CN107425928A - A kind of signal synthesis method and system - Google Patents
A kind of signal synthesis method and system Download PDFInfo
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- CN107425928A CN107425928A CN201710311860.0A CN201710311860A CN107425928A CN 107425928 A CN107425928 A CN 107425928A CN 201710311860 A CN201710311860 A CN 201710311860A CN 107425928 A CN107425928 A CN 107425928A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04B17/309—Measuring or estimating channel quality parameters
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- H—ELECTRICITY
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- H04B1/109—Means associated with receiver for limiting or suppressing noise or interference by improving strong signal performance of the receiver when strong unwanted signals are present at the receiver input
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract
The present invention relates to a kind of signal synthesis method and system, this method is used to be synthesized the output signal of each sensor in multiple sensors, and this method includes：Determine the power factor estimate of each sensor in the multiple sensor；According to the power factor estimate and the relation of first threshold, determine that first of each sensor in the multiple sensor synthesizes weights；Using the first synthesis weights of each sensor in the multiple sensor, the output signal of each sensor in the multiple sensor is synthesized.Therefore according to power factor estimate and the relation of first threshold, it can be determined that go out whether sensor breaks down, so as to targetedly determine the synthesis weights of the sensor, synthesis loss can be reduced to greatest extent.
Description
Technical field
The present invention relates to signal processing technology field, more particularly to a kind of signal synthesis method and system.
Background technology
Sensor (English name：Transducer/sensor) it is a kind of detection means, measured letter can be experienced
Breath, and the information that will can be experienced, electric signal or the information output of other required forms are for conversion into according to certain rules, to meet
The requirement such as transmission, processing, storage, display, record and control of information.
How to ensure that the high efficient and reliable of small-signal receives a difficult point in always modern signal processing.At present due to
The limitation of technical merit, the performance of single sensor node can not possibly be improved unrestrictedly, and receiver noise also is difficult to further drop
Low, a kind of effective solution is to carry out joint reception to same signal using multiple sensors, and reception signal is closed
Into to improve Signal-to-Noise.It is random lay multiple sensor signals synthetic technology have application flexibly, cost is relatively low and performance
The advantages that sane, there is wide application prospect in various fields.
A variety of signal synthesis methods for multiple sensors in the prior art be present.
A kind of article delivered for D.H.Rogstad et al.《The SUMPLE Algorithm for Aligning
Arrays of Receiving Radio Antennas:Coherence Achieved with Less Hardware and
Lower Combining Loss》It is proposed that one kind can in (IPN Progress Report, vol.42, no.162, pp.1-29)
For laying the SUMPLE algorithms of multiple sensor signals synthesis at random.
First, first reception signal is modeled as：
Wherein Si,kThe signal that i-th of sensor arrives in kth reception, i=1,2 ..., N are represented, N is that sensor is always individual
Number, k is sampled point time variable；si,kThe source signal that i-th of sensor arrives in kth reception is represented,For i-th of sensing
The noise that device arrives in kth reception, subscript s are representedWith si,kCorresponding relation be present.
The synthesis weights of signal are expressed as：
Wi,K=wi,K+ηi,K (2)
Wherein Wi,KRepresent the synthesis weights for the signal that i-th of sensor receives in K sections relevant periods；wi,KRepresent
The preferable synthesis weights for the source signal that i-th of sensor receives in K sections relevant periods, can be described as signal weights；ηi,KTable
Show that i-th of sensor in K section relevant periods weights estimation error as caused by noise, can be described as noise weights.K and K
Between relation as shown in figure 1, ncor is relevant periods, K is the time variable in units of relevant periods ncor.
According to formula (1) and (2), composite signal CkIt can be expressed as：
Wherein, symbol * represents complex conjugate computing, the signal terms c of composite signalkAnd noise itemRespectively：
Assuming that each sensor receptivity is identical, each reception signal has been alignd, and the weights of each sensor signal
When orthogonal, the mean power of signal terms after synthesis can be calculated according to formula (4) | ck|2：
Wherein, | sk|2The mean power of synthesis front signal item is represented, | ηK|2Represent the mean power of composite noise weights.
The mean power of noise item after synthesis can be calculated according to formula (5)
Wherein,The mean power of noise item before synthesizing is represented, | wK|2Represent the mean power of composite signal weights.
Therefore according to formula (6) and formula (7), the signal to noise ratio of composite signal is SNR：
Assuming that the signal to noise ratio of each sensor input signal isThe signal to noise ratio for synthesizing weights is ρw=| wK
|2/|ηK|2, then above formula can using abbreviation as：
Wherein ρ is equal to：
As it is assumed that each sensor receptivity is identical, therefore ρ ≈ 1.Accordingly, the object function of composite signal is established
For：
As the signal to noise ratio ρ of synthesis weightswDuring ＞ ＞ 1, according to formula (11), then the signal to noise ratio of composite signal can reach substantially
To optimal performance theoretically attainable(OPTA) SNRopt：
SNRopt=N ρs (12)
In fact, due to the influence of composite noise weights, synthesis performance is unable to reach optimal performance theoretically attainable(OPTA).Therefore can count
Calculate the signal to noise ratio snr of actual composite signalpractical：
Wherein,Represent that sensor array is listed in the flat of signal in ncor time interval
Equal power,Represent that sensor array is listed in the mean power of noise in ncor time interval.
With reference to formula (12) and (13), synthesis loss SNR can be obtainedloss：
SNRloss=SNRopt-SNRpractical (14)
Understood according to formula (11), signal to noise ratio snr and the synthesis weights signal to noise ratio ρ of composite signalwIt is closely related, and synthesize
Weights signal to noise ratio is bigger, and the signal to noise ratio of composite signal levels off to optimal performance theoretically attainable(OPTA) value, and synthesis loss is smaller.
The SUMPLE that this article proposes establishes the iterative formula for synthesizing weights, i.e. root as a kind of algorithm of recursion iteration
According to Wi,KDerive weights W of i-th of sensor in K+1 sectionsi,K+1：
Wherein Pi,KIt is equal to：
In formula (15),Represent the synthesis letter of all the sensors reception signal in addition to i-th of sensor
Reference signal number as i-th of sensor reception signal, RK+1It is normalization coefficient, its role is to ensure each sensor
Weights quadratic sum is equal to number of probes, as shown in formula (18), to prevent weights amplitude from becoming unstable because of Continuous accumulation.
In fact, in larger sensor arrays, some sensors can may break down in use.Due to event
It is noise to hinder received by sensor, and it synthesizes weights and is not zero, therefore the noise introduced can increase the synthesis damage of array
Lose.It is therefore desirable to detect and judge whether sensor array breaks down, and the synthesis weights to fault sensor are repaiied
Just, synthesis loss is reduced by reducing the weights of fault sensor.
The estimation problem of synthesis weights when Rogstad does not have to analyze failure.
The article that another kind is delivered for Shen Caiyao et al.《Failure antenna analysis based on SUMPLE algorithms in antenna array》
A kind of (aerospace journal, vol.32, no.11, pp.2445-2450) middle SUMPLE of increase α modified weight coefficients of proposition (α-
SUMPLE) method, for the α-SUMPLE that this article proposes as a kind of algorithm of recursion iteration, the iteration for establishing synthesis weights is public
Formula, i.e., according to Wi,KDerive weights W of i-th of sensor in K+1 sectionsi,K+1：
In formula (19) | Wi,K|αIt is modified weight coefficient, its function is weighed by improving the synthesis of fault-free sensor
Value reduces its influence to strengthen its effect by reducing the synthesis weights of fault sensor, and then reduces failure
When synthesis loss.
The shortcomings that existing method, has：The estimation problem of synthesis weights when SUMPLE algorithms do not have to analyze failure, because
This can not reduce synthesis loss.α-SUMPLE algorithm shortcomings are：(1) when sensor array does not break down, modified weight
Coefficient | Wi,K|αExtra synthesis loss can be increased；(2) when sensor array breaks down, the synthesis weights of fault sensor
It can not be zero, i.e., can not reduce synthesis loss to greatest extent.
The content of the invention
The present invention provides a kind of signal synthesis method and system, can reduce synthesis loss to greatest extent.
First aspect provides a kind of signal synthesis method, and methods described is used for each sensor in multiple sensors
Output signal is synthesized, and methods described includes：Determine the power factor estimate of each sensor in the multiple sensor；
According to the power factor estimate and the relation of first threshold, determine that first of each sensor in the multiple sensor is closed
Into weights；, will be every in the multiple sensor using the first synthesis weights of each sensor in the multiple sensor
The output signal of individual sensor is synthesized.
Second aspect provides a kind of signal synthesis system, and the system is used for each sensor in multiple sensors
Output signal is synthesized, and the system includes：Power factor estimate determining module, for determining in the multiple sensor
The power factor estimate of each sensor；First synthesis weights determining module, for true according to the power factor estimate
The power factor estimate and the relation of first threshold that cover half block determines, determine the of each sensor in the multiple sensor
One synthesis weights；Signal synthesizing module, for the multiple sensor determined using the described first synthesis weights determining module
In each sensor the first synthesis weights, the output signal of each sensor in the multiple sensor is closed
Into.
In the embodiment of the present invention, when the output signal of each sensor in multiple sensors is synthesized, first determine
The power factor estimate of each sensor in the multiple sensor；Then according to the power factor estimate and the first threshold
The relation of value, determine the first synthesis weights of each sensor in the multiple sensor；Recycle in the multiple sensor
The first synthesis weights of each sensor, the output signal of each sensor in the multiple sensor is synthesized.
Therefore the synthesis weights of each sensor are not to be used uniformly iterative algorithm to determine in the embodiment of the present invention, but root
According to power factor estimate and the relation of first threshold, determine each to sense in the multiple sensor using corresponding mode
The synthesis weights of device, that is to say, that according to power factor estimate and the relation of first threshold, it can be determined that whether go out sensor
Break down, so as to targetedly determine the synthesis weights of the sensor, synthesis loss can be reduced to greatest extent.
Brief description of the drawings
Relation schematic diagrams of the Fig. 1 between time variable；
Fig. 2 is a kind of ETDGE algorithmic systems block diagram provided in an embodiment of the present invention；
Fig. 3 is a kind of signal synthesis method flow chart provided in an embodiment of the present invention；
Fig. 4 is another signal synthesis method flow chart provided in an embodiment of the present invention；
Fig. 5 is another signal synthesis method flow chart provided in an embodiment of the present invention；
Fig. 6 is a kind of GFC-SUMPLE signals synthesis system block diagram provided in an embodiment of the present invention；
Algorithms of different is shown the loss contrast of signal synthesis performance when Fig. 7 is fault-free sensor provided in an embodiment of the present invention
It is intended to；
When Fig. 8 is one fault sensor of presence provided in an embodiment of the present invention, algorithms of different is to the synthetic energy loss of signal
Lose contrast schematic diagram；
Fig. 9 is that ETDGE algorithms power factor at the 1st sensor is estimated before and after failure provided in an embodiment of the present invention occurs
The change comparison diagram of evaluation；
Figure 10 is conjunction of the GFC-SUMPLE algorithms at the 1st sensor before and after failure provided in an embodiment of the present invention occurs
Change comparison diagram into weights；
Figure 11 is that the signal synthesis performance of algorithms of different before and after failure provided in an embodiment of the present invention occurs loses comparison diagram；
Figure 12 is a kind of signal synthesis system structural representation provided in an embodiment of the present invention；
Figure 13 is another signal synthesis system structural representation provided in an embodiment of the present invention.
Embodiment
Below by drawings and examples, technical scheme is described in further detail.
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached in the embodiment of the present invention
Figure, the technical scheme in the present invention is clearly and completely described, it is clear that described embodiment is a part of the invention
Embodiment, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art are not making wound
The every other embodiment obtained under the premise of the property made work, belongs to the scope of protection of the invention.
For ease of the understanding to the embodiment of the present invention, it is further explained below in conjunction with accompanying drawing with specific embodiment
Bright, embodiment does not form the restriction to the embodiment of the present invention.
It is an object of the invention to provide a kind of signal synthesis method and system, and failure has been carried out in signal building-up process
Detection.First, by analyzing the failure situation of signal composition algorithm model, disclose fault sensor and synthesize weights to non-
Fault sensor synthesizes the influence of weights signal to noise ratio.Then, based on above analysis result, using in delay time estimation method
The power factor of ETDGE (Explicit Time Delay and Gain Estimator) algorithm estimates that each sensor is defeated
Enter the signal to noise ratio of signal, can be by the synthesis of the sensor when the power factor for detecting some sensor input signal is zero
Weights are arranged to zero, reduce synthesis loss to greatest extent.
Advantages of the present invention mainly has (1) this method to detect sensor array by the power factor of ETDGE algorithms
Failure in row；(2) when sensor array does not break down, synthesis weights are consistent with SUMPLE algorithms, will not increase volume
Outer synthesis loss；(3) when sensor array breaks down, the synthesis weights of fault sensor can be arranged to zero, most
Limits reduce synthesis loss.
The embodiment of the present invention proposes a kind of signal synthesis method GFC-SUMPLE (Gain Factor Controlled
SUMPLE).And GFC-SUMPLE establishes the iterative formula for synthesizing weights, i.e., according to W as a kind of algorithm of recursion iterationi,KPush away
Export weights W of i-th of sensor in K+1 sectionsi,K+1.The form of the iterative formula of its synthesis weights is identical with formula (15),
But in each iterative process, the step of adding fault detect and modified weight.
First, the failure situation of signal composition algorithm model is analyzed, for easy analysis, it is assumed that in N number of sensing
In device array, certain sensor failure, the signal to noise ratio of its reception signal is far below other sensors, and other sensors receive
The signal to noise ratio of signal is identical.Without loss of generality, it is assumed that the 1st sensor failure, i.e. S1,kThere is no source signal, only exist and make an uproar
Sound.The composite signal of sensor array is expressed as：
Wherein ck,N-1WithSignal terms and noise item after synthesis are represented respectively.
Assuming that signal and noise power are held essentially constant in the time of integration, and signal and noise are relatively independent, warps
After crossing integration, formula (22) can be expressed as：
Wherein si,KRepresent the component of signal before being synthesized after integration, cK,N-1Represent the signal after being synthesized after integration
Component, andThe noise component(s) before being synthesized after integration is represented,Represent the noise component(s) after being synthesized after integration.Due to
Assuming that noise power keeps constant, therefore can be replaced with K=0 noise.
It is divided into component of signal and noise component(s) by weights are synthesized with reference to formula (2), then the individual sensor synthesis power in i-th (i ≠ 1)
The component of signal w of valuei,K+1With noise component(s) ηi,K+1Respectively：
As it is assumed that each sensor receptivity is identical, and received signal power keeps constant.Therefore the K section times
The mean power at interval can be replaced by the 0th section, i.e.,Now the component of signal of weights is averaged
Power is：
And the mean power of noise component(s) is：
Again because the signal to noise ratio of synthesis weights is ρw=| wK|2/|ηK|2, then | wi,K+1|2It can be expressed as：
Formula (27) and formula (28) are substituted into weights signal to noise ratio formula：
Therefore it is in the presence of the weights signal to noise ratio of a fault sensor：
Make factor gammawiIt is equal to：
Then formula (30) can arrange：
As it is assumed that each sensor receptivity is identical, if the signal to noise ratio of synthesis weights is higher, restrained in algorithmic stability
Afterwards, the power of the individual sensor weights in i-th (i ≠ 1) is constant, then：
|Wi,K+1|2≈N/(N-1) (33)
And the synthesis weights power of the synthesis weights power of fault sensor (i=1) and non-faulting sensor (i ≠ 1) it
It is equal to than β：
Then formula (31) can using abbreviation as
With reference to formula (11), (32) and (35), it can be seen that fault sensor can reduce the synthesis power of non-faulting sensor
It is worth signal to noise ratio, i.e. β value is bigger, then γwiIt is worth bigger, the synthesis weights signal to noise ratio ρ of non-faulting sensorwiIt is smaller, and then reduce and close
Into the signal to noise ratio snr of signal.Due to γwi>=N-2, the only power when fault sensor synthesis weightsWhen equal sign
Set up, therefore the synthesis weights of fault sensor are arranged to zero, synthesis loss can be reduced to greatest extent.From another
Angle considers, due to received by the 1st sensor being noise, therefore theoretic synthesis weights should be 0, so just not
Can be on the synthetically produced influence of signal.And in SUMPLE algorithms and α-SUMPLE algorithms, although the synthesis weights of fault sensor
It can reduce, but synthesize weights and be but not equal to zeroTherefore synthesis loss is added.
Based on above analysis result, the embodiment of the present invention proposes a kind of signal synthesis method GFC-SUMPLE, this method profit
The power factors of ETDGE algorithms in being estimated with time delay estimates the signal to noise ratio of each sensor input signal, when detecting certain
When the power factor of individual sensor input signal is zero, illustrate the sensor failure, can be by the synthesis weights of the sensor
Zero is arranged to, so as to reduce synthesis loss to greatest extent.
The ETDGE algorithms used in the embodiment of the present invention are briefly described below.
The article that H.C.So et al. is delivered《Performance Analysis of ETDGE–an Efficient and
Unbiased TDOA Estimator》(IEEE Proceedings.Radar, Sonar and Navigation, vol.145,
No.6, pp.325-330) in propose a kind of ETDGE algorithms.Filter coefficient formal constraint is by ETDGE algorithmsTime delay estimate is represented, upper triangle label ^ represents that the variable is estimate.
Two-way input signal is expressed as
Wherein Si,ref,kRepresent the reference signal that i-th of sensor arrives in kth reception, in real system, Ke Yixuan
The reception signal of any one sensor is taken as reference signal, si,k-DThe delay time signal of i-th of sensor is represented, when D is represented
Prolong value,Represent the reference noise that i-th of sensor arrives in kth reception.ETDGE algorithmic system block diagrams are as shown in Figure 2.
WhereinPower factor estimate of i-th of sensor at the kth moment is represented, it can be defeated to i-th of sensor
The signal to noise ratio for entering signal is tracked；hi,kFilter coefficient of i-th of sensor at the kth moment is represented,Represent i-th of biography
Sensor is in the time delay estimate at kth moment, yi,kRepresent i-th of sensor in kth moment filter output signal, ei,kRepresent the
For i sensor in kth moment error signal, the exponent number of wave filter is 2P+1.
The basic thought of ETDGE algorithms is desirable to the mean square error minimum target in filtering output result and reference signal
Under, obtain power factor estimate and time delay estimate, its filter coefficient are：
Input signal is：
Filter output signal and error signal are respectively：
Wherein symbol T represents transposition computing.The iterative formula of power factor estimate is：
Wherein μgRepresent the iteration step length of power factor estimate.The iterative formula of time delay estimate is：
Wherein μDRepresent the iteration step length of time delay estimate.
The power factor estimate being primarily upon in the embodiment of the present invention in ETDGE algorithms, it can track input signal
Signal to noise ratio, when power factor estimate is zero, it is believed that the signal to noise ratio of the sensor input signal is zero, i.e. the sensing
Device breaks down.A threshold value Δ can be set to the estimate of power factor in actual applications, when the estimation of power factor
When value is less than the threshold valueThe signal to noise ratio for thinking the sensor input signal is zero, i.e. the sensor failure.
Fig. 3 is a kind of signal synthesis method flow chart provided in an embodiment of the present invention, and this method is used for multiple sensors
In the output signal of each sensor synthesized, this method includes：
Step 301, the power factor estimate of each sensor in the multiple sensor is determined.
For example, the iterative formula according to power factor estimateDetermine institute
State the power factor estimate of each sensor in multiple sensors；Wherein, i is sensor number, and i=1,2 ..., N, N is institute
State the sensor total number of multiple sensors；K is sampled point time variable；For i-th of sensor the kth moment power
Factor estimate；For i-th of sensor the moment of kth+1 power factor estimate；μgFor power factor estimate
Iteration step length；ei,kIt is i-th of sensor in kth moment error signal；Si,kBelieve for output of i-th of sensor at the kth moment
Number；hi,kFor filter coefficient.
Step 302, according to the power factor estimate and the relation of first threshold, determine every in the multiple sensor
First synthesis weights of individual sensor.
, can be according to the power factor estimate and the relation of first threshold, to judge to sense in the embodiment of the present invention
Whether device breaks down, so as to determine the conjunction of sensor in different ways for fault sensor and non-faulting sensor
Into weights.
Step 303, using the first synthesis weights of each sensor in the multiple sensor, by the multiple biography
The output signal of each sensor is synthesized in sensor.
For example, according to formulaDetermine composite signal；Wherein, i is sensor number, i=
1,2 ..., N, N are the sensor total number of the multiple sensor；K is sampled point time variable；Ncor is between correlation time
Every；K is the time variable in units of ncor；Wi,KFor i-th of sensor K section relevant periods output signal
The first synthesis weights；Si,kFor i-th of sensor the kth moment output signal；CkTo be each in the multiple sensor
Composite signal of the output signal of sensor at the kth moment.
As shown in figure 4, in one example, before step 301, methods described also includes：Step 300 is according to recursion iteration
Algorithm determines the second synthesis weights of each sensor in the multiple sensor.Step 302 includes：Step 3021, for institute
The sensor that power factor estimate is less than first threshold is stated, the first synthesis weights of the sensor are arranged to zero；Step
3022, the sensor of the first threshold is more than or equal to for the power factor estimate, by described the of the sensor
Two synthesis weights are defined as the first synthesis weights of the sensor.
As shown in figure 5, in another example, step 302 includes：Step 3023, for the power factor estimate
Less than the sensor of first threshold, the first synthesis weights of the sensor are arranged to zero；Step 3024, for the power because
Sub- estimate is more than or equal to the sensor of the first threshold, and the first synthesis of the sensor is determined according to recursion iterative algorithm
Weights.
In example shown in Fig. 4 and Fig. 5, when determining synthesis weights using recursion iterative algorithm, formula can be used
(15) unlike, formula (15) in Fig. 4 it is confirmed that in the multiple sensor each sensor synthesis weights, in Fig. 5
Formula (15) is it is confirmed that the power factor estimate in the multiple sensor is more than or equal to the first threshold
The synthesis weights of sensor, that is, determine the synthesis weights of non-faulting sensor.
Below for Fig. 5 example, to determining that the synthesis weights of non-faulting sensor illustrate using formula (15).Root
According to the iterative formula of synthesis weights(15) the multiple biography is determined
The power factor estimate in sensor is more than or equal to the first synthesis weights of the sensor of the first threshold；Wherein,
I, j is sensor number, i=1,2 ..., N ', j=1,2 ..., N ', N ' they are the power factor in the multiple sensor
Estimate is more than or equal to the sensor total number of the first threshold；K is sampled point time variable；Ncor is between correlation time
Every；K is the time variable in units of ncor；Wi,KFor i-th of sensor K section relevant periods output signal
First synthesis weights；Wi,K+1For i-th of sensor K+1 section relevant periods output signal first synthesis weights；
Si,kFor i-th of sensor the kth moment output signal；RK+1For normalization coefficient.
The signal synthesis method that the embodiment of the present invention proposes is properly termed as GFC-SUMPLE signal synthesis methods, this method
Fault detect has been carried out in processing procedure, alternatively, after signal synthesis, formula (13) can also have been utilized, calculate this power
It is worth the signal to noise ratio of the composite signal of iteration.
Fig. 6 is a kind of GFC-SUMPLE signals synthesis system block diagram provided in an embodiment of the present invention, wherein, the system can use
Signal synthesis is carried out in fault detect, and according to the result of fault detect.
A kind of synthetic effect of the signal synthesis method proposed below by emulation to the embodiment of the present invention illustrates.
SUMPLE, α-SUMPLE (α=0.3), α-SUMPLE (α=0.5) and GFC-SUMPLE are emulated in emulation 1
Comparing, source signal is equally distributed random process, and noise is set to irrelevant zero average white Gaussian noise, parameter N=4,
Ncor=1000, the SNR ranges of input signal take -10dB~0dB, carry out 2000 independent tests.
Algorithms of different is lost to signal synthesis performance and contrasted when Fig. 7 gives fault-free sensor.It can be seen that with defeated
Enter the reduction of Signal-to-Noise, the signal synthesis performance of each algorithm all constantly declines.And due to the presence of noise, it is actual to calculate
Synthesis weights be not equal to theoretical best weight value, when fault-free sensor, the modified weight coefficient of α-SUMPLE algorithms | Wi,K
|αSo that the weights actually calculated are further offset from theoretical best weight value, and the conjunction of GFC-SUMPLE algorithms and SUMPLE algorithms
It is essentially identical into performance, excellent α-SUMPLE algorithms, extra synthesis loss will not be increased.
When Fig. 8 is given in the presence of a fault sensor, algorithms of different is lost to signal synthesis performance and contrasted.Can from Fig. 8
To find out, compared with SUMPLE algorithms, α-SUMPLE algorithms can reduce the synthesis loss of sensor array to a certain extent.But
It is the synthesis performance of GFC-SUMPLE algorithms still better than SUMPLE and α-SUMPLE algorithms.
SUMPLE, α-SUMPLE (α=0.5) and GFC-SUMPLE are subjected to emulation comparison in emulation 2, source signal is uniform
The random process of distribution, noise are set to irrelevant zero average white Gaussian noise, parameter μg=0.0003,2P+1=21, Δ=
0.04, N=10, ncor=1000, signal to noise ratio -10dB, the K=40 of input signal, wherein the 1st sensor is sent out in K=20
Raw failure, carries out 500 independent tests.
Fig. 9 gives the change contrast of ETDGE algorithms power factor estimate at the 1st sensor before and after failure occurs
Figure, it can be seen that power factor estimate converges on 0.1 before failure generation, and this is basically identical with the signal to noise ratio of input signal.
As K=21, power factor estimate starts to be decreased obviously, and as K=24, power factor estimate is equal to 0, and now explanation should
Sensor failure.This also illustrates that GFC-SUMPLE is not only able to reduce influence of the fault sensor to synthesis performance, and
With effective detection and which sensor failure can be judged, this plays an important roll in sensor array.
Figure 10 gives synthesis weights change contrast of the GFC-SUMPLE algorithms at the 1st sensor before and after failure occurs
Figure, it can be seen that synthesis weights are approximately equal to 1 before failure occurs.As K=21, synthesis weights start to be decreased obviously, and work as K=
When 24,0 is arranged to by weights are synthesized according to GFC-SUMPLE algorithms.
Figure 11 gives the signal synthesis performance loss comparison diagram of algorithms of different before and after failure occurs.As can be seen that in event
GFC-SUMPLE algorithms are identical with the synthesis performance of SUMPLE algorithms before barrier occurs, better than α-SUMPLE algorithms.And sent out in failure
After life, as K >=24, because GFC-SUMPLE algorithms complete the amendment of synthesis weights, therefore its synthesis performance is better than
SUMPLE and α-SUMPLE algorithms.
With reference to above simulation result, it can be seen that GFC-SUMPLE is in signal synthesis performance and fault sensor context of detection
Advantage.
Figure 12 is a kind of signal synthesis system structural representation provided in an embodiment of the present invention, and the system is used to perform this hair
The signal synthesis method that bright embodiment provides, the system are used to be closed the output signal of each sensor in multiple sensors
Into the system includes：
Power factor estimate determining module 1201, for determine the power of each sensor in the multiple sensor because
Sub- estimate；
First synthesis weights determining module 1202, for what is determined according to the power factor estimate determining module 1201
The relation of power factor estimate and first threshold, determine the first synthesis weights of each sensor in the multiple sensor；
Signal synthesizing module 1203, for the multiple biography determined using the described first synthesis weights determining module 1202
The first synthesis weights of each sensor, the output signal of each sensor in the multiple sensor is carried out in sensor
Synthesis.
Alternatively, the power factor estimate determining module 1201, is specifically used for：According to changing for power factor estimate
For formulaDetermine the power factor estimate of each sensor in the multiple sensor；
Wherein, i is sensor number, and i=1,2 ..., N, N is the sensor total number of the multiple sensor；K is the sampled point time
Variable；For i-th of sensor the kth moment power factor estimate；It is i-th of sensor at the moment of kth+1
Power factor estimate；μgFor the iteration step length of power factor estimate；ei,kBelieve for i-th of sensor in kth moment error
Number；Si,kFor i-th of sensor the kth moment output signal；hi,kFor filter coefficient.
In one example, as shown in figure 13, the system also includes：
Second synthesis weights determining module 1200, described in being determined in the power factor estimate determining module 1201
In multiple sensors before the power factor estimate of each sensor, the multiple sensor is determined according to recursion iterative algorithm
In each sensor the second synthesis weights；
The first synthesis weights determining module 1202, is specifically used for：It is less than first for the power factor estimate
The sensor of threshold value, the first synthesis weights of the sensor are arranged to zero；It is more than or waits for the power factor estimate
In the sensor of the first threshold, the first synthesis that the second synthesis weights of the sensor are defined as to the sensor is weighed
Value.
In another example, the first synthesis weights determining module 1202, it is specifically used for：For the power factor
Estimate is less than the sensor of first threshold, and the first synthesis weights of the sensor are arranged into zero；For the power factor
Estimate is more than or equal to the sensor of the first threshold, and the first synthesis for determining the sensor according to recursion iterative algorithm is weighed
Value.
Alternatively, the first synthesis weights determining module 1202, is specifically used for：According to the iterative formula of synthesis weightsDetermine that first of each sensor in the multiple sensor closes
Into weights；Wherein, i, j are sensor number, i=1,2 ..., N ', j=1,2 ..., N ', N ' they are in the multiple sensor
The power factor estimate is more than or equal to the sensor total number of the first threshold；K is sampled point time variable；ncor
For relevant periods；K is the time variable in units of ncor；Wi,KIt is i-th of sensor in K section relevant periods
Output signal first synthesis weights；Wi,K+1For i-th of sensor K+1 section relevant periods output signal
One synthesis weights；Si,kFor i-th of sensor the kth moment output signal；RK+1For normalization coefficient.
Alternatively, the signal synthesizing module 1203, is specifically used for：According to formulaIt is determined that synthesis letter
Number；Wherein, i is sensor number, and i=1,2 ..., N, N is the sensor total number of the multiple sensor；When k is sampled point
Between variable；Ncor is relevant periods；K is the time variable in units of ncor；Wi,KIt is i-th of sensor in K section phases
Close the first synthesis weights of the output signal of time interval；Si,kFor i-th of sensor the kth moment output signal；Ck
For each sensor in the multiple sensor output signal the kth moment composite signal.
Signal synthesis method provided in an embodiment of the present invention has the following advantages that：(1) embodiment of the present invention proposes one kind
Can fault detection method in signal building-up process, which be detected in sensor array by the power factor of ETDGE algorithms
Individual sensor failure；(2) when sensor array does not break down, synthesis weights are consistent with SUMPLE algorithms, will not
Increase extra synthesis loss；(3) when sensor array breaks down, the synthesis weights of fault sensor can be arranged to
Zero, synthesis loss is reduced to greatest extent.
In a kind of signal building-up process that the embodiment of the present invention proposes sensing is detected using the power factor of ETDGE algorithms
The method and system of device array failure；When sensor array does not break down, synthesis weights are consistent with SUMPLE algorithms, no
Extra synthesis loss can be increased；When sensor array breaks down, the synthesis weights of fault sensor can be arranged to
Zero, synthesis loss is reduced to greatest extent.
Professional should further appreciate that, each example described with reference to the embodiments described herein
Unit and algorithm steps, it can be realized with electronic hardware, computer software or the combination of the two, it is hard in order to clearly demonstrate
The interchangeability of part and software, the composition and step of each example are generally described according to function in the above description.
These functions are performed with hardware or software mode actually, application-specific and design constraint depending on technical scheme.
Professional and technical personnel can realize described function using distinct methods to each specific application, but this realization
It is it is not considered that beyond the scope of this invention.
The method that is described with reference to the embodiments described herein can use hardware, computing device the step of algorithm
Software module, or the two combination are implemented.Software module can be placed in random access memory (RAM), internal memory, read-only storage
(ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technical field
In any other form of storage medium well known to interior.
Above-described embodiment, the purpose of the present invention, technical scheme and beneficial effect are carried out further
Describe in detail, should be understood that the embodiment that the foregoing is only the present invention, be not intended to limit the present invention
Protection domain, within the spirit and principles of the invention, any modification, equivalent substitution and improvements done etc., all should include
Within protection scope of the present invention.
Claims (10)
1. a kind of signal synthesis method, it is characterised in that methods described is used for the output of each sensor in multiple sensors
Signal is synthesized, and methods described includes：
Determine the power factor estimate of each sensor in the multiple sensor；
According to the power factor estimate and the relation of first threshold, of each sensor in the multiple sensor is determined
One synthesis weights；
Using the first synthesis weights of each sensor in the multiple sensor, will each be passed in the multiple sensor
The output signal of sensor is synthesized.
2. the method as described in claim 1, it is characterised in that the work(for determining each sensor in the multiple sensor
Rate factor estimate, including：
According to the iterative formula of power factor estimateDetermine every in the multiple sensor
The power factor estimate of individual sensor；
Wherein, i is sensor number, and i=1,2 ..., N, N is the sensor total number of the multiple sensor；
K is sampled point time variable；
For i-th of sensor the kth moment power factor estimate；
For i-th of sensor the moment of kth+1 power factor estimate；
μgFor the iteration step length of power factor estimate；
ei,kIt is i-th of sensor in kth moment error signal；
Si,kFor i-th of sensor the kth moment output signal；
hi,kFor filter coefficient.
3. the method as described in claim 1, it is characterised in that the work(for determining each sensor in the multiple sensor
Before rate factor estimate, methods described also includes：
The second synthesis weights of each sensor in the multiple sensor are determined according to recursion iterative algorithm；
It is described according to the power factor estimate and the relation of first threshold, determine each sensor in the multiple sensor
First synthesis weights, including：
It is less than the sensor of first threshold for the power factor estimate, the first synthesis weights of the sensor is arranged to
Zero；
It is more than or equal to the sensor of the first threshold for the power factor estimate, by described the second of the sensor
Synthesis weights are defined as the first synthesis weights of the sensor.
4. the method as described in claim 1, it is characterised in that described according to the power factor estimate and first threshold
Relation, the first synthesis weights of each sensor in the multiple sensor are determined, including：
It is less than the sensor of first threshold for the power factor estimate, the first synthesis weights of the sensor is arranged to
Zero；
It is more than or equal to the sensor of the first threshold for the power factor estimate, is determined according to recursion iterative algorithm
First synthesis weights of the sensor.
5. method as claimed in claim 4, it is characterised in that described to be more than or equal to institute for the power factor estimate
The sensor of first threshold is stated, the first synthesis weights of the sensor are determined according to recursion iterative algorithm, including：
According to the iterative formula of synthesis weightsDetermine the multiple
First synthesis weights of each sensor in sensor；
Wherein, i, j are sensor number, i=1,2 ..., N ', j=1,2 ..., N ', N ' they are described in the multiple sensor
Power factor estimate is more than or equal to the sensor total number of the first threshold；
K is sampled point time variable；
Ncor is relevant periods；
K is the time variable in units of ncor；
Wi,KFor i-th of sensor K section relevant periods output signal first synthesis weights；
Wi,K+1For i-th of sensor K+1 section relevant periods output signal first synthesis weights；
Si,kFor i-th of sensor the kth moment output signal；
RK+1For normalization coefficient.
6. the method as any one of claim 1 to 5, it is characterised in that described using every in the multiple sensor
The first synthesis weights of individual sensor, the output signal of each sensor in the multiple sensor is synthesized, wrapped
Include：
According to formulaDetermine composite signal；
Wherein, i is sensor number, and i=1,2 ..., N, N is the sensor total number of the multiple sensor；
K is sampled point time variable；
Ncor is relevant periods；
K is the time variable in units of ncor；
Wi,KFor i-th of sensor K section relevant periods output signal it is described first synthesis weights；
Si,kFor i-th of sensor the kth moment output signal；
CkFor each sensor in the multiple sensor output signal the kth moment composite signal.
7. a kind of signal synthesis system, it is characterised in that the system is used for the output of each sensor in multiple sensors
Signal is synthesized, and the system includes：
Power factor estimate determining module, for determining that the power factor of each sensor in the multiple sensor is estimated
Value；
First synthesis weights determining module, for the power factor estimation determined according to the power factor estimate determining module
The relation of value and first threshold, determine the first synthesis weights of each sensor in the multiple sensor；
Signal synthesizing module, for each being passed in the multiple sensor using the described first synthesis weights determining module determination
The first synthesis weights of sensor, the output signal of each sensor in the multiple sensor is synthesized.
8. system as claimed in claim 7, it is characterised in that the power factor estimate determining module, be specifically used for：
According to the iterative formula of power factor estimateDetermine every in the multiple sensor
The power factor estimate of individual sensor；
Wherein, i is sensor number, and i=1,2 ..., N, N is the sensor total number of the multiple sensor；
K is sampled point time variable；
For i-th of sensor the kth moment power factor estimate；
For i-th of sensor the moment of kth+1 power factor estimate；
μgFor the iteration step length of power factor estimate；
ei,kIt is i-th of sensor in kth moment error signal；
Si,kFor i-th of sensor the kth moment output signal；
hi,kFor filter coefficient.
9. system as claimed in claim 7, it is characterised in that the system also includes：
Second synthesis weights determining module, used in determining the multiple sensor in the power factor estimate determining module
Before the power factor estimate of each sensor, each sensor in the multiple sensor is determined according to recursion iterative algorithm
Second synthesis weights；
The first synthesis weights determining module, is specifically used for：
It is less than the sensor of first threshold for the power factor estimate, the first synthesis weights of the sensor is arranged to
Zero；
It is more than or equal to the sensor of the first threshold for the power factor estimate, by described the second of the sensor
Synthesis weights are defined as the first synthesis weights of the sensor.
10. system as claimed in claim 7, it is characterised in that the first synthesis weights determining module, be specifically used for：
It is less than the sensor of first threshold for the power factor estimate, the first synthesis weights of the sensor is arranged to
Zero；
It is more than or equal to the sensor of the first threshold for the power factor estimate, is determined according to recursion iterative algorithm
First synthesis weights of the sensor.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108600324A (en) * | 2018-03-27 | 2018-09-28 | 中国科学院声学研究所 | A kind of signal synthesis method and system |
| CN110688365A (en) * | 2019-09-18 | 2020-01-14 | 华泰证券股份有限公司 | Method and device for synthesizing financial time series and storage medium |
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2017
- 2017-05-05 CN CN201710311860.0A patent/CN107425928B/en active Active
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| H.C. SO: "Performance analysis of ETDGE - an efficient and unbiased TDOA estimator", 《IEE》 * |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108600324A (en) * | 2018-03-27 | 2018-09-28 | 中国科学院声学研究所 | A kind of signal synthesis method and system |
| CN108600324B (en) * | 2018-03-27 | 2020-07-28 | 中国科学院声学研究所 | Signal synthesis method and system |
| CN110688365A (en) * | 2019-09-18 | 2020-01-14 | 华泰证券股份有限公司 | Method and device for synthesizing financial time series and storage medium |
| WO2021051976A1 (en) * | 2019-09-18 | 2021-03-25 | 华泰证券股份有限公司 | Financial time sequence synthesis method and device, and storage medium |
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