CN106021697B - A kind of rapid phase-control battle array radar Time-energy resource joint management method - Google Patents

A kind of rapid phase-control battle array radar Time-energy resource joint management method Download PDF

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CN106021697B
CN106021697B CN201610325318.6A CN201610325318A CN106021697B CN 106021697 B CN106021697 B CN 106021697B CN 201610325318 A CN201610325318 A CN 201610325318A CN 106021697 B CN106021697 B CN 106021697B
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程婷
张洁
张宇轩
李姝怡
王栋
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University of Electronic Science and Technology of China
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Abstract

The invention belongs to phased array technology fields, a kind of rapid phase-control battle array radar Time-energy resource joint management method is specifically provided, the existing phased-array radar Time-energy method for managing resource based on error covariance is computationally intensive, operation is slow several times, system resources in computation consumes big defect to overcome.The present invention initially sets up the offline library of target maneuver parameter, on this basis, based on to target maneuver parameter progress real-time estimation, the adjustment location error variance of every kind of waveform in radar waveform library is predicted with tracking accuracy simultaneously, then each waveform corresponding sampling period is quickly calculated according to equation, the waveform for most saving radar resource is finally selected from all waveforms；Effectively avoid calculating the error co-variance matrix of all parameter combinations；To substantially reduce calculation amount, calculating speed is significantly improved, effectively saves radar computing resource；And this method can carry out the tracking accuracy of control algolithm by adjusting desired location error variance.

Description

A kind of rapid phase-control battle array radar Time-energy resource joint management method
Technical field
The invention belongs to phased array technology fields, specifically design a kind of rapid phase-control battle array radar Time-energy resource Corporate management method.
Background technique
Phased-array radar is to study extensively at present and the radar of application, since its wave beam can be arbitrarily directed toward, and can be Agile is carried out in Microsecond grade, thus there is multi-functional and height adaptive ability, and flexibility is very big.Effectively to distribute phased array thunder All kinds of resources reached, so that radar system resource and entire task load match, to give full play to its performance, it is necessary to phase It controls battle array radar and implements effective resource management；Phased-array radar resource management is broadly divided into three parts: search management, tracking pipe Reason and task schedule are (see document: S.L.C.Miranda, C.J.Baker, K.Woodbridge, et al.Knowledge- based resource management for multifunction radar[J].IEEE Signal Processing Magazine, 2006, (6): 66-76), wherein tracing management is portion relatively complicated in phased-array radar resource management Point.On the one hand tracing management will be resident echo data to the tracking that system obtains and carry out data processing, to extract the shape of each target State information；On the other hand, it will determine the execution parameter of later period tracing task to be formed according to the target status information obtained Tracing task request.The execution parameter of tracing task includes sampling period, transmitted waveform etc., wherein to time dependent parameter Control is known as time resource management, is known as energy resource management to the control with energy-related parameter.
The optimization distribution of time resource is realized by the way that the reasonable tracking sampling period is arranged to each tracing task.? In terms of adaptive sampling period strategy, Van Keuk obtained first the Singer model down-sampling period analytical expression (see Document: Adapative controlled target tracking with a phased array radar [J] .Van Keuk, G.IEEE International Radar Conference.April 21-23,1975,429-432), referred to as public Formula method, the motor-driven relating to parameters in sampling period and Singer model in this method.With the promotion of target maneuver performance, occur The adaptive sampling period algorithm of Interactive Multiple-Model (Interacting Multiple Model, IMM) based on multi-model, Watson and Blair proposes a kind of predicting covariance threshold method based on covariance threshold judgement to select the sampling period (see document: Watson G A, Blair W D.Tracking performance of a phased array radar with revisit time controlled using the IMM algorithm.[C]Radar Conference, 1994,Record of the 1994IEEE National.IEEE,1994:160-165.).H.Benoudnin etc. is proposed A kind of quick self-adapted sampling period method (Fast Adaptive update rate control in the based on IMM IMM Algorithm, FAIMM) (see document: Fast adaptive update rate for phased array radar using IMM target tracking algorithm.H.Benoudnine,M.Keche,A.Ouamri, M.S.Woolfson.IEEE International Symposium on Signal Processing and Information Technology,2006)。
In terms of Time-energy resource joint optimizes distribution, W.H.Gilson is fully considering target maneuver characteristic Under the premise of, give minimum power consumed by radar and target tracking accuracy, tracking sampling period and letter under tracking mode The functional relation made an uproar than between is (see document: W.H.Gilson.Minimum power requirements for tracking [C].IEEE International Radar Conference,New York,1990:417-421).Kirubarajan etc. When people has studied the radar tracking under false-alarm and electronic interferences environment Resources Management (see document: T.Kirubarajan, Y.Bar-Shalom,W.D.Blair,et al.IMMPDF for radar management and tracking benchmark with ECM[J].IEEE Transactions on Aerospace and Electronic Systems, 1998,34 (4): 1115-1134), using the angle measurement error variance of prediction and prediction, signal-to-noise ratio is adaptively selected adopts in text Sample period and waveform with different emitted energies.Selection for waveform adaptive under multi-target condition, Chinese scholar Lu Jian It is refined etc. to propose a kind of united beam waveform adaptive scheduling algorithm based on covariance control thought (see document: Lu Jianbin, Xiao Intelligent, Xi Zemin waits joint adaptive scheduling algorithm's [J] the system engineering and electronic technology of phased-array radar wave beam waveform, and 2011, 33 (1): 84-88), the method based on covariance control, for adaptively selected tracking target sequence number of each moment and accordingly Waveform, to realize the adaptively selected of each destination sample period and waveform.From above-mentioned document as it can be seen that for each tracking mesh Target Time-energy resource joint management, common method is: being realized by making predicting covariance be less than thresholding to sampling The control in period predicts that signal-to-noise ratio is less than selection of the thresholding realization to waveform by making.Assuming that the possibility value in sampling period Number is M, and the number of waveform to be selected is N, then this method needs to calculate M × N number of parameter combination covariance matrix, and determination meets item The feasible parameter combination of part, and therefrom select the combination for most saving radar resource；It is missed due to needing to calculate all parameter combinations Poor covariance matrix, judges whether it meets constraint condition, causes such method computationally intensive, arithmetic speed is slow, system-computed Resource consumption is big.
Summary of the invention
M- energy when the rapid phase-control battle array radar that the object of the present invention is to provide a kind of based on target maneuver parameter Estimation Resource joint management method is measured, this method carries out real-time estimation to target maneuver parameter, while to every kind in radar waveform library Adjustment location error variance and the tracking accuracy of waveform are predicted, each waveform pair is then quickly calculated according to equation The sampling period answered finally selects the waveform for most saving radar resource from all waveforms, and this method can be by adjusting Desired location error variance carrys out the tracking accuracy of control algolithm.
In order to which the sampling period is quickly calculated, the present invention initially sets up the offline library of target maneuver parameter, in real-time tracking In, need to only be searched in offline library and the matched motor-driven parameter of current time model probability, it can be rapidly true according to equation Determine the sampling period.
Common several concepts of the invention are explained first:
Singer model: Singer model is by aimed acceleration a (t) as with the random mistake of the autocorrelative zero-mean of index The auto-correlation function of Cheng Jianmo, R (ε) expression aimed acceleration:
R (ε)=E [a (t) a (t+ ε)]=σ2e-α|ε| (1)
Wherein, ε is correlation time；{α,σ2Be Singer model motor-driven parameter, α is model maneuvering frequency, σ2It is model Acceleration variance；Model motor-driven parameter { α, σ2Determine the maneuvering characteristics that model represents.
Model probability matching degree: matching degree of the model probability matching degree to measure two group model probability, it is assumed that two groups Model probability is respectively as follows:
Use μi1, μi2Respectively indicate two group model probability U1With U2In i-th of element, 1≤i≤N, N indicate Number of Models, Then model probability correspondence function are as follows:
Functional value f (U1,U2) smaller, then U1With U2Matching degree it is higher.
Equation: it under Singer model, usesIndicate the predicted position error variance of target,Indicate the measurement of target Location error variance, tracking accuracyWithWithThe ratio between indicate:
The then calculation formula of next sampling period T are as follows:
In above formula,For the estimation of the maneuvering frequency of target,For the acceleration standard deviation of target,WithFor target fortune Dynamic motor-driven parameter can obtain estimating in real time to target maneuver parameter by the probability of each model when using IMM filter Meter.
In order to realize quick phased-array radar Time-energy resource joint management, the present invention needs design object first The motor-driven offline library of parameter selects optimum waveform and corresponding sampling period on this basis from waveform library, realizes that clock synchronization is m- The corporate management of energy resource；So technical solution of the present invention includes following two part: the foundation in the motor-driven offline library of parameter With real-time time-energy resource corporate management.
The invention adopts a technical scheme as: a kind of rapid phase-control battle array radar Time-energy resource joint management method, including Following steps:
Establish the offline library of motor-driven parameter:
Step 1: N number of Singer model interacts in setting IMM filter, the motor-driven parameter of each model are as follows:
Discretization is carried out to model probability valued space [0,1], it is assumed that the feasible total M of probabilistic combination after discretization, then Probabilistic combination set U are as follows:
U={ U1,U2,…,UM} (7)
Wherein, the either element U of set UjAre as follows:
Uj={ μ1j,μ2j…,μNj},1≤j≤M (8)
For Uj={ μ1j,μ2j…,μNj, μijIndicate probabilistic combination UjIn i-th of element, μijIt needs to meet:
Step 2: in each feasible probabilistic combination Uj, under j=1,2 ..., M, estimate the acceleration auto-correlation letter of target Number
It calculatesMain value interval boundary pointMake:
Wherein, λ be invariant and 0 < λ < 1, it is rightSection carry out discretization, it is discrete after value set expression Are as follows:
Wherein, the number r > > 2 of ε value；
Step 3: in probabilistic combination UjUnder, calculate vectorValue:
Wherein:
Step 4: probabilistic combination U is calculatedjThe estimated value of lower target maneuver parameter:
Wherein,Indicate vectorFirst element,Indicate vectorSecond element；By probability Combine UjCorresponding motor-driven parameterIt is stored in the offline library of motor-driven parameter；
Step 5: establishing the offline library of target maneuver parameter, wherein including feasible probabilistic combination set U and corresponding mesh Mark motor-driven parameter sets C；Set U is shown in formula (7) that target maneuver parameter sets C is expressed as follows:
C={ C1,C2,…,CM} (17)
Wherein,
Real-time time-energy resource corporate management:
Step 1: next sampling instant is predicted:
Wherein,For the prediction to k-th of sampling instant, tk-1For -1 sampling instant of kth, T (tk-1) adopted for kth -1 Sample interval；
Step 2: using the output of IMM filter as a result, calculatingThe Prediction distance value at momentAnd azimuth Prediction error variance
Wherein,Exist for targetThe predicted position at moment,It is missed for the predicted position under spherical coordinates Poor covariance matrix；
Step 3: assuming that having W kind waveform, respectively w in phased-array radar waveform libraryl(l=1,2 ..., W), in waveform wl Under, calculate its corresponding prediction signal-to-noise ratio
Wherein, B is the round trip beam angle of transmitting-receiving,For azimuthal prediction error variance,For The prediction signal-to-noise ratio in beam position direction:
τlFor waveform wlTransmitting signal pulsewidth,For waveform wlPulse repeat number, PtFor transmitter general power, Gt And GrThe respectively transmitter antenna gain (dBi) and receiving antenna gain of radar, LtotFor the total losses of radar system, N0For noise power Spectrum density, N0=kT0F0, k is Boltzmann constant, T0For radar receiver temperature, F0For radar receiver noise coefficient,For tk-1Moment target is averaged the estimated value of RCS；
Step 4: in waveform wlUnder, the measuring standard of the target radial of prediction distance, azimuth and pitch angle is poor are as follows:
Wherein,For range resolution,WithTypical value for beam angle, constant c is 1.57；Error in measurement is converted to rectangular coordinate system, then waveform wlCorresponding measurement covariance matrixAre as follows:
Wherein, J is the Jacobian transition matrix from spherical coordinates to rectangular coordinate system；Then waveform wlThe amount of corresponding target Location sets error varianceAre as follows:
Step 5: desired target predicted position error variance is determinedCalculate waveform wlCorresponding tracking accuracy
Step 6: IMM filter prediction is utilizedThe model probability at momentIn the offline library of target maneuver parameter Search withThe highest model probability combination of its matching degreeAsk:
It is corresponding search withCorresponding target maneuver parameterWherein, the meter of function f Formula:
Step 7: waveform w is calculated using equationlCorresponding sampling period Tl(tk):
Step 8: the radar resource for calculating every kind of waveform consumes cost function:
Wherein, El, (l=1,2 ..., W) is phased-array radar work consumed energy, c on each waveform1With c2 For the weighting coefficient of energy resource and time resource, 0≤c1≤ 1,0≤c2≤ 1 and c1+c2=1；Then optimum waveform subscript are as follows:
The corresponding sampling period are as follows:
Step 9: next sampling instant is determined are as follows:
tk=tk-1+T(tk) (33)
Then tkMoment uses waveformTarget is tracked；
Repeat above step.
The working principle of the invention is:
The time resource management of phased-array radar realizes that energy resource management passes through selection work by the control sampling period It is realized as waveform；Assuming that having W kind waveform, respectively w in radar waveform libraryl(l=1,2 ..., W), it is known that in tk-1Moment is complete At scheduled tracing task；Then next it needs to be determined that next sampling instant tkAnd tkIt is most preferably tracked used by moment Waveform.
For sampling instant tkDetermination, equation have speed it is fast, the small feature of calculation amount；It is adopted by equation calculating When the sample period, the motor-driven parameter real-time estimation to target is needed, but for the IMM filter comprising multiple Singer models, Since the model parameter of each Singer model is different, need by the model probabilities of multiple models to the motor-driven parameter of target into Row estimation, in order to rapidly estimate target maneuver parameter, the offline library of the motor-driven parameter of design object；On this basis, due to every kind The accuracy in measurement of waveform is different, and the sampling period of oneself can be calculated in every kind of waveform by equation；Compare each waveform to be disappeared The resource of consumption can determine best tracking waveform and its corresponding sampling period；Pass through equation, it will be able to complete to phased The corporate management of battle array radar Time-energy resource.
By formula (5), the calculating of equation needs known following parameter: adjustment location error variance, tracking accuracy, target Maneuvering frequency, acceleration standard deviation.
For the first two parameter, the calculated result of every kind of waveform is different；Next sampling instant is predicted, sees formula (18), then it can calculate at the prediction samples momentUnder, waveform wlCorresponding prediction signal-to-noise ratioAs shown in formula (21), In every kind of waveform beam position direction prediction signal-to-noise ratioAre as follows:
As it can be seen that due to the transmitting signal pulsewidth τ of each waveformlDifference, for different waveforms, signal-to-noise ratio is not identical； The accuracy in measurement of the corresponding radial distance of different wave, azimuth and pitch angle is not also identical, sees formula (23)；Go to right angle seat Under mark system, the corresponding adjustment location error variance of different wave is not also identical, sees formula (24) and formula (25)；Different wave is corresponding Tracking accuracy is not also identical, sees formula (26)；Therefore, different waveforms will embody different adjustment location errors in equation Variance and tracking accuracy, so the adaptive waveform selection based on equation is feasible.
Latter two parameter needed for equation: the maneuvering frequency and acceleration standard deviation of target are needed in tracking, Real-time estimation is carried out to it according to the motion conditions of target；Consider the IMM problem with N number of Singer model, the machine of each model Dynamic parameter are as follows:
Wherein, αiFor the maneuvering frequency of each model；For the acceleration variance of each model；For Singer model, model i Aimed acceleration ai(t) auto-correlation function are as follows:
At current time, when above-mentioned N number of model interacts, the acceleration auto-correlation function of target is in least mean-square error Optimal estimation under meaning are as follows:
Wherein, μiFor the probability of current time model i,For the estimation of the acceleration variance of current target,For The estimation of current target maneuvering frequency；
Logarithm is taken about natural number e to second equal sign both sides of (38) formula:
It, will in (39) formulaWithAs unknown number, then | ε | when taking different values, it can establish different lines Property equation；For | ε | value selection, by
It can be seen thatIt is even function, andFor the weighted array of the exponential function of N number of decline, thenValue with | ε | increase and reduce；It takes:
Wherein, 0 < λ < 1；It willAsMain value interval carries out discretization to the section, obtains r It is a discrete | ε | value, be respectively as follows: | ε1|,|ε2|,…,|εr|}；The system of linear equations comprising r equation can then be established:
Ax=b (42)
Wherein, the expression formula of unknown number x is shown below:
The expression of A, b such as formula (14), shown in formula (15)；
In order to estimate the maneuvering frequency and acceleration variance of target as precisely as possible, when establishing the system of linear equations, | ε | value number r should be much larger than unknown number number 2, r > > 2, i.e. equation number be much larger than unknown quantity number；At this point, System of linear equations can find one without solutionSo that error vector e obtains minimum under least square meaning, even if The quadratic sum of the mould of evaluated error
J=eHE=(Ax-b)H(Ax-b) (44)
Minimum is obtained, obtained solution is least square solution, sees formula (13)；The least square solution for obtaining equation group it Afterwards, formula (16) are shown in the estimation that can obtain the maneuvering frequency and acceleration variance of current target；
Due to needing to matrix inversion, if at every sampling moment when solving the least square solution of system of linear equations If all carrying out On-line Estimation to target maneuver parameter, system resources in computation consumption is big.So the present invention considers design object It the motor-driven offline library of parameter only need to be according to the model prediction probability at current time in the offline library of motor-driven parameter in real-time tracking Matching motor-driven parameter is searched, to improve arithmetic speed, reduces system resources in computation consumption.
For having the IMM there are two model, if model probability value interval [0,1] is pressed 0.1 discretization of step-length are as follows:
{0,0.1,0.2,…,0.9,1} (45)
Then corresponding all feasible probabilistic combination set U are as follows:
The probabilistic combination set U of 1 two models of table
U1 U2 U3 U4 U5 U6 U7 U8 U9 U10 U11
0,1 0.1,0.9 0.2,0.8 0.3,0.7 0.4,0.6 0.5,0.5 0.6,0.4 0.7,0.3 0.8,0.2 0.9,0.1 1,0
When carrying out real-time tracking to target, under normal circumstances, the value of the probability of two models will not exactly set U In element；For example, the probability of two models of current time is { 0.13,0.87 }, at this point, general using model in real-time tracking Rate correspondence function is searched in set U and is combined with the current time highest model probability of model probability combinations matches degree, is calculated Method is shown in formula (28)；Model probability combination is then corresponded to, the estimation of target maneuver parameter can be obtained in the motor-driven offline library of modelAfter aforementioned four parameter has been determined, each waveform corresponding sampling week can be calculated according to formula French (29) Phase.
There are W kind waveform, respectively w in radar waveform libraryl(l=1,2 ..., W), phased-array radar work in each wave Consumed energy is respectively E in shapel, (l=1,2 ..., W) is represented by the mean power of radar waveform here.Each The waveform corresponding sampling period is respectively Tl, (l=1,2 ..., W)；Assuming that tkMoment uses waveform wl, then tkMoment radar money It includes two parts that source, which consumes cost function: energy resource consumption is consumed with time resource, and radar resource consumes cost function form Such as formula (30):
The previous item of formula (46) indicates that work is consumed in the energy resource of l kind waveform, and latter indicates work in l kind The time resource of waveform consumes；Since waveform power and sampling time are two entirely different factors, dimension in cost function Also not identical, therefore summation cannot be directly weighted to it, thus respectively to the maximum value of waveform power and sampling time into Row normalized, c1And c2Weighted value respectively after energy resource and time resource normalization, 0≤c1≤ 1,0≤c2≤1 And c1+c2=1.
According to tkThe moment the smallest principle of phased-array radar resource consumption cost, most preferably tracking waveformSubscript determine See formula (31)；The sampling period sees formula (32) accordingly, next sampling instant tkReally definite opinion formula (33), i.e. tkMoment uses wave ShapeTarget is tracked.
In conclusion the present invention provides a kind of rapid phase-control battle array radar Time-energy based on target maneuver parameter Estimation Resource joint management method initially sets up the offline library of target maneuver parameter, on this basis, based on to the progress of target maneuver parameter Real-time estimation, while the adjustment location error variance of every kind of waveform in radar waveform library is predicted with tracking accuracy, so Each waveform corresponding sampling period is quickly calculated according to equation afterwards, finally selects most to save radar from all waveforms The waveform of resource；Effectively avoid calculating the error co-variance matrix of all parameter combinations；To substantially reduce calculation amount, significantly mention High calculating speed effectively saves radar computing resource；And this method can be controlled by adjusting desired location error variance The tracking accuracy of algorithm processed.
Detailed description of the invention
Fig. 1 is the real motion track of target in embodiment.
Fig. 2 is sampling interval change curve and waveform number change curve in embodiment.
Fig. 3 is the RMSE curve of rapid phase-control battle array radar Time-energy resource joint management method in embodiment.
Fig. 4 is the RMSE curve of existing method.
Specific embodiment
The present invention is described in further details with reference to the accompanying drawings and examples.
Based on technical solution of the present invention, motion state in the present embodiment according to the target adaptive decision sampling period with And adaptively selected waveform, and compared using the existing method based on error covariance.
It is assumed that the total array number of the antenna of radar is 2048, radar transmitter peak power is 108W, antenna transmitting gain and Reception gain is respectively 45dB and 44dB, and system total losses are 7.5dB, radar false alarm probability 10-6, working frequency 10GHz, method 0.2 ° wide, the filter gain 0.3 to 3dB wave, wavelength 3cm, antenna spacing differ half wavelength；It is fighter plane by object definition A kind of target, RCS relief model are SwerlingI type, and average RCS value is 1m2。
Assuming that a total of 8 kinds of waveforms in radar waveform library, pulse reduced width is 0.15us, and distance resolution is 22.5m, the pulse width of 8 kinds of waveforms are respectively { 0.15,0.3,0.45,0.6,0.75,0.9,1.05,1.2 } us, and waveform is put down Equal power is respectivelyEnergy resource and time resource normalize Weighted value c afterwards1、c2It is 0.5；In the conventional method, sampling period optional parameters set are as follows: 2,1.9,1.8,1,7 ..., 0.4,0.3,0.2 } s, in method proposed by the present invention and existing method, desired target predicted position error to standard deviation is all set It is set to 50m.
If initial distance of the target away from radar is 230km, height 15km, target velocity holding 2Ma are first 50s at the uniform velocity Linear motion height rises to 17.2km, then does the constant speed rate turning motion of the 3g of 25s, is then 30s's on new course After unaccelerated flight, the constant speed rate turning motion of the 3g of a lasting 30s is remake, is finally flied at a constant speed to away from radar 300km Local track terminate, entire track continues 150s, and entirely target is gradually increased away from the distance of radar in track；Target True movement track is as shown in Figure 1.
IMM filter uses three typical Singer models, model maneuvering frequency and acceleration variance be respectively as follows: (1, 0.002), (1/60,1500), (1/20,5400) respectively represent non-maneuver, weak motor-driven and strong maneuver modeling；In IMM algorithm The initial model probability of three models is taken as 1/3, Markov model transition probability matrix are as follows:
The sampling interval change curve of two methods is drawn, as shown in Figure 2；Due to each Monte-Carlo sampling when Between be all different with sampled data, so target run duration is carried out equal part, since 0 ing, every 8s unites as a section The sampling number and each sampling interval, calculating averaged sampling interval for counting the section draw curve.Using same method Waveform number change curve and its RMSE curve are drawn, respectively as shown in Fig. 2, Fig. 3 and Fig. 4.
As shown in Figure 2, the sampling interval of two methods, target maneuver was strong, sampling with the motor-driven situation variation of target Interval reduces；Target maneuver is weak, and the sampling interval increases；And with the increase of distance between target and radar, the sampling interval is in The trend being gradually reduced.It is numbered and is schemed by waveform, with the increase of distance between target and radar, waveform number becomes in what is become larger The higher waveform of gesture, i.e. choice accuracy, compared with existing method, method waveform number growth trend proposed by the present invention is relatively slow. By Fig. 3 and Fig. 4 it is found that the RMSE of two methods is below the RMSE of observation, illustrate that two methods can guarantee filtering Validity.
In order to further compare the performance of algorithm, it is assessed in terms of following three: target tracking accuracy, radar System resources consumption amount, the operand of algorithm use average position error AMSE, average resource consumption respectivelyProgram is averagely transported The row timeCharacterization.
Average position error:
Average resource consumption:
Program average operating time:
Wherein, NMCFor the number of Monte-Carlo；MnFor the sampling number of n-th Monte-Carlo, xkExist for target The actual position of k-th of sampling instant,In the target state estimator position of k-th of sampling instant when for n-th Monte-Carlo,The radar resource consumption of k-th of sampling instant when for n-th Monte-Carlo,When for n-th Monte-Carlo The runing time of program.
Method proposed by the present invention is compared with the existing radar resource management algorithm based on error covariance, table 2 be the performance comparison result of both methods.
The performance comparison result of table 2 proposition method and existing method of the present invention
As can be seen from the above table, the tracking accuracy of two methods is suitable with consumed resource, i.e., the present invention may be implemented existing Methodical effect, but the calculation amount of method proposed by the present invention is far below existing method, and calculating speed is faster.Illustrate with it is existing Method is compared, and method proposed by the present invention can be effectively saved radar computing resource on the basis of guaranteeing target tracking accuracy Consumption.
The above description is merely a specific embodiment, any feature disclosed in this specification, except non-specifically Narration, can be replaced by other alternative features that are equivalent or have similar purpose；Disclosed all features or all sides Method or in the process the step of, other than mutually exclusive feature and/or step, can be combined in any way.

Claims (1)

1. a kind of rapid phase-control battle array radar Time-energy resource joint management method, comprising the following steps:
Establish the offline library of motor-driven parameter:
Step 1: N number of Singer model interacts in setting IMM filter, the motor-driven parameter of each model are as follows:
Discretization is carried out to model probability valued space [0,1], it is assumed that the feasible total M of probabilistic combination is a after discretization, then probability Composite set U are as follows:
U={ U1,U2,…,UM}
Wherein, the either element U of set UjAre as follows:
Uj={ μ1j,μ2j…,μNj},1≤j≤M
For Uj={ μ1j,μ2j…,μNj, μijIndicate probabilistic combination UjIn i-th of element, μijIt needs to meet:
Step 2: in each feasible probabilistic combination Uj, under j=1,2 ..., M, estimate the acceleration auto-correlation function of target
It calculatesMain value interval boundary pointMake:
Wherein, λ be invariant and 0 < λ < 1, it is rightSection carry out discretization, it is discrete after value set expression are as follows:
Step 3: in probabilistic combination UjUnder, calculate vectorValue:
Wherein:
Step 4: probabilistic combination U is calculatedjThe estimated value of lower target maneuver parameter:
Wherein,Indicate vectorFirst element,Indicate vectorSecond element；By probabilistic combination Uj Corresponding motor-driven parameterIt is stored in the offline library of motor-driven parameter；
Step 5: establishing the offline library of target maneuver parameter, wherein including feasible probabilistic combination set U and corresponding target machine Dynamic parameter sets C；Target maneuver parameter sets C is expressed as follows:
C={ C1,C2,…,CM}
Wherein,
Real-time time-energy resource corporate management:
Step 1: next sampling instant is predicted:
Wherein,For the prediction to k-th of sampling instant, tk-1For -1 sampling instant of kth, T (tk-1) between kth -1 sampling Every；
Step 2: using the output of IMM filter as a result, calculatingThe Prediction distance value at momentAnd it is azimuthal pre- Survey error variance
Step 3: assuming that having W kind waveform, respectively w in phased-array radar waveform libraryl, l=1,2 ..., W, in waveform wlUnder, meter Calculate its corresponding prediction signal-to-noise ratio
Step 4: in waveform wlUnder, the measuring standard difference of the target radial of prediction distance, azimuth and pitch angle is respectivelyAndError in measurement is converted to rectangular coordinate system, then waveform wlCorresponding measurement association side Poor matrixAre as follows:
Wherein, J is the Jacobian transition matrix from spherical coordinates to rectangular coordinate system；Then waveform wlThe measurement position of corresponding target Set error varianceAre as follows:
Step 5: desired target predicted position error variance is determinedCalculate waveform wlCorresponding tracking accuracy
Step 6: IMM filter prediction is utilizedThe model probability at momentIt is searched in the offline library of target maneuver parameter WithThe highest model probability combination of its matching degreeAsk:
Wherein, f () indicates model probability correspondence function；
It is corresponding search withCorresponding target maneuver parameter
Step 7: waveform w is calculated using equationlCorresponding sampling period Tl(tk):
Step 8: the radar resource for calculating every kind of waveform consumes cost function:
Wherein, El, (l=1,2 ..., W) is phased-array radar work consumed energy, c on each waveform1With c2For energy Measure the weighting coefficient of resource and time resource, 0≤c1≤ 1,0≤c2≤ 1 and c1+c2=1；Then optimum waveform subscript are as follows:
The corresponding sampling period are as follows:
Step 9: next sampling instant is determined are as follows:
tk=tk-1+T(tk)
Then tkMoment uses waveformTarget is tracked.
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