents the size of carrier frequency, uses respectivelyRepresent；

Second step, to each sampling instant p (p=0,1,2 ... P-1), utilize clustering algorithm pairGathered Class, it is same availableA cluster centre, is usedRepresent；

3rd step, to allAverage and rounding, obtain the estimation of source signal numberI.e.

4th step, finds outAt the time of, use p_hRepresent, to the p of each section of continuous value_hIntermediate value is sought, is usedRepresent the l sections of p that are connected_hIntermediate value, thenRepresent the estimation at l-th of frequency hopping moment；

5th step, obtains according to estimation in second stepAnd the 4th estimate to obtain in step The frequency hopping moment estimate it is each jump it is correspondingA hybrid matrix column vectorSpecifically formula is：

HereIt is corresponding to represent that l is jumpedA mixing Matrix column vector estimate；

6th step, estimates the corresponding carrier frequency of each jump, usesIt is corresponding to represent that l is jumpedA frequency estimation, calculation formula are as follows：

It is electrically connected with main control module, the eye test module of the eyesight number of degrees for obtaining user by vision tester for eyesight；

It is electrically connected with main control module, for the data message of eye motion capture module, the acquisition of eye test module to be shown The display module come is shown；

The normalization hybrid matrix column vector estimation time-frequency domain frequency hopping source signal that the display module is estimated, specific step It is rapid as follows：

The first step, judges which moment index belongs to and jump, specific method is to all sampling instants index p：IfThen represent that moment p belongs to l jumps；IfThen represent that moment p belongs to the 1st Jump；

Second step, all moment p jumped to l (l=1,2 ...)_l, estimate the time-frequency domain number of each frequency hopping source signal of the jump According to calculation formula is as follows：

It is electrically connected with main control module, for training equipment to carry out the trick to trick brain progress exercise for coordination by trick brain Brain training module.

Further, the novel visual motion tracking training system is additionally provided with platform, the left side welding peace of the platform Equipped with camera device, controller is provided with the right side of the camera device；

Slide is provided with the middle part of the platform, adjustable plate is movably installed with the slide；The right end weldering of the platform Connect and stent is installed, the upper end of the stent is installed by welding with connecting rod, and the upper end of the connecting rod is installed by welding with observation dress Put；The lower end of the stent is lifted with the image collecting device being connected with adjustable plate, and described image harvester is filled with observation Put electrical connection.

Further, the surface of the platform is provided with the graduated scale to match with slide, and card slot is provided with the slide, The lower end of the adjustable plate is provided with the buckle to match with card slot.

Further, the surface of the platform is equipped with solar panels, and the lower end of the table top is provided with storage battery；It is described too Positive energy plate is electrically connected with storage battery, and the storage battery is electrically connected with the controller.

Further, the eye motion capture module records the absolute acceleration of measuring point using accelerometer, and Using feedback data of the data obtained as control system, force cell will be formulated on each control device, each to accelerate The sensitiveness index of degree meter is taken as：S_a=10V/g=10V/9.81ms^-2, the sensitiveness index of each force snesor is taken as S_f= 10V/1000kN；Using MR damper as control device, by controlling magnetic field magnetic rheological body is realized in Millisecond Reversible change between the viscous fluid and semisolid of free-flowing；

Step 2, the simulation of damping is carried out using the spencer models of MR damper；Damping force is carried out with following formula Calculate

In formula：k₁For the accumulator rigidity of MR damper, k₀For high speed when control rigidity, x₀For spring k₁Just Beginning displacement, c₀For viscous damping coefficient of speed when larger, c₁For adhesive elements, during for producing low speed in power-length velocity relation Decay, A, beta, gamma, n is constant, and value is determined by MR damper architectural characteristic,

U is the magnitude of voltage on MR damper when producing corresponding damping force, and voltage, which can lag behind, calculates desired value u_c, need Correct：

Step 3, the design of active control is carried out using LQR/LQG control laws, and LQG controllers are increased by Optimal state-feedback Benefit and Kalman filter two parts composition, Optimal state-feedback gain is tried to achieve using classical linear optimal active control algorithm LQR；

Step 4, makes semi-automatic control device reach approximately actively optimal control with reference to MR damper spy and analysis on aqueduct structure The effect of power processed, designs semi-active control law；

Control law can be described as：

u_c=U (f) * H { (f_c-f)f}

Wherein, U (f) is to characterize voltage and the continuous function of power, corresponding with relative velocity hysteretic loop top intersection point using power Power as characterization, simulated with 0.1v incremental voltages, obtain the discrete relationship of power and voltage；

Step 5, analysis on aqueduct structure space power is calculated using the finite segment method；

Step 6, by the form and size of tentative calculation Optimal Control Force process, constantly adjustment weight matrix Q and R, to obtain Control effect and controling power integrate optimal active controlling force.

The present invention can carry out exercise for coordination by trick brain training module to the hand, eye, brain of user, strengthen training effect Fruit；It can check the eyesight of user, and training program formulated according to eyesight have by eye test module at the same time Targetedly, can be with fast lifting training effect.It can be pressed after long-time visual exercise by the eyes on optometry table stent The instrument that rubs eye is massaged, and can eliminate the malaise symptoms in training process with relieving eye strain；Meanwhile in training During user side can be facilitated to train, while checking instruction by projecting apparatus by the front of training information data projection to user Practice data, user can correct trained operation according to training data as early as possible.Synchronized orthogonal frequency hopping letter of the present invention based on cluster Number blind source separation method, under conditions of any channel information is not known, according only to the mixing of the multiple Frequency Hopping Signals received Signal, estimates frequency hopping source signal, can reception antenna number be less than source signal number under conditions of, to multiple Frequency Hopping Signals into Row blind estimate, with only Short Time Fourier Transform, and calculation amount is small, easy to implement, and this method is blind to Frequency Hopping Signal progress While separation, moreover it is possible to partial parameters are estimated, it is highly practical, there is stronger popularization and application value, improve instruction Experienced effect.

Brief description of the drawings

Fig. 1 is novel visual motion tracking training system structure diagram provided in an embodiment of the present invention；

Fig. 2 is the external structure schematic diagram of novel visual motion tracking training system provided in an embodiment of the present invention；

In figure：1st, eye motion capture module；2nd, eye test module；3rd, trick brain training module；4th, main control module；5、 Display module；6th, device is observed；7th, adjustable plate；8th, slide；9th, image collecting device；10th, platform；11st, camera device；12nd, control Device processed；13rd, stent；14th, connecting rod.

Embodiment

In order to further understand the content, features and effects of the present invention, the following examples are hereby given, and coordinate attached drawing Describe in detail as follows.

The structure of the present invention is explained in detail below in conjunction with the accompanying drawings.

As shown in Figure 1, novel visual motion tracking training system provided in an embodiment of the present invention is provided with for detection The main control module 4 that data and training equipment are controlled；

It is electrically connected with main control module 4, the eye motion for catching eye motion data message by eye tracker catches mould Block 1；

It is electrically connected with main control module 4, the eye test module 2 of the eyesight number of degrees for obtaining user by vision tester for eyesight；

It is electrically connected with main control module 4, for the data message for obtaining eye motion capture module 1, eye test module 2 The display module 5 shown；

It is electrically connected with main control module 4, for training equipment to carry out the hand to trick brain progress exercise for coordination by trick brain Eye brain training module 3.

As the preferred embodiment of the present invention, as shown in Fig. 2, novel visual motion tracking training system be additionally provided with it is flat Platform, the left side of platform 10 are installed by welding with camera device 11, and the right side of camera device 11 is provided with controller 12；

The middle part of platform 10 is provided with slide 8, and adjustable plate 7 is movably installed with slide 8；The right end welded and installed of platform 10 There is stent 13, the upper end of stent 13 is installed by welding with connecting rod 14, and the upper end of connecting rod 14 is installed by welding with observation device 6；Branch The lower end of frame 13 is lifted with the image collecting device 9 being connected with adjustable plate 7, and image collecting device 9 is electrically connected with observation device 6 Connect.

As the preferred embodiment of the present invention, the surface of platform 10 is provided with the graduated scale to match with slide 8, slide 8 Card slot is inside provided with, the lower end of adjustable plate 7 is provided with the buckle to match with card slot.

As the preferred embodiment of the present invention, the surface of platform 10 is equipped with solar panels, and the lower end of table top is provided with storage Battery；Solar panels are electrically connected with storage battery, and storage battery is electrically connected with controller 12.

As the preferred embodiment of the present invention, eye motion capture module 1 accelerates the absolute of measuring point using accelerometer Degree is recorded, and using feedback data of the data obtained as control system, force cell will be formulated in each control dress Put, the sensitiveness index of each accelerometer is taken as：S_a=10V/g=10V/9.81ms^-2, the sensitiveness of each force snesor Index is taken as S_f=10V/1000kN；Using MR damper as control device, by controlling magnetic field magnetic rheological body is existed The reversible change between the viscous fluid flowed freely and semisolid is realized in Millisecond；

Step 3, the design of active control is carried out using LQR/LQG control laws, LQG controllers 12 are by Optimal state-feedback Gain and Kalman filter two parts composition, Optimal state-feedback gain is tried to achieve using classical linear optimal active control algorithm LQR；

Control law can be described as：

u_c=U (f) * H { (f_c-f)f}

3rd step, by different frequency hopping point estimation to the signal for belonging to same source signal be stitched together, as final Time-frequency domain source signal estimation, use Y_n(p, q) represents time-frequency domain estimate of n-th of source signal in time frequency point (p, q), p= 0,1,2 ..., P, q=0,1,2 ..., N_ft-1：

1.1) LFM signal models are expressed as：

L_A(t, f)=∫ f (t+ τ) h* (τ) K_A(f, τ) d τ；

1.3) it is as follows to define Gauss function h (t)：

Then：

It can obtain：

Then it is (L, K) to calculate Hough transform matrix points respectively, (2L, K) ..., (pL, K), (L, 2K), (2L, 2K) ..., (pL, 2K) ..., the energy of (pL, qK) place window P (m, n) and, obtain the test statistics Q (m, n) of p × q, its count Calculation method is as follows：

M=1,2 ..., p, n=1,2 ..., q；

5th step, obtains according to estimation in second stepp≠p_hAnd the 4th estimate to obtain in step The frequency hopping moment estimate it is each jump it is correspondingA hybrid matrix column vectorSpecifically formula is：

The present invention is detected the movement locus of adjustable plate 7 by image collecting device, and will detect signal transmission extremely Device 6 is observed, the height and position for observing device 6 can be adjusted by stent 13；Camera device 11 is shot with video-corder content and can be led to Controller 12 is crossed to be adjusted.Eye test module 2 checks the eyesight of user, and sends the data to main control module 4, main control module 4 formulates adaptable training program by trick brain training module 3 according to the strong and weak of eyesight；User is in training During, eye motion capture module 1 can catch user's eye, and data message is carried out by display module 5 It has been shown that, user can be according to optical data come adjusting training scheme.

The above is only the preferred embodiments of the present invention, and not makees limitation in any form to the present invention, Every technical spirit according to the present invention belongs to any simple modification made for any of the above embodiments, equivalent variations and modification In the range of technical solution of the present invention.

Claims

1. a kind of novel visual motion tracking training system, it is characterised in that the novel visual motion tracking training system is set It is equipped with the main control module for being controlled to detection data and training equipment；

The main control module is to frequency-hopping mixing signal time-frequency domain matrixPre-processed, had Body includes following two step：

The first step is rightLow energy is carried out to pre-process, i.e., will in each sampling instant pValue of the amplitude less than thresholding ε is set to 0, and is obtained The setting of thresholding ε can be determined according to the average energy for receiving signal；

Second step, finds out the time-frequency numeric field data of p moment (p=0,1,2 ... P-1) non-zero, usesRepresent, whereinRepresent the response of p moment time-frequencyCorresponding frequency indices when non-zero, normalize these non-zeros and pre-process, obtain Pretreated vector b (p, q)=[b₁(p,q),b₂(p,q),…,b_M(p,q)]^T, wherein

The first step, estimation l jump correspondingA incident angle, is usedRepresent that l jumps the corresponding incidence angle of n-th of source signal Degree,Calculation formula it is as follows：

<mrow> <msub> <mover> <mi>&theta;</mi> <mo>^</mo> </mover> <mi>n</mi> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mi>M</mi> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <munderover> <mo>&Sigma;</mo> <mrow> <mi>m</mi> <mo>=</mo> <mn>2</mn> </mrow> <mi>M</mi> </munderover> <msup> <mi>sin</mi> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mo>&lsqb;</mo> <mfrac> <mrow> <mi>a</mi> <mi>n</mi> <mi>g</mi> <mi>l</mi> <mi>e</mi> <mrow> <mo>(</mo> <msub> <mover> <mi>a</mi> <mo>^</mo> </mover> <mrow> <mi>n</mi> <mo>,</mo> <mi>m</mi> </mrow> </msub> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> <mo>/</mo> <msub> <mover> <mi>a</mi> <mo>^</mo> </mover> <mrow> <mi>n</mi> <mo>,</mo> <mi>m</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> <mo>)</mo> <mo>*</mo> <mi>c</mi> </mrow> <mrow> <mn>2</mn> <mi>&pi;</mi> <msub> <mover> <mi>f</mi> <mo>^</mo> </mover> <mrow> <mi>c</mi> <mo>,</mo> <mi>n</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> <mi>d</mi> </mrow> </mfrac> <mo>&rsqb;</mo> <mo>,</mo> <mi>n</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2.</mn> <mo>,</mo> <mover> <mi>N</mi> <mo>^</mo> </mover> <mo>;</mo> </mrow>

Represent that l jumps n-th of hybrid matrix column vector that estimation obtainsM-th of element, c represent the light velocity, i.e. v_c =3 × 10⁸Meter per second；

Second step, judges that l (l=2,3 ...) jumps the corresponding pass between the source signal of estimation and the source signal of the first jump estimation System, judgment formula are as follows：

<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <msup> <msub> <mi>m</mi> <mi>n</mi> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> </msup> <mo>=</mo> <munder> <mrow> <mi>arg</mi> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> <mi>m</mi> </munder> <mo>|</mo> <msubsup> <mover> <mi>&theta;</mi> <mo>^</mo> </mover> <mi>m</mi> <mrow> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> </msubsup> <mo>-</mo> <msubsup> <mover> <mi>&theta;</mi> <mo>^</mo> </mover> <mi>m</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>|</mo> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mover> <mi>N</mi> <mo>^</mo> </mover> </mrow> </mtd> </mtr> </mtable> </mfenced>

Wherein m_n ^(l)Represent that l jumps the m of estimation_n ^(l)A signal and first n-th of signal for jumping estimation, which belong to same source, to be believed Number；

3rd step, by different frequency hopping point estimation to the signal for belonging to same source signal be stitched together, as it is final when Frequency domain source signal is estimated, uses Y_nTime-frequency domain estimate of n-th of the source signal of (p, q) expression in time frequency point (p, q), p=0,1, 2 ..., P, q=0,1,2 ..., N_fft-1：

The main control module docks the Short Time Fourier Transform that received LFM signals do linear domain of holomorphy, obtains linear domain of holomorphy Short Time Fourier Transform spectrum；

1.1) LFM signal models are expressed as：

L_A(t, f)=∫ f (t+ τ) h* (τ) K_A(f,τ)dτ；

Wherein, (t, f) is the point on time-frequency domain,For the parameter of linear canonical transform, and ad-bc=1, h (t) are Window function, uses Gaussian window, h* (t) is the conjugation of h (t), and τ is substitution of variable in of the invention；In addition, also have：

<mrow> <msub> <mi>K</mi> <mi>A</mi> </msub> <mrow> <mo>(</mo> <mi>f</mi> <mo>,</mo> <mi>&tau;</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <msqrt> <mrow> <mi>j</mi> <mn>2</mn> <mi>&pi;</mi> <mi>b</mi> </mrow> </msqrt> </mfrac> <mi>exp</mi> <mo>&lsqb;</mo> <mi>j</mi> <mrow> <mo>(</mo> <mfrac> <mi>d</mi> <mrow> <mn>2</mn> <mi>b</mi> </mrow> </mfrac> <msup> <mi>f</mi> <mn>2</mn> </msup> <mo>-</mo> <mfrac> <mn>1</mn> <mi>b</mi> </mfrac> <mi>f</mi> <mi>&tau;</mi> <mo>+</mo> <mfrac> <mi>a</mi> <mrow> <mn>2</mn> <mi>b</mi> </mrow> </mfrac> <msup> <mi>&tau;</mi> <mn>2</mn> </msup> <mo>)</mo> </mrow> <mo>&rsqb;</mo> <mo>,</mo> <mi>b</mi> <mo>&NotEqual;</mo> <mn>0</mn> <mo>;</mo> </mrow>

1.3) it is as follows to define Gauss function h (t)：

<mrow> <mi>h</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mi>&alpha;</mi> <mi>&pi;</mi> </mfrac> <mo>)</mo> </mrow> <mfrac> <mn>1</mn> <mn>4</mn> </mfrac> </msup> <mi>exp</mi> <mrow> <mo>(</mo> <mo>-</mo> <mfrac> <mrow> <msup> <mi>&alpha;t</mi> <mn>2</mn> </msup> </mrow> <mn>2</mn> </mfrac> <mo>)</mo> </mrow> <mo>,</mo> <mi>&alpha;</mi> <mo>></mo> <mn>0</mn> <mo>;</mo> </mrow>

<mrow> <mtable> <mtr> <mtd> <mrow> <msub> <mi>L</mi> <mi>A</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>f</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <msub> <mi>A</mi> <mn>0</mn> </msub> <msqrt> <mrow> <mi>j</mi> <mn>2</mn> <mi>&pi;</mi> <mi>b</mi> </mrow> </msqrt> </mfrac> <msup> <mrow> <mo>(</mo> <mfrac> <mi>&alpha;</mi> <mi>&pi;</mi> </mfrac> <mo>)</mo> </mrow> <mfrac> <mn>1</mn> <mn>4</mn> </mfrac> </msup> <mo>&Integral;</mo> <mi>exp</mi> <mo>{</mo> <msub> <mi>jf</mi> <mn>0</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>+</mo> <mi>&tau;</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>j</mi> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mi>k</mi> <msup> <mrow> <mo>(</mo> <mi>t</mi> <mo>+</mo> <mi>&tau;</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <mfrac> <mrow> <msup> <mi>&alpha;&tau;</mi> <mn>2</mn> </msup> </mrow> <mn>2</mn> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>+</mo> <mi>j</mi> <mfrac> <mi>d</mi> <mrow> <mn>2</mn> <mi>b</mi> </mrow> </mfrac> <msup> <mi>f</mi> <mn>2</mn> </msup> <mo>-</mo> <mi>j</mi> <mfrac> <mrow> <mi>f</mi> <mi>&tau;</mi> </mrow> <mi>b</mi> </mfrac> <mo>+</mo> <mi>j</mi> <mfrac> <mrow> <msup> <mi>a&tau;</mi> <mn>2</mn> </msup> </mrow> <mrow> <mn>2</mn> <mi>b</mi> </mrow> </mfrac> <mo>}</mo> <mi>d</mi> <mi>&tau;</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>=</mo> <mfrac> <msub> <mi>A</mi> <mn>0</mn> </msub> <msqrt> <mrow> <mi>j</mi> <mn>2</mn> <mi>&pi;</mi> <mi>b</mi> </mrow> </msqrt> </mfrac> <msup> <mrow> <mo>(</mo> <mfrac> <mi>&alpha;</mi> <mi>&pi;</mi> </mfrac> <mo>)</mo> </mrow> <mfrac> <mn>1</mn> <mn>4</mn> </mfrac> </msup> <mo>&Integral;</mo> <mi>exp</mi> <mo>{</mo> <msup> <mi>&tau;</mi> <mn>2</mn> </msup> <mrow> <mo>(</mo> <mi>j</mi> <mfrac> <mi>k</mi> <mn>2</mn> </mfrac> <mo>+</mo> <mfrac> <mi>&alpha;</mi> <mn>2</mn> </mfrac> <mo>+</mo> <mi>j</mi> <mfrac> <mi>a</mi> <mrow> <mn>2</mn> <mi>b</mi> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <mi>&tau;</mi> <mrow> <mo>(</mo> <msub> <mi>jf</mi> <mn>0</mn> </msub> <mo>+</mo> <mi>j</mi> <mi>k</mi> <mi>t</mi> <mo>-</mo> <mi>j</mi> <mfrac> <mi>f</mi> <mi>b</mi> </mfrac> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>+</mo> <mi>j</mi> <mfrac> <mrow> <msup> <mi>kt</mi> <mn>2</mn> </msup> </mrow> <mn>2</mn> </mfrac> <mo>+</mo> <msub> <mi>jf</mi> <mn>0</mn> </msub> <mi>t</mi> <mo>+</mo> <mi>j</mi> <mfrac> <mi>d</mi> <mrow> <mn>2</mn> <mi>b</mi> </mrow> </mfrac> <msup> <mi>f</mi> <mn>2</mn> </msup> <mo>}</mo> <mi>d</mi> <mi>&tau;</mi> </mrow> </mtd> </mtr> </mtable> <mo>;</mo> </mrow>

Then：

<mrow> <mo>|</mo> <msub> <mi>L</mi> <mi>A</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>f</mi> <mo>)</mo> </mrow> <mo>|</mo> <mo>=</mo> <mo>|</mo> <mfrac> <msub> <mi>A</mi> <mn>0</mn> </msub> <msqrt> <mrow> <mi>j</mi> <mn>2</mn> <mi>&pi;</mi> <mi>b</mi> </mrow> </msqrt> </mfrac> <msup> <mrow> <mo>(</mo> <mfrac> <mi>&alpha;</mi> <mi>&pi;</mi> </mfrac> <mo>)</mo> </mrow> <mfrac> <mn>1</mn> <mn>4</mn> </mfrac> </msup> <mo>&Integral;</mo> <mi>exp</mi> <mo>{</mo> <msup> <mi>&tau;</mi> <mn>2</mn> </msup> <mrow> <mo>(</mo> <mi>j</mi> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mi>k</mi> <mo>+</mo> <mfrac> <mi>&alpha;</mi> <mn>2</mn> </mfrac> <mo>+</mo> <mi>j</mi> <mfrac> <mi>a</mi> <mrow> <mn>2</mn> <mi>b</mi> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <mi>&tau;</mi> <mrow> <mo>(</mo> <msub> <mi>jf</mi> <mn>0</mn> </msub> <mo>+</mo> <mi>j</mi> <mi>k</mi> <mi>t</mi> <mo>-</mo> <mi>j</mi> <mfrac> <mi>f</mi> <mi>b</mi> </mfrac> <mo>)</mo> </mrow> <mo>}</mo> <mi>d</mi> <mi>&tau;</mi> <mo>|</mo> <mo>;</mo> </mrow>

It can obtain：

<mrow> <mo>|</mo> <msub> <mi>L</mi> <mi>A</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>f</mi> <mo>)</mo> </mrow> <mo>|</mo> <mo>=</mo> <mo>|</mo> <mfrac> <msub> <mi>A</mi> <mn>0</mn> </msub> <msqrt> <mrow> <mi>b</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mfrac> <mi>a</mi> <mi>b</mi> </mfrac> <mo>-</mo> <mi>j</mi> <mi>&alpha;</mi> <mo>)</mo> </mrow> </mrow> </msqrt> </mfrac> <msup> <mrow> <mo>(</mo> <mfrac> <mi>&alpha;</mi> <mi>&pi;</mi> </mfrac> <mo>)</mo> </mrow> <mfrac> <mn>1</mn> <mn>4</mn> </mfrac> </msup> <mi>exp</mi> <mo>-</mo> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>jf</mi> <mn>0</mn> </msub> <mo>+</mo> <mi>j</mi> <mi>k</mi> <mi>t</mi> <mo>-</mo> <mi>j</mi> <mfrac> <mi>f</mi> <mi>b</mi> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mrow> <mi>j</mi> <mn>2</mn> <mi>k</mi> <mo>+</mo> <mn>2</mn> <mi>&alpha;</mi> <mo>+</mo> <mi>j</mi> <mfrac> <mrow> <mn>2</mn> <mi>a</mi> </mrow> <mi>b</mi> </mfrac> </mrow> </mfrac> <mo>|</mo> <mo>;</mo> </mrow>

<mrow> <mtable> <mtr> <mtd> <mrow> <mo>|</mo> <msub> <mi>L</mi> <mi>A</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>f</mi> <mo>)</mo> </mrow> <msup> <mo>|</mo> <mn>2</mn> </msup> <mo>=</mo> <mfrac> <mrow> <msqrt> <mi>&alpha;</mi> </msqrt> <msup> <msub> <mi>A</mi> <mn>0</mn> </msub> <mn>2</mn> </msup> </mrow> <mrow> <msqrt> <mi>&pi;</mi> </msqrt> <mi>b</mi> <mo>|</mo> <mi>k</mi> <mo>+</mo> <mfrac> <mi>a</mi> <mi>b</mi> </mfrac> <mo>-</mo> <mi>j</mi> <mi>&alpha;</mi> <mo>|</mo> </mrow> </mfrac> <mo>|</mo> <mi>exp</mi> <mo>-</mo> <mfrac> <mrow> <msup> <mrow> <mo>(</mo> <msub> <mi>jf</mi> <mn>0</mn> </msub> <mo>+</mo> <mi>j</mi> <mi>k</mi> <mi>t</mi> <mo>-</mo> <mi>j</mi> <mfrac> <mi>f</mi> <mi>b</mi> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mrow> <mo>(</mo> <mn>2</mn> <mi>&alpha;</mi> <mo>-</mo> <mi>j</mi> <mn>2</mn> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mfrac> <mi>a</mi> <mi>b</mi> </mfrac> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mrow> <mn>4</mn> <msup> <mi>&alpha;</mi> <mn>2</mn> </msup> <mo>+</mo> <mn>4</mn> <msup> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mfrac> <mi>a</mi> <mi>b</mi> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> <msup> <mo>|</mo> <mn>2</mn> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>=</mo> <mfrac> <mrow> <msqrt> <mi>&alpha;</mi> </msqrt> <msup> <msub> <mi>A</mi> <mn>0</mn> </msub> <mn>2</mn> </msup> </mrow> <mrow> <msqrt> <mi>&pi;</mi> </msqrt> <mi>b</mi> <msqrt> <mrow> <msup> <mi>&alpha;</mi> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mfrac> <mi>a</mi> <mi>b</mi> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mrow> </mfrac> <mi>exp</mi> <mo>-</mo> <mfrac> <mrow> <mn>4</mn> <mi>&alpha;</mi> <msup> <mrow> <mo>(</mo> <msub> <mi>f</mi> <mn>0</mn> </msub> <mo>+</mo> <mi>k</mi> <mi>t</mi> <mo>-</mo> <mfrac> <mi>f</mi> <mi>b</mi> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> <mrow> <mn>4</mn> <msup> <mi>&alpha;</mi> <mn>2</mn> </msup> <mo>+</mo> <mn>4</mn> <msup> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mfrac> <mi>a</mi> <mi>b</mi> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>=</mo> <mfrac> <mrow> <msup> <msub> <mi>A</mi> <mn>0</mn> </msub> <mn>2</mn> </msup> </mrow> <mrow> <msqrt> <mi>&pi;</mi> </msqrt> <mi>b</mi> </mrow> </mfrac> <mo>&CenterDot;</mo> <mfrac> <mn>1</mn> <msqrt> <mrow> <mi>&alpha;</mi> <mo>+</mo> <mfrac> <mn>1</mn> <mi>&alpha;</mi> </mfrac> <msup> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mfrac> <mi>a</mi> <mi>b</mi> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mfrac> <mi>exp</mi> <mo>-</mo> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>f</mi> <mn>0</mn> </msub> <mo>+</mo> <mi>k</mi> <mi>t</mi> <mo>-</mo> <mfrac> <mi>f</mi> <mi>b</mi> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mrow> <mi>&alpha;</mi> <mo>+</mo> <mfrac> <mn>1</mn> <mi>&alpha;</mi> </mfrac> <msup> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mfrac> <mi>a</mi> <mi>b</mi> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> </mrow> </mtd> </mtr> </mtable> <mo>;</mo> </mrow>

Hough transform is done to the Short Time Fourier Transform spectrum of the linear domain of holomorphy of obtained LFM signals, obtains Hough transform square Battle array；Hough transform is done to the Short Time Fourier Transform spectrum of the linear domain of holomorphy of signal to be carried out as follows：

First, polar equation is ρ=tcos θ+fsin θ, wherein, (t, f) be time-frequency domain on point, ρ for the point to origin away from From θ was the point and origin straight line and the angle of x-axis, and polar coordinate space (ρ, θ) is quantified as (ρ_u,θ_v), u=1 ..., M, v =1 ..., N, obtains the two-dimensional matrix M (ρ, θ) of M × N, and M (ρ, θ) is an accumulator, initial value 0；

Then each point (t, f) on time-frequency domain is corresponded to, its spectral amplitude is | L_A(t,f)|², to improve calculating speed, setting works as certain The spectral amplitude of a point is more than the maximum of the spectral amplitude of all the pointsWhen then carry out Hough transform, otherwise neglect the point；

Finally to meeting that spectral amplitude is more than the maximum of the spectral amplitude of all the pointsPoint (t, f), all quantized values of θ are substituted into Polar equation, obtains corresponding ρ, and accumulator is added | L_A(t,f)|², i.e. M (ρ, θ)=M (ρ, θ)+| L_A(t,f)|², obtain To Hough transform matrix M (ρ, θ)；

The matrix after Hough transform is traveled through using two-dimentional sliding window, and energy accumulation is done in window, so as to be examined Statistic；The matrix after Hough transform is traveled through with two-dimentional sliding window, and does energy accumulation in window and is carried out as follows：

The length for setting two dimension sliding window P (m, n) first is L, width K, wherein, m and n represent the horizontal stroke of two-dimentional sliding window respectively Coordinate and ordinate；The length that the Hough transform matrix obtained in setting steps S2 is M (ρ, θ) is M, width N, then Hough Transformation matrix is divided intoBlock, whereinRepresent downward rounding；

Then it is (L, K) to calculate Hough transform matrix points respectively, (2L, K) ..., (pL, K), (L, 2K), (2L, 2K) ..., (pL, 2K) ..., the energy of (pL, qK) place window P (m, n) and, obtain the test statistics Q (m, n) of p × q, its computational methods It is as follows：

<mrow> <mtable> <mtr> <mtd> <mrow> <mi>Q</mi> <mrow> <mo>(</mo> <mi>m</mi> <mo>,</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>u</mi> <mo>=</mo> <mrow> <mo>(</mo> <mi>m</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mi>L</mi> <mo>+</mo> <mn>1</mn> </mrow> <mrow> <mi>m</mi> <mi>L</mi> </mrow> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>v</mi> <mo>=</mo> <mrow> <mo>(</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mi>K</mi> <mo>+</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> <mi>K</mi> </mrow> </munderover> <mi>M</mi> <mrow> <mo>(</mo> <msub> <mi>&rho;</mi> <mi>u</mi> </msub> <mo>,</mo> <msub> <mi>&theta;</mi> <mi>v</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>p</mi> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>q</mi> </mrow> </mtd> </mtr> </mtable> <mo>;</mo> </mrow>

Optimal decision threshold is obtained according to recipient's operating characteristic curve (ROC), by by optimal decision threshold with obtaining Test statistics be compared, LFM signals are detected；

It is electrically connected with main control module, for catching the eye motion capture module of eye motion data message by eye tracker；

The eye motion capture module estimates the jumping moment of each jump using clustering algorithm and respectively jumps corresponding normalization Hybrid matrix column vector, Hopping frequencies when, comprise the following steps：

The first step is right at p (p=0,1,2 ... the P-1) momentThe frequency values of expression are clustered, obtained cluster centre NumberRepresent carrier frequency number existing for the p moment,A cluster centre then represents the size of carrier frequency, uses respectivelyRepresent；

Second step, to each sampling instant p (p=0,1,2 ... P-1), utilizes clustering algorithm pairClustered, equally It is availableA cluster centre, is usedRepresent；

<mrow> <mover> <mi>N</mi> <mo>^</mo> </mover> <mo>=</mo> <mi>r</mi> <mi>o</mi> <mi>u</mi> <mi>n</mi> <mi>d</mi> <mrow> <mo>(</mo> <mfrac> <mn>1</mn> <mi>p</mi> </mfrac> <munderover> <mo>&Sigma;</mo> <mrow> <mi>p</mi> <mo>=</mo> <mn>0</mn> </mrow> <mrow> <mi>P</mi> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <msub> <mover> <mi>N</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>;</mo> </mrow>

4th step, finds outAt the time of, use p_hRepresent, to the p of each section of continuous value_hIntermediate value is sought, is used Represent the l sections of p that are connected_hIntermediate value, thenRepresent the estimation at l-th of frequency hopping moment；

5th step, obtains according to estimation in second stepAnd the 4th frequency estimated in step It is corresponding that rate jumping moment estimates each jumpA hybrid matrix column vectorSpecifically formula is：

<mrow> <mtable> <mtr> <mtd> <mrow> <msub> <mover> <mi>a</mi> <mo>^</mo> </mover> <mi>n</mi> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mfrac> <mn>1</mn> <mrow> <msub> <mover> <mi>p</mi> <mo>&OverBar;</mo> </mover> <mi>h</mi> </msub> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>&CenterDot;</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>p</mi> <mo>&NotEqual;</mo> <msub> <mi>p</mi> <mi>h</mi> </msub> </mrow> <mrow> <msub> <mover> <mi>p</mi> <mo>&OverBar;</mo> </mover> <mi>h</mi> </msub> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </munderover> <msubsup> <mi>b</mi> <mrow> <mi>n</mi> <mo>,</mo> <mi>p</mi> </mrow> <mn>0</mn> </msubsup> </mrow> </mtd> <mtd> <mrow> <mi>l</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <mn>1</mn> <mrow> <msub> <mover> <mi>p</mi> <mo>&OverBar;</mo> </mover> <mi>h</mi> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mover> <mi>p</mi> <mo>&OverBar;</mo> </mover> <mi>h</mi> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>&CenterDot;</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>p</mi> <mo>=</mo> <msub> <mover> <mi>p</mi> <mo>&OverBar;</mo> </mover> <mi>h</mi> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>p</mi> <mo>&NotEqual;</mo> <msub> <mi>p</mi> <mi>h</mi> </msub> </mrow> <mrow> <msub> <mover> <mi>p</mi> <mo>&OverBar;</mo> </mover> <mi>h</mi> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> </mrow> </munderover> <msubsup> <mi>b</mi> <mrow> <mi>n</mi> <mo>,</mo> <mi>p</mi> </mrow> <mn>0</mn> </msubsup> </mrow> </mtd> <mtd> <mrow> <mi>l</mi> <mo>></mo> <mn>1</mn> <mo>,</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow> </mtd> <mtd> <mrow> <mo>,</mo> <mi>n</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mover> <mi>N</mi> <mo>^</mo> </mover> </mrow> </mtd> </mtr> </mtable> <mo>;</mo> </mrow>

HereIt is corresponding to represent that l is jumpedA hybrid matrix Column vector estimate；

6th step, estimates the corresponding carrier frequency of each jump, usesIt is corresponding to represent that l is jumpedIt is a Frequency estimation, calculation formula are as follows：

<mrow> <mtable> <mtr> <mtd> <mrow> <msub> <mover> <mi>f</mi> <mo>^</mo> </mover> <mrow> <mi>c</mi> <mo>,</mo> <mi>n</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mfrac> <mn>1</mn> <mrow> <msub> <mover> <mi>p</mi> <mo>&OverBar;</mo> </mover> <mi>h</mi> </msub> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>&CenterDot;</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>p</mi> <mo>&NotEqual;</mo> <msub> <mi>p</mi> <mi>h</mi> </msub> </mrow> <mrow> <msub> <mover> <mi>p</mi> <mo>&OverBar;</mo> </mover> <mi>h</mi> </msub> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </munderover> <msubsup> <mi>f</mi> <mi>o</mi> <mi>n</mi> </msubsup> <mrow> <mo>(</mo> <mi>p</mi> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mrow> <mi>l</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <mn>1</mn> <mrow> <msub> <mover> <mi>p</mi> <mo>&OverBar;</mo> </mover> <mi>h</mi> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mover> <mi>p</mi> <mo>&OverBar;</mo> </mover> <mi>h</mi> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>&CenterDot;</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>p</mi> <mo>=</mo> <msub> <mover> <mi>p</mi> <mo>&OverBar;</mo> </mover> <mi>h</mi> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>p</mi> <mo>&NotEqual;</mo> <msub> <mi>p</mi> <mi>h</mi> </msub> </mrow> <mrow> <msub> <mover> <mi>p</mi> <mo>&OverBar;</mo> </mover> <mi>h</mi> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> </mrow> </munderover> <msubsup> <mi>f</mi> <mi>o</mi> <mi>n</mi> </msubsup> <mrow> <mo>(</mo> <mi>p</mi> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mrow> <mi>l</mi> <mo>></mo> <mn>1</mn> <mo>,</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow> </mtd> <mtd> <mrow> <mo>,</mo> <mi>n</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mover> <mi>N</mi> <mo>^</mo> </mover> </mrow> </mtd> </mtr> </mtable> <mo>;</mo> </mrow>

It is electrically connected with main control module, for the data message of eye motion capture module, the acquisition of eye test module to be shown The display module come；

The normalization hybrid matrix column vector estimation time-frequency domain frequency hopping source signal that the display module is estimated, specific steps are such as Under：

Second step, all moment p jumped to l (l=1,2 ...)_l, estimate the time-frequency numeric field data of each frequency hopping source signal of the jump, count It is as follows to calculate formula：

<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mover> <mi>S</mi> <mo>~</mo> </mover> <mi>j</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>l</mi> </msub> <mo>,</mo> <mi>q</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mo>|</mo> <mo>|</mo> <msub> <mover> <mi>a</mi> <mo>^</mo> </mover> <mi>j</mi> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> </mrow> </mfrac> <mo>&CenterDot;</mo> <msubsup> <mover> <mi>a</mi> <mo>^</mo> </mover> <mi>j</mi> <mi>H</mi> </msubsup> <mrow> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> <mo>&times;</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <msub> <mover> <mi>X</mi> <mo>~</mo> </mover> <mn>1</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>l</mi> </msub> <mo>,</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mover> <mi>X</mi> <mo>~</mo> </mover> <mn>2</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>l</mi> </msub> <mo>,</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mover> <mi>X</mi> <mo>~</mo> </mover> <mi>M</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>l</mi> </msub> <mo>,</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>j</mi> <mo>=</mo> <munder> <mrow> <mi>arg</mi> <mi>max</mi> </mrow> <mrow> <msub> <mi>j</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mover> <mi>N</mi> <mo>^</mo> </mover> </mrow> </munder> <mrow> <mo>(</mo> <mo>|</mo> <msup> <mrow> <mo>&lsqb;</mo> <msub> <mover> <mi>X</mi> <mo>~</mo> </mover> <mn>1</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>l</mi> </msub> <mo>,</mo> <mi>q</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mover> <mi>X</mi> <mo>~</mo> </mover> <mn>2</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>l</mi> </msub> <mo>,</mo> <mi>q</mi> <mo>)</mo> </mrow> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mover> <mi>X</mi> <mo>~</mo> </mover> <mi>M</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>l</mi> </msub> <mo>,</mo> <mi>q</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> <mi>H</mi> </msup> <mo>&times;</mo> <msub> <mover> <mi>a</mi> <mo>^</mo> </mover> <msub> <mi>j</mi> <mn>0</mn> </msub> </msub> <mo>(</mo> <mi>l</mi> <mo>)</mo> </mrow> <mo>|</mo> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mtable> <mtr> <mtd> <mrow> <msub> <mover> <mi>S</mi> <mo>~</mo> </mover> <mi>m</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>l</mi> </msub> <mo>,</mo> <mi>q</mi> <mo>)</mo> </mrow> <mo>=</mo> <mn>0</mn> </mrow> </mtd> <mtd> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>M</mi> <mo>,</mo> <mi>m</mi> <mo>&NotEqual;</mo> <mi>j</mi> </mrow> </mtd> </mtr> </mtable> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>q</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>N</mi> <mrow> <mi>f</mi> <mi>f</mi> <mi>t</mi> </mrow> </msub> <mo>-</mo> <mn>1</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>;</mo> </mrow>

It is electrically connected with main control module, the trick brain for training equipment carried out to trick brain exercise for coordination by trick brain is instructed Practice module.

2. novel visual motion tracking training system as claimed in claim 1, it is characterised in that the novel visual movement chases after Track training system is additionally provided with platform, and camera device is installed by welding with the left of the platform, is set on the right side of the camera device It is equipped with controller；

Slide is provided with the middle part of the platform, adjustable plate is movably installed with the slide；The right end welding peace of the platform Equipped with stent, the upper end of the stent is installed by welding with connecting rod, and the upper end of the connecting rod is installed by welding with observation device；Institute The lower end for stating stent is lifted with the image collecting device being connected with adjustable plate, and described image harvester is electrically connected with observation device Connect.

3. novel visual motion tracking training system as claimed in claim 2, it is characterised in that the surface of the platform is set There is the graduated scale to match with slide, card slot is provided with the slide, the lower end of the adjustable plate is provided with and card slot phase The buckle matched somebody with somebody.

4. novel visual motion tracking training system as claimed in claim 2, it is characterised in that the surface of the platform is laid with There are solar panels, the lower end of the table top is provided with storage battery；The solar panels are electrically connected with storage battery, the storage battery with Controller is electrically connected.

5. novel visual motion tracking training system as claimed in claim 1, it is characterised in that the eye motion catches mould Block records the absolute acceleration of measuring point using accelerometer, and using feedback coefficient of the data obtained as control system According to force cell will be formulated on each control device, and the sensitiveness index of each accelerometer is taken as：S_a=10V/g= 10V/9.81ms^-2, the sensitiveness index of each force snesor is taken as S_f=10V/1000kN；Using MR damper as control Device processed, by control magnetic field make magnetic rheological body realized in Millisecond between the viscous fluid of free-flowing and semisolid can Inversion；

Step 2, the simulation of damping is carried out using the spencer models of MR damper；Damping force is calculated with following formula

<mrow> <mi>f</mi> <mo>=</mo> <msub> <mi>c</mi> <mn>0</mn> </msub> <mover> <mi>x</mi> <mo>&CenterDot;</mo> </mover> <mo>+</mo> <mi>&alpha;</mi> <mi>z</mi> </mrow>

<mrow> <mover> <mi>z</mi> <mo>&CenterDot;</mo> </mover> <mo>=</mo> <mo>-</mo> <mi>&gamma;</mi> <mo>|</mo> <mover> <mi>x</mi> <mo>&CenterDot;</mo> </mover> <mo>-</mo> <mover> <mi>y</mi> <mo>&CenterDot;</mo> </mover> <mo>|</mo> <mi>z</mi> <mo>|</mo> <mi>z</mi> <msup> <mo>|</mo> <mrow> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mo>-</mo> <mi>&beta;</mi> <mrow> <mo>(</mo> <mover> <mi>x</mi> <mo>&CenterDot;</mo> </mover> <mo>-</mo> <mover> <mi>y</mi> <mo>&CenterDot;</mo> </mover> <mo>)</mo> </mrow> <mo>|</mo> <mi>z</mi> <msup> <mo>|</mo> <mi>n</mi> </msup> <mo>+</mo> <mi>A</mi> <mrow> <mo>(</mo> <mover> <mi>x</mi> <mo>&CenterDot;</mo> </mover> <mo>-</mo> <mover> <mi>y</mi> <mo>&CenterDot;</mo> </mover> <mo>)</mo> </mrow> </mrow>

<mrow> <mover> <mi>y</mi> <mo>&CenterDot;</mo> </mover> <mo>=</mo> <mo>&lsqb;</mo> <mi>&alpha;</mi> <mi>z</mi> <mo>+</mo> <msub> <mi>c</mi> <mn>0</mn> </msub> <mover> <mi>x</mi> <mo>&CenterDot;</mo> </mover> <mo>+</mo> <msub> <mi>k</mi> <mn>0</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> <mo>/</mo> <mrow> <mo>(</mo> <msub> <mi>c</mi> <mn>0</mn> </msub> <mo>+</mo> <msub> <mi>c</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mrow>

In formula：k₁For the accumulator rigidity of MR damper, k₀For high speed when control rigidity, x₀For spring k₁Initial bit Move, c₀For viscous damping coefficient of speed when larger, c₁For adhesive elements, declining during for producing low speed in power-length velocity relation Subtracting, A, beta, gamma, n is constant, and value is determined by MR damper architectural characteristic,

<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mi>&alpha;</mi> <mo>=</mo> <mi>&alpha;</mi> <mo>(</mo> <mi>u</mi> <mo>)</mo> <mo>=</mo> <msub> <mi>&alpha;</mi> <mi>a</mi> </msub> <mo>+</mo> <msub> <mi>&alpha;</mi> <mi>b</mi> </msub> <mi>u</mi> </mtd> </mtr> <mtr> <mtd> <msub> <mi>c</mi> <mn>0</mn> </msub> <mo>=</mo> <msub> <mi>c</mi> <mn>0</mn> </msub> <mo>(</mo> <mi>u</mi> <mo>)</mo> <mo>=</mo> <msub> <mi>c</mi> <mrow> <mn>0</mn> <mi>a</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>c</mi> <mrow> <mn>0</mn> <mi>b</mi> </mrow> </msub> <mi>u</mi> </mtd> </mtr> <mtr> <mtd> <msub> <mi>c</mi> <mn>1</mn> </msub> <mo>=</mo> <msub> <mi>c</mi> <mn>1</mn> </msub> <mo>(</mo> <mi>u</mi> <mo>)</mo> <mo>=</mo> <msub> <mi>c</mi> <mrow> <mn>1</mn> <mi>a</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>c</mi> <mrow> <mn>1</mn> <mi>b</mi> </mrow> </msub> <mi>u</mi> </mtd> </mtr> </mtable> </mfenced>

U is the magnitude of voltage on MR damper when producing corresponding damping force, and voltage, which can lag behind, calculates desired value u_c, it is necessary to school Just：

<mrow> <mover> <mi>u</mi> <mo>&CenterDot;</mo> </mover> <mo>=</mo> <mo>-</mo> <mi>&eta;</mi> <mrow> <mo>(</mo> <mi>u</mi> <mo>-</mo> <msub> <mi>u</mi> <mi>c</mi> </msub> <mo>)</mo> </mrow> <mo>;</mo> </mrow>

Step 3, carries out the design of active control using LQR/LQG control laws, LQG controllers by Optimal state-feedback gain and Kalman filter two parts form, and Optimal state-feedback gain is tried to achieve using classical linear optimal active control algorithm LQR；

Step 4, makes semi-automatic control device reach approximately active Optimal Control Force with reference to MR damper spy and analysis on aqueduct structure Effect, design semi-active control law；

Control law can be described as：

u_c=U (f) * H { (f_c-f)f}

Wherein, U (f) is characterization voltage and the continuous function of power, using power power corresponding with relative velocity hysteretic loop top intersection point As characterization, simulated with 0.1v incremental voltages, obtain the discrete relationship of power and voltage；

Step 6, by the form and size of tentative calculation Optimal Control Force process, constantly adjustment weight matrix Q and R, to be controlled Effect and controling power integrate optimal active controlling force.