CN110501675A - One kind being based on MIMO radar low sidelobe transmitting pattern design method - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/023—Interference mitigation, e.g. reducing or avoiding non-intentional interference with other HF-transmitters, base station transmitters for mobile communication or other radar systems, e.g. using electro-magnetic interference [EMI] reduction techniques
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/414—Discriminating targets with respect to background clutter
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/418—Theoretical aspects
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/42—Diversity systems specially adapted for radar
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- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
One kind being based on MIMO radar low sidelobe direction of the launch drawing method, belongs to array signal processing technology.It is to realize to lower secondary lobe and improve main lobe signal power by addition main lobe and side-lobes power constraint, and then improve target detection capabilities, but may cause main lobe division that tradition, which minimizes secondary lobe algorithm, cannot be to constraint of the main lobe and figuration.For this problem, this paper presents be based on the improved minimum secondary lobe method of MIMO radar, this method remains the advantages of minimizing secondary lobe algorithm, it is constrained using the main lobe upper bound and lower bound pair main lobe, space is divided into main lobe area and secondary lobe area using half power points, respectively using the difference maximum value of main lobe and peak side-lobe as objective function, it is set to constraint condition with each array element transmitting signal constant, by the limitation for changing bound pair main lobe fluctuation on main lobe, establish semi definite programming problem, optimal solution is acquired using convex optimization, this method only utilizes primary convex Optimization Solution, the controllable waveform of low sidelobe can be realized.
Description
Technical field
It is mainly a kind of to utilize bound pair main lobe under the main lobe upper bound and main lobe the invention belongs to array signal processing technology
It is constrained, the difference of increase main lobe center power and peak side-lobe power is realized by increasing dividing value on main lobe, thus establishes half
Positive definite planning problem solves the method for realizing low sidelobe characteristic.
Background technique
Multiple-input and multiple-output (MIMO) radar is the new radar mode that scholar proposes on the basis of phased array radar, from proposition
When compared to phased array radar have a clear superiority and become hot topic, Centralized Mode and distribution can be divided into according to array element structure difference
Formula mode.This paper main research is centralized MIMO radar, and centralized MIMO radar utilizes small array element spacing, improves hair
The flexibility of Design of Signal is penetrated, plays the performance advantage of MIMO radar waveform diversity to the greatest extent.In comparison, centralized
MIMO radar has higher estimated accuracy when carrying out DOA estimation, so being always for the design of centralized MIMO radar waveform
Hot spot in research.
There are orthogonal waveforms design and waveform correlation to design both of which in the Waveform Design of centralized MIMO radar: utilizing
Orthogonal waveforms carry out the irradiation in full airspace, find target present in airspace, this mode is mainly used in the target search stage；And
It after target occurs, is tracked using relevant waveform, improves detection performance, this mode is mainly used in the target tracking stage；It is right
The problem of influencing MIMO radar tracking performance with sidelobe clutter is interfered in the secondary lobe that secondary lobe generates, using in direction of the launch G- Design
Middle reduction side-lobe energy improves main lobe energy, improves influence of the secondary lobe to MIMO radar Mutual coupling.Weighting is proposed earliest
Method, directional diagram is weighted by introducing weighting matrix, but to will cause MIMO radar power distribution uneven for weighting, influences
Radar range；It then proposes the part waveform correlation design method based on semi definite programming, but only considers low sidelobe characteristic,
The problems such as ignoring and main lobe regulated and controled, main lobe distortion can be generated.Therefore, set forth herein one kind to be based on MIMO radar low sidelobe launch party
To drawing method, this method is objective function using the maximum value of the difference of main lobe center power and side lobe peak power, passes through main lobe
Bound pair main lobe addition constraint makes main lobe power to the greatest extent may be used while dividing value on increasing main lobe and reduction secondary lobe under the upper bound and main lobe
It can increase the difference of main lobe center power and peak side-lobe power close to the main lobe upper bound, be achieved in the characteristic of low sidelobe.Consider real
In a direction, there may be the influences of strong clutter in border, can form null in specific direction and be inhibited.Context of methods purport
By reducing transmitting pattern secondary lobe, mitigates sidelobe clutter and secondary lobe interferes the influence generated to MIMO radar, improve DOA and estimate
Precision is counted, system block diagram is as shown in Figure 1.
Summary of the invention
The technical problem to be solved by the present invention is to using semi definite programming class algorithm in the same of design low sidelobe directional diagram
When, it is constrained by being added to main lobe, realization can reduce secondary lobe while improving main lobe center power, and to strong miscellaneous in space
The influence of wave forms null and is inhibited, and thus improves accuracy and stability when MIMO radar carries out DOA estimation.
To solve the above problems, the present invention adopts the following technical scheme that:
One kind minimizing secondary lobe design method based on MIMO radar transmitting pattern, and the particular content of this method is as follows:
The first step emits signal by MIMO radar and is emulated, obtain target received signal, obtains signal power and hair
Penetrate the direct relationship of signal covariance matrix.
Second step establishes semi definite programming problem, first according to the relationship of transmitting signal covariance matrix and signal power
Using the difference of the maximum power of transmitting signal main lobe power and peak side-lobe as objective function, by the maximization for seeking its difference
Guarantee low sidelobe, then constrained using bound pair main lobe under the main lobe upper bound and main lobe, increases the value in the main lobe upper bound, it is low in guarantee
While secondary lobe, makes main lobe power as close as the main lobe upper bound, increases the difference of main lobe center power and peak side-lobe power,
Low sidelobe characteristic is realized with this.Then left and right half-power beam point is utilized respectively as main lobe and secondary lobe watershed area, to guarantee
Radar range applies equated constraint to covariance matrix diagonal entry, and finally to specific direction, there are the influences of strong clutter
Null is generated to be inhibited.
Third step establishes semi definite programming problem using the above objective function and constraint condition, using convex in Matlab
Optimization Toolbox is solved, and transmitting signal covariance matrix is obtained, and thus acquires transmitting signal power directional diagram.
4th step evaluates the validity of proposed low sidelobe method, is verified by the peak side-lobe comparison of transmitting pattern
Low sidelobe characteristic verifies DOA by comparison spectral peak figure and estimates performance, passes through mean square error and Riming time of algorithm verification algorithm
It can superiority and inferiority.Mean square error formula:
In formula, i indicate i-th Simulation results, α andIndicate corresponding pitch angle and azimuth input value, αiWithPoint
Not Biao Shi i-th simulation result output pitch angle and azimuth estimated value.
Feature of the invention is as follows:
(1) it is objective function using the maximum value of the difference of main lobe center power and peak side-lobe power, obtains preferable low
Sidelobe performance.
(2) apply constraint using bound pair main lobe under the main lobe upper bound and main lobe, while increasing the main lobe upper bound, so that main lobe
Center power increases the difference of main lobe center power and peak side-lobe power as close as the main lobe upper bound, is realized with this
Low sidelobe characteristic.
(3) for the interference that may somewhere have strong clutter in space, null can be formed and inhibited, improved anti-dry
Immunity energy.
Compared with prior art, the invention has the following advantages:
The invention proposes one kind to be based on MIMO radar low sidelobe direction of the launch drawing method, and with experiment simulation to algorithm
Performance when sidelobe performance and progress DOA estimation is verified, and this method has more excellent peak compared with existing low sidelobe algorithm
It is worth secondary lobe, and computational complexity and root-mean-square error are also smaller, root-mean-square error is to the especially big or special small error in one group of measurement
Reflect it is very sensitive, so, root-mean-square error can be well reflected out the precision measured, i.e., expression DOA estimation effect more
It is good.To improve MIMO radar low sidelobe characteristic and DOA estimation effect.
Detailed description of the invention
Fig. 1 MIMO radar low sidelobe transmitting pattern method system block diagram
Fig. 2 emission array model
Fig. 3 method and thought schematic diagram
Tetra- kinds of algorithm transmitting patterns of Fig. 4 compare, a) represent be four kinds of algorithm Direction Pattern Simulations as a result, b) represent its
A) enlarged drawing in figure at the first peak side-lobe of the left side
When tetra- kinds of algorithm signal-to-noise ratio of Fig. 5 are 10dB, DOA estimation simulation result spectral peak comparison is carried out.Wherein scheme left side table in a
Show Chebyshev's weighting algorithm spectrum peak search as a result, the right corresponds to contour map for it；Scheming the left side in b indicates association-integral secondary lobe
Algorithm spectrum peak search is as a result, the right corresponds to contour map for it；Scheming the left side in c indicates association-peak side-lobe algorithm spectrum peak search knot
Fruit, the right correspond to contour map for it；Scheming the left side in d indicates mentioned method spectrum peak search herein as a result, the right corresponds to for it etc.
High line chart
Tetra- kinds of algorithms of Fig. 6 carry out mean square error root when DOA estimation and compare
Specific embodiment
Yi Xiajiehejutishishili,Bing Canzhaofutu,Dui Benfamingjinyibuxiangxishuoming.
Step 1 emulates MIMO radar transmitting signal, and radar mockup is set as what transmitting terminal was made of M array element
Even linear array.Wherein a antenna element transmitting signal of m (1≤m≤M) is represented by sm, then transmitting signal matrix can be indicated
Are as follows:
S=[s1,s2,…,sM]T
Assuming that far field objects be located at intended recipient at θ to signal X be
X=aH(θ)S
Wherein a (θ) indicates that MIMO radar emits signal guide vector, aH(θ) is the transposition of steering vector a (θ),Carrier frequency is fc, c represents the light velocity, and array element spacing is d.By
This can obtain directional diagram expression formula P (θ) are as follows:
Wherein, L indicates that Baud Length, covariance matrix R are P (θ), and covariance matrix R will be straight as can be seen from the above equation
Connecing influences transmitting pattern spatial characteristics, and P (θ) directly indicates distribution of the power at corresponding angle θ, obtains transmitting signal
Model.
Step 2 utilizes ΩSIndicate secondary lobe region, ΩmMain lobe region is indicated, with θ1, θ2Respectively indicate the left half-power of main lobe
Beam spot and right half-power beam point, centre folded by region be main lobe Ωm, the region other than left and right half-power beam point is ΩS。
Transmitting pattern θ can be obtaineds(θs∈Ωs) direction side-lobes power expression formula are as follows:
aH(θs)Ra(θs),θs∈Ωs
Transmitting pattern θm(θm∈Ωm) direction main lobe power expression:
aH(θm)Ra(θm),θm∈Ωm
Step 3 lacks due to minimizing secondary lobe algorithm and integral secondary lobe algorithm to constraint of the main lobe, is easy to produce main lobe distortion
The problems such as, it is constrained using bound pair main lobe under the main lobe upper bound and main lobe, increases the value in the main lobe upper bound, guaranteeing the same of low sidelobe
When, make main lobe power as close as the main lobe upper bound, increase the difference of main lobe center power and peak side-lobe power, is come with this real
Existing low sidelobe characteristic.Set the main lobe upper bound as β and main lobe lower bound be η, η be set as herein 1, β indicate constraint of the main lobe be it is variable,
Its constraint is as follows:
η indicates main lobe lower bound, then:
aH(θm)Ra(θm)≥η,θm∈Ωm
β is the constraint of the main lobe upper bound, is made:
|aH(θm)Ra(θm)-η|≤β,θm∈Ωm
Step 4, θm0(θm0∈Ωm) indicate main lobe center power point, the constraint of three dB bandwidth half power points are as follows:
In step 5, actual environment, might have clutter in secondary lobe area, (radar scattering of other objects other than target is returned
Wave) it influences to receive signal, cause the echo signal-to-noise ratio of MIMO radar to reduce, and then influence MIMO radar and estimate performance；It therefore can
In secondary lobe region clutter θl(θl∈Ωs) at generate corresponding null to inhibit to strong clutter.If (δ is any for δ > ε > 0
Number greater than 0), if there is the influence of clutter, can be added in constraint condition:
aH(θl)Ra(θl)≤ε,θl∈Ωs
Step 6 is to guarantee that each array element transmission power is equal, does not influence MIMO radar detection performance, enables RmmIndicate the of R
M (0 < m < M) a diagonal entry adds following constraint:
Rmm=1/M
Step 7, using the difference of main lobe center power and peak side-lobe power as objective function, guarantee its low sidelobe characteristic,
θm0(θm0∈Ωm) indicating main lobe central point, objective function form is as follows:
aH(θm0)Ra(θm0)-aH(θs)Ra(θs)
Step 8 enables covariance matrix R >=0 guarantee its Positive, utilizes the constraint function and step 3, step in step 7
Rapid 4, step 5, the constraint condition of step 6 establish positive semidefinite Optimized model as follows:
Step 9 obtains Optimized model by step 8, is solved, is obtained using Matlab convexity Optimization Toolbox cvx
Emit signal covariance matrix, and then acquires transmitting signal.
Step 10, evaluation and test low sidelobe characteristic, it is non-with Chebyshev's weighting algorithm, based on covariance using the mentioned method of this paper
The modified minimum peak side-lobe algorithm of diagonal entry and minimum integral secondary lobe algorithm carry out emulation and obtain its direction of the launch
Figure simulation result is as shown in a in Fig. 4, and wherein b is enlarged drawing at the first peak side-lobe in Fig. 4.Four kinds of algorithm peak side-lobe numerical value
Such as table 1.(for convenience of describing, covariance amendment off diagonal element method is referred to as " association-")
1 four kinds of algorithm peak side-lobe data of table compare
| Algorithm title | Chebyshev's weighting | Association-integral secondary lobe | Association-peak side-lobe | Context of methods |
| Peak side-lobe | -16.03 | -16.69 | -17.46 | -17.35 |
Step 11 is verifying four kinds of algorithms simulation performance when carrying out DOA estimation, utilizes hair obtained by the design of four kinds of algorithms
Signal is penetrated, then carries out DOA estimation using 2D-MUSIC algorithm, gained spectral peak is as shown in Figure 5.
Step 12, the performance superiority and inferiority of tradeoff four kinds of algorithm DOA estimation
In the case where two conditions of emulation are constant above, signal-to-noise ratio is changed to SNR ∈ [- 10,10], the value of each signal-to-noise ratio
Add 2.The joint root-mean-square error (RMSE) of angle estimation is to weigh the important criteria of the performance superiority and inferiority of DOA estimation, combines RMSE
Is defined as:
Wherein, i indicate i-th Simulation results, α andIndicate corresponding pitch angle and azimuth input value, αiWithPoint
Not Biao Shi i-th simulation result output pitch angle and azimuth estimated value.Carry out 100 independences respectively under each signal-to-noise ratio
It is as shown in Figure 6 with SNR transformation results to acquire root-mean-square error RMSE for emulation experiment.When record signal-to-noise ratio is 0dB, 10dB respectively
It carries out 100 independent repetitions to test, record DOA estimation mean square error and average operating time such as table 2.(computer processor:
Intel(R)Core(TM)2Quad CPU Q9550@2.83GHz；Memory: 4.5GB is installed；OS Type: 32)
The comparison of the mean square error and runing time of 2 algorithms of different of table
Present invention is characterized in that
1, it establishes based on MIMO radar low sidelobe transmitting pattern method model, with bound pair master under the main lobe upper bound and main lobe
Valve is constrained, and while realizing to main lobe regulation, guarantees its low sidelobe characteristic.
2, it is objective function using the maximum value of the difference of main lobe power and peak side-lobe power, semi definite programming is established with this
Problem realizes low sidelobe characteristic.
3, the problem of influencing for a direction in space there may be clutter, can form null in specific direction and be pressed down
System.
Above embodiments are only exemplary embodiment of the present invention, are not used in the limitation present invention, protection scope of the present invention
It is defined by the claims.Those skilled in the art can within the spirit and scope of the present invention make respectively the present invention
Kind modification or equivalent replacement, this modification or equivalent replacement also should be regarded as being within the scope of the present invention.
Claims (1)
1. one kind is based on MIMO radar low sidelobe transmitting pattern design method, which is characterized in that specific as follows:
Step 1: emulated to MIMO radar transmitting signal, by radar mockup be set as transmitting terminal be made of M array element it is uniform
Linear array；Wherein m-th of antenna element transmitting signal is expressed as sm, then transmitting signal matrix can be indicated are as follows: 1≤m≤M；
S=[s1,s2,…,sM]T
Assuming that far field objects be located at intended recipient at θ to signal X be
X=aH(θ)S
Wherein a (θ) indicates that MIMO radar emits signal guide vector, aH(θ) is the transposition of steering vector a (θ),Carrier frequency is fc, c represents the light velocity, and array element spacing is d；
It can thus be concluded that directional diagram expression formula P (θ) are as follows:
Wherein, L indicates that Baud Length, covariance matrix R are P (θ), and covariance matrix R will direct shadow as can be seen from the above equation
Transmitting pattern spatial characteristics are rung, P (θ) directly indicates distribution of the power at corresponding angle θ, obtains transmitting signal mode
Type；
Step 2: utilizing ΩSIndicate secondary lobe region, ΩmMain lobe region is indicated, with θ1, θ2Respectively indicate the left half-power beam of main lobe
Point and right half-power beam point, centre folded by region be main lobe Ωm, the region other than left and right half-power beam point is ΩS；It can obtain
Transmitting pattern θsThe side-lobes power expression formula in direction are as follows:
aH(θs)Ra(θs),θs∈Ωs
Transmitting pattern θm(θm∈Ωm) direction main lobe power expression:
aH(θm)Ra(θm),θm∈Ωm
Step 3: set the main lobe upper bound as β and main lobe lower bound be η, η be set as 1, β indicate constraint of the main lobe be it is variable, constraint is such as
Under:
η indicates main lobe lower bound, then:
aH(θm)Ra(θm)≥η,θm∈Ωm
β is the constraint of the main lobe upper bound, is made:
|aH(θm)Ra(θm)-η|≤β,θm∈Ωm
Step 4: θm0(θm0∈Ωm) indicate main lobe center power point, the constraint of three dB bandwidth half power points are as follows:
Step 5: might have the radar scattering echo of other objects other than clutter i.e. target in secondary lobe area in actual environment
It influences to receive signal, the echo signal-to-noise ratio of MIMO radar is caused to reduce, and then influence MIMO radar and estimate performance；In secondary lobe region
Clutter θlPlace generates corresponding null to inhibit to strong clutter；If δ > ε > 0, δ are any number greater than 0；If there is miscellaneous
The influence of wave, is added in constraint condition: being just added without the following conditions if there is no clutter；
aH(θl)Ra(θl)≤ε,θl∈Ωs
Step 6: not influencing MIMO radar detection performance to guarantee that each array element transmission power is equal, enabling RmmIndicate m-th pair
Diagonal element, wherein 0 < m < M；The following constraint of addition:
Rmm=1/M
Step 7: guaranteeing its low sidelobe characteristic, θ using the difference of main lobe center power and peak side-lobe power as objective functionm0
(θm0∈Ωm) indicating main lobe central point, objective function form is as follows:
aH(θm0)Ra(θm0)-aH(θs)Ra(θs)
Step 8: covariance matrix R >=0 is enabled to guarantee its Positive, using in the 7th step constraint function and step third extremely
The constraint condition of 7th step establishes positive semidefinite Optimized model as follows:
Step 9: obtaining Optimized model by the 8th step, is solved, sent out using Matlab convexity Optimization Toolbox cvx
Signal covariance matrix is penetrated, and then acquires transmitting signal.
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