CN109633695A - A kind of unmanned plane is to defending the active positioning method for leading jammer - Google Patents
A kind of unmanned plane is to defending the active positioning method for leading jammer Download PDFInfo
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- CN109633695A CN109633695A CN201910024118.0A CN201910024118A CN109633695A CN 109633695 A CN109633695 A CN 109633695A CN 201910024118 A CN201910024118 A CN 201910024118A CN 109633695 A CN109633695 A CN 109633695A
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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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/21—Interference related issues ; Issues related to cross-correlation, spoofing or other methods of denial of service
Abstract
The invention discloses a kind of unmanned planes to defending the active positioning method for leading jammer.The present invention uses DRSS algorithm that can carry out more accurately ranging to target in the case where jammer signal strength is unknown first.Technology cannot to the problem that unknown disturbances machine positions and cost is excessively high before effective solution.In view of noise, the position only fixed with DRSS has sizable error with true position, so the present invention reuses Extended Kalman filter instead of least square method to position, and tracking and monitoring to jammer are realized using Liapunov vector field, more accurate positioning can be carried out to GNSS jammer.Meanwhile it can be very good to solve the problems, such as that positioning system is difficult the good sighting distance relationship of same jammer holding in urban environment using unmanned aerial vehicle platform.
Description
Technical field
The invention mainly relates to a kind of unmanned planes to the active positioning method for leading jammer is defended, and is based on DRSS principle and expansion
The unmanned plane of Kalman filtering is opened up to GNSS jammer positive location.
Background technique
No matter GNSS navigation system all plays huge effect in military and civil field.GNSS system can mention
For accurate global location, the functions such as time calibration, our daily life of great convenience.Especially on navigational guidance,
All kinds of unmanned vehicles include unmanned plane, unmanned boat, and unmanned vehicle etc. all has very big dependence to GNSS system.However, GNSS
One of major defect of system be exactly satellite orbit send back to the signal of unmanned aerial vehicle onboard receiver as intensity is weaker and caused by
Vulnerability to jamming only has -130dbM from satellite orbit back to the signal of receiver by taking GPS signal as an example, it is only necessary to which one simple
Omnidirectional antenna it is constantly fixed high-power constantly at this with one in the frequency range (about 1.57GHz) of GPS receiver work
Frequency range transmitting signal can be achieved with the pressing type interference to GPS signal.Currently, the GPS interference unit of similar principles is also got on the market
More spreading unchecked, GPS interference unit is easily obtained in the market and cheap, these jammers, to the place for relying on GNSS system,
Such as police unmanned plane is equipped in such as airport, law enforcement, the system such as power grid etc. of dependence GNSS system time service be likely to cause compared with
Big influence.
Dependence in view of sorts of systems to GNSS system, GNSS user is anti-interference to GNSS signal to propose certain need
It asks, a kind of research direction is to improve receiver robustness, its anti-interference is improved, and law enforcement agency etc. is in the reception for wishing oneself
Machine can it is jamproof simultaneously, can also there is corresponding method to go positive location to find out jammer.By taking GPS as an example, GPS signal is dry
The positive location technology for disturbing machine is asked with reference to the target following of the node locating technique of wireless sensor network and radar mostly
Topic, in the ideal case (such as Plain, in the case that clear is blocked), most of this kind of location algorithm can be to mesh
Mark carries out more accurate positioning, but under complicated urban environment, blocks, and keep non line of sight to close with object is detected
When being (non line of sight, NLOS), the performance of these location algorithms will receive considerable influence.And in city ring
To keep good sighting distance relationship (Line of Sight, LOS) in border, a solution is to be deployed in sensor as far as possible
Eminence.The development for having benefited from low-cost unmanned machine technology, sensor deployment is had become one in the air in height can be simple
The scheme of realization, in view of this, the present invention considers to carry corresponding sensor using unmanned aerial vehicle platform to jammer or similar
High power transmitter is positioned, can be effectively reduced sensor ground as block and caused by error, and to biography
Sensor provides more convenient flexible displacement.
Currently, mainly for GNSS interference unit target localization method there are two types of, one is by measurement difference orientation
Angle come the method that determines position, i.e., a kind of algorithm for being based on direction of arrival degree (Angle of Arrival, AOA), application
Observer is measured the positive location to realize radar to target to direction of arrival degree in different location, and passes through minimum two
Multiplication seeks source location or application extension Kalman filtering to seek the position of signal source.Second method is basis
The distance that difference measures is come the algorithm that is positioned, i.e., algorithm (TOA) based on arrival time, and is based on reaching time-difference
Algorithm (TDOA), and be based on signal strength (RSSI) location algorithm.But positive location is carried out using unmanned plane and is then needed
Consider the applicability of algorithm and the installation of corresponding hardware device and cost problem.Location algorithm based on AOA, which has, to be calculated
The characteristics of method is simply easily realized, does not need the cooperation of measured target, can accurately be positioned to measured target, still yet
AOA algorithm needs expensive an aerial array and clouds terrace system to realize positioning, and for unmanned plane, unmanned plane needs
Biggish load capacity carrys out the weight of loaded antenna array and clouds terrace system, on the other hand, considers cost problem, aerial array
And the cost of clouds terrace system is excessively high, also and is not suitable for inexpensive unmanned plane scheme.
Location algorithm accurate positioning based on TOA and TDOA, hardware cost is moderate, but TOA needs to be tracked
The cooperation of target is positioned (time synchronization, and sending information includes temporal information etc.), to unknown object or hostility
Target is obvious and is not suitable for, and TDOA algorithm registration, but multiple unmanned planes is needed to monitor simultaneously an identical signal
It to determine distance, is more focused on positive location is carried out to GNSS jammer using single rack unmanned plane herein, in addition, even if using more
Machine carries out while listening for when the signal source as jammer carries out compacting interference to channel, the signal of sending is big absolutely jammer
Majority is meaningless signal, more increases the measurement difficulty between unmanned plane for information reaching time-difference, it can be seen that, TDOA
It is not particularly suited for positioning unknown disturbances machine.
The method that another kind is used to determine position in wireless sensor network is based on signal strength (Received
Signal Strength Indication, RSSI) algorithm, cost is relatively low for the algorithm, and positioning accuracy is higher.The algorithm passes through
The signal measured and its original intensity, calculate the attenuation of signal, thus come calculate this away from target point away from
From.It, can not in the case where not knowing about jammer original power but since the GNSS jammer power of commercial type is different
It obtains the green strength of signal, is also impossible to obtain its attenuation, also just do not know where to begin then calculating distance.
Summary of the invention
It is an object of the present invention to against the above deficiency, provide one kind to filter based on DRSS principle and spreading kalman
The unmanned plane of wave device cannot be to unknown it is expected that solution is at high cost in the prior art to the method for GNSS jammer positive location
Jammer is positioned, and due to that can not obtain the green strength of signal, cannot calculate jammer range, and error is biggish asks
Topic.
In order to solve the above technical problems, the invention adopts the following technical scheme:
A kind of unmanned plane based on DRSS principle and Extended Kalman filter provided by the present invention is to GNSS jammer
Active positioning method can carry out target in the case where jammer signal strength is unknown more accurate using DRSS principle
Ground ranging is very suitable to apply the positioning in unmanned aerial vehicle platform to GNSS system jammer.In view of noise, only made with DRSS
The position come has sizable error with true position, so recycling Kalman filtering to carry out position location estimation, together
When navigated in unmanned plane tracking phase using Liapunov vector field and more accurately position to realize.
Preferably, the principle of DRSS algorithm is to do the difference that difference is calculated by the RSSI intensity measured in difference
It is one after partite transport calculation to be eliminated with jamming power and the not related amount of antenna gain, the uncertainty of jammer,
So with the value active tracing can be carried out to no cooperation, unknown or hostility jammer as the foundation of positioning distance measuring
Positioning.
Determine three or three or more DRSS circles, the intersection point of these circles two-by-two by the unmanned plane position of different location
It is the position of jammer.
Preferably, further technical solution is: in view of making an uproar in order to keep the positioning to jammer more accurate
Sound has sizable error with true position come the position made by three geometry circles, in the present invention based on extension
It is estimated the estimating algorithm position of Kalman filtering.
Preferably, further technical solution is: the present invention positions jammer using unmanned plane.To carrying
Mainly in two stages, one is the search phase, and one is tracking phase for the navigation of the unmanned plane of hardware device.Consider general
Property, the present invention in navigational portions use the kinetic model of simple fixed-wing unmanned plane as refer to.
Preferably, further technical solution is: in the search phase, to avoid interference from removing uncertainty, and
More possible positions are provided to carry out more accurately DRSS positioning, unmanned plane is made to keep serpentine path operation, it can be to course angle
Simply controlled.
Preferably, further technical solution is: in tracking phase, it is contemplated that jammer is unknown object or enemy
The lift efficiency of target of anticipating and fixed-wing unmanned plane is needed using target point as the center of circle, and certain distance is the circular rail of radius
It spirals on road, can be navigated as a result, in tracking phase with Liapunov vector field.And it navigates by Liapunov vector field
During, it is still required to be corrected predicted position by continuous measurement and positioning and is more accurately positioned with realizing.
Compared with prior art, the beneficial effects of the present invention are: using DRSS algorithm can jammer signal strength not
More accurately ranging is carried out to target in the case where knowing.Technology cannot determine unknown disturbances machine before effective solution
Position and the excessively high problem of cost.It replaces least square method to be positioned using Extended Kalman filter, and uses Li Yapu
Promise husband vector field realizes the tracking and monitoring to jammer, and more accurate positioning can be carried out to GNSS jammer.Together
When, it can be very good to solve positioning system in urban environment using unmanned aerial vehicle platform and be difficult the good view relationship of same jammer holding
The problem of.
Detailed description of the invention
Fig. 1 is the system structure schematic block diagram for illustrating one embodiment of the invention.
Fig. 2 is the jammer that (corresponding position) calculates at every point of time of the expanded Kalman filtration algorithm based on DRSS
With the comparison of true jammer position.
Fig. 3 is the jammer that (corresponding position) calculates at every point of time of the expanded Kalman filtration algorithm based on DRSS
Position is at a distance from true jammer.
Fig. 4 is the position of unmanned plane during flying track in two-dimensional surface, jammer position and Extended Kalman filter measuring and calculating
It sets.
Fig. 5 is the range error information of random 1,000 times experiments.
Specific embodiment
The present invention is further elaborated with reference to the accompanying drawing.
The present invention is that a kind of unmanned plane based on DRSS principle and Extended Kalman filter is actively fixed to GNSS jammer
Position algorithm, specific implementation step are as follows:
Step 1: writing out the signal strength received i-th of position by fries transmission formula.
In formula, PrFor the signal strength received, PtTo emit signal strength, GtFor the antenna gain of jammer, and GrThen
For the signal gain of unmanned aerial vehicle onboard receiver, in addition, λ is then the wavelength of signal, the positioning target of this paper is GNSS interference unit,
By taking GPS interference unit is target as an example, many GPS interference units all work at L1 frequency range (1.57GHz), and signal wave at this time is about
It is then unmanned plane with the distance between jammer for 20cm or so, d.
Ignore multipath effect, consider the unmanned plane in multiple directions, then above formula can be rewritten are as follows:
Wherein, PriIndicate the signal strength received i-th of position, PtFor jammer signal strength, diFor at i-th
Distance of the unmanned plane with jammer, X when positionaiIndicate the noise jamming item when position.N is signal strength with range attenuation
Coefficient.Since unmanned plane from the ground, can make receiver band receiver and jammer to keep a good view in the sky
Away from relationship, the communication environment between receiver and GNSS jammer can be simplified to the LOS channel circumstance of not barrier, this
When, signal power is with a square decaying, it can be deduced that n=2.
The same jammer then has when to j-th of position of unmanned plane on another position:
Wherein, PrjFor the signal strength received j-th of position, djUnmanned plane is the same as jammer when for j-th of position
Distance, since jammer is fixed, unmanned plane is constant, so Pt,Gt,GrIt is constant.
Step 2: doing Difference Calculation to the signal strength measured on two positions:
Diffij=Pri-Prj=10n log (dj)-10n log(di)+ΔXaij
As it can be seen that by formula it is found that Diff after calculus of differencesijEliminate the P of jammert,GtInfluence, be one with interference
The not related amount of power and antenna gain of machine, the uncertainty of jammer are eliminated, so with the value as positioning
The foundation of ranging can carry out active tracing positioning to no cooperation, unknown or hostility jammer.
Step 3: determining m positioning node DRSS value are as follows:
Step 4: determining the coordinate of DRSS setting circle.
For DRSS algorithm with RSSI algorithm, TOA and TDOA algorithm is different, and the ranging localization algorithm of this type is derived
Geometry used for positioning circle the center of circle always with unmanned plane in same position, and the coordinate of DRSS setting circle is in cdk=(xdk,
ydk), it is determined by a pair of of measurement point, in which:
Wherein, DijFor distance of i-th of position with j-th of position of unmanned plane measurement, and αijIt is then one with DRSS amount
Relevant coefficient, by signal strength expression:
Step 4: determining the position of jammer.
It can determine three or three or more two-by-two by the unmanned plane position of different location by three above formula
DRSS circle, these circle intersection points be jammer position.
By predetermined n=2, then have:
It may be noted that when the signal power measured at two o'clock is identical, the set circle of DRSS expression will be will become as α=1
A piece straight line that is vertical with j-th of position line perpendicular to i-th of position and crossing jammer point, the linear equation are as follows:
Work as yj-yiWhen ≠ 0
Work as yj-yiWhen=0
It may be noted that when two o'clock position is very close or is overlapped, i.e., in formula (3.13), xi=xj, then taking at this time
Point is not act on and meaning in itself in positioning application.
Step 5: being estimated using Kalman filtering position.
In view of noise, sizable error is had with true position come the position made by three geometry circles,
It attempts to estimate position using Kalman filtering in the present invention.
For fixed jammer, three-dimensional coordinate vector are as follows:
The signal power received when unmanned plane mounted antennas is to measure unmanned plane in j point, it is contemplated that DRSS value has
Measurement result does difference processing twice, in order to make the relevant factor alpha of same difference componentijIt is obvious enough in the variable quantity at each moment,
A point in need when doing Difference Calculation two measurement points is fixed as to the starting point of unmanned plane started when measuring, note
Are as follows:
Keep unmanned plane height constant, then unmanned plane is in the position where moment k:
To calculate the relevant factor alpha of same difference component1k。
Then observational equation can indicate are as follows:
Wherein, wkIt indicates that the measurement of k moment unmanned plane position obtains the error of received signal power, meets Gauss point
Cloth.h(xk) it is that nonlinear observation function is obtained by DRSS principle:
For fixed target jamming machine, at the k moment, predicted position are as follows:
WhereinIt is the predicted position made at the k-1 moment by observation at that time.Simultaneously as dry
The machine of disturbing is fixed, so state-transition matrix F is equal to unit matrix.
Status predication equation:
X (k | k)=X (k | k-1)+K [z1k-h(xk|k-1)] (4.7)
Wherein, X (k) is the system mode at k moment, and X (k | k-1) it is to be predicted using laststate as a result, K is gain square
Battle array, z1kTo measure the DRSS value in the case of signal power, but measurement equation therein is nonlinear, so the problem is not
It can be solved by linear Kalman filter, consider Extended Kalman filter.
Step 6: by finding out h (xk) partial derivative, using Taylor expansion to carry out first-order linear truncation, be
This, finds out the Jacobian matrix of h (x):
So, go down in order to which extended Kalman filter to be enabled constantly is run until systematic procedure terminates, we also want
The covariance of status predication equation under k-state is updated: covariance can update at this time are as follows:
P (k | k)=[I-KH (X (k | k-1))] P (k | k-1) (4.9)
At this point it is possible to find out gain matrix K:
K=P (k | k-1) HT(X(k|k-1))S-1 (4.10)
Wherein S are as follows:
S=H (X (k | k-1)) P (k | k-1) HT(X(k|k-1))+R(k) (4.11)
Wherein R (k) is the measurement error at k moment, meets Gaussian Profile.
Covariance P (k+1 | k) is updated again, is had:
P (k+1 | k)=FP (k | k) FT+Q (4.12)
Q is process noise, is taken:
Pay attention to, it is assumed that process noise is not present in positive location problem, then there is q11=0, to guarantee that P is nonsingular, q22
It can be set to the minimum positive number being not zero.
In this way, algorithm can autoregressive operation go down, to find out the status predication equation at k moment, obtain accurately
Position.
Step 7: to avoid interference from removing uncertainty, and providing more possible positions in the search phase to carry out more
Accurately DRSS is positioned, and so that unmanned plane is kept serpentine path operation, this can be to the simple control of course angle.
Step 8: in tracking phase, it is contemplated that jammer is unknown object or hostility target and fixed-wing unmanned plane
Lift efficiency, need using target point as the center of circle, certain distance be radius circuit orbit on spiral, as a result, in tracking rank
Section is navigated using literary Liapunov vector field.
Liapunov vector field:
Wherein, r is distance of the aircraft with target, rdFor desired distance, v0For the speed of unmanned plane,It is the point in x-axis
Desired speed on direction, ydFor the desired speed of point on the y axis.
Applied to fixed-wing unmanned plane, each point of the unmanned plane in vector field is kept by controlling the course angle of unmanned plane
Place can be flown with desired angle and speed, be had:
Differential is done to above formula, obtains desired angular speed:
During being navigated by Liapunov vector field, it is still required to be corrected by continuous measurement and positioning pre-
Location is set to realize and more accurately position.
In conjunction with attached drawing, the advantages of the present invention will be described in detail and beneficial effect
Refering to what is shown in Fig. 1, an embodiment of the invention is a kind of based on DRSS principle and Extended Kalman filter
Unmanned plane to GNSS jammer positive location algorithm, in which: first using DRSS algorithm can jammer signal strength not
More accurately ranging is carried out to target in the case where knowing.But consider noise, the position only fixed with DRSS is the same as true
Position has sizable error, replaces least square method to position so we reuse Extended Kalman filter,
Search phase simply controls course angle, and the tracking to jammer is realized using Liapunov vector field in tracking phase
And monitoring, and error is constantly reduced, more accurate positioning finally is carried out to GNSS jammer.Meanwhile it is flat using unmanned plane
Platform can be very good to solve the problems, such as that positioning system is difficult the good sighting distance relationship of same jammer holding in urban environment.
In order to verify correctness of the invention, following emulation experiment is carried out.
Taking simulating scenes is a 12km X 12km square area.The initial position of unmanned plane is in coordinate section (x ∈
(0,60), (0,60) y ∈) in place is randomly generated.Nobody initial heading facing area angle, i.e.,Section in
It is randomly generated.The speed of unmanned plane is fixed as 30m/s, highly maintains 200m.Unmanned plane starts from starting point to target
It is positioned and is tracked, and constantly correction predicted position gradually decreases error realization precise positioning.
Take work as object, then can see the jammer in emulation in the GPS jammer of L1 frequency range (1.57GHz)
Work is jammer of the work in L1 frequency range.It is insensitive for transformation of the verification algorithm to the transmitting signal power of jammer,
The transmitting signal power of jammer is randomly generated in the range of 1mW~650mW in emulation.The position of jammer is equally in region
Inside it is randomly generated.
For jammer antenna, assume that its jammer antenna meets omni-directional herein, it is identical to having in all directions
Gain.
To the antenna for being used for measured signal power on unmanned plane, the influence in view of noise is needed, wherein be affected
It is thermal noise, simulates thermal noise with Johnson-Nyquist equation in simulations:
Pth=kbT·B
Wherein, PthFor thermal noise, kbFor Boltzmann constant, T is the absolute temperature of sensor, and B is the bandwidth of jammer.
The noise can be handled by low-pass filter.
When DRSS geometry circle intersects, Extended Kalman filter is started to work, always to the initial guess position of jammer
It is fixed as scene center coordinate
Fig. 2 is that the expanded Kalman filtration algorithm based on DRSS calculates at every point of time (corresponding position)
The comparison of jammer position and true jammer position.
Fig. 3 is that the expanded Kalman filtration algorithm based on DRSS is calculated at every point of time (corresponding position)
Jammer position out is at a distance from true jammer.
Fig. 2 and Fig. 3 shows the estimation precision of Extended Kalman filter in emulation experiment as unmanned plane is in more positions
It sets and true jammer nearby coordinates is continuously improved and converged to after measuring to DRSS value.It is finally surveyed in this time emulation
The jammer position arrived is 98 meters with the final range error of true jammer position, the precision under 12 kilometers of scale
It is acceptable.
Fig. 4 is unmanned plane during flying track in two-dimensional surface, and jammer position and EKF calculate position.
Fig. 5 shows that unmanned plane can be positioned and be tracked to fixed jammer using the algorithm.
Can more intuitively it be found out by Fig. 5, in 1000 experiments, the extended Kalman filter algorithm based on DRSS
Relatively good jammer can be positioned.
Claims (6)
1. the active positioning method that a kind of unmanned plane leads jammer to defending, it is characterised in that include the following steps:
1) jammer is positioned using DRSS algorithm；
The foundation of value that difference is calculated as positioning distance measuring is done by the RSSI intensity measured in difference, by differently
The unmanned plane position of point determines three or three or more DRSS circles two-by-two, and the intersection point of these DRSS circle is the position of jammer
It sets；
2) unmanned plane reduces position error using extended Kalman filter.
2. according to the method described in claim 1, it is characterized in that passing through the RSSI measured in difference in the step 1)
Intensity does the step of foundation of value that difference is calculated as positioning distance measuring, specifically:
1.1) signal strength received i-th of position is write out by fries transmission formula；
Fries transmission formula is
In formula, PrFor the signal strength received, PtTo emit signal strength, GtFor the antenna gain of jammer, GrFor unmanned plane
The signal gain of airboarne receiver, in addition, λ is then the wavelength of signal, d is then unmanned plane with the distance between jammer；
Ignore multipath effect, consider that the unmanned plane in multiple directions, above formula are rewritten are as follows:
Wherein, PriIndicate the signal strength received i-th of position, PtFor jammer signal strength, diFor i-th of position
When distance of the unmanned plane with jammer, XaiIndicate the noise jamming item when position；N is that signal strength is with range attenuation
Number；
The same jammer then has when to unmanned plane on the j of another position:
Wherein, PrjFor the signal strength received j-th of position, djWhen for j-th of position unmanned plane with jammer distance,
Since jammer is fixed, unmanned plane is constant, so Pt,Gt,GrIt is constant；
1.2) Difference Calculation is done to the signal strength measured on two positions:
Diffij=Pri-Prj=10nlog (dj)-10nlog(di)+ΔXaij
By formula it is found that Diff after calculus of differencesijEliminate the P of jammert,GtInfluence, be one with jamming power with
And the not related amount of antenna gain, the uncertainty of jammer are eliminated, so with the value as the foundation of positioning distance measuring
Active tracing positioning can be carried out to no cooperation, unknown or hostility jammer.
3. according to the method described in claim 1, it is characterized in that passing through the unmanned seat in the plane of different location in the step 1)
It sets and determines that three or three or more DRSS are round two-by-two, the intersection point of these DRSS circle is the position of jammer；Specifically:
1.3) m positioning node DRSS value is determined are as follows:
Step 4: determining the coordinate of DRSS setting circle；
The coordinate c of DRSS setting circledk=(xdk,ydk), it is determined by a pair of of measurement point, in which:
Radius:
Wherein, DijFor distance of i-th of position with j-th of position of unmanned plane measurement, and αijIt is then one related with DRSS amount
Coefficient, by signal strength expression:
1.4) position of jammer is determined
It can determine three or three or more two-by-two by the unmanned plane position of different location by three above formula
The intersection point of DRSS circle, these circles is the position of jammer.
4. according to the method described in claim 1, it is characterized in that the step 2) specifically:
2.1) for fixed jammer, three-dimensional coordinate vector are as follows:
The signal power received when unmanned plane mounted antennas is to measure unmanned plane in j point, it is contemplated that DRSS value has twice
Measurement result does difference processing, in order to make the relevant factor alpha of same difference componentijIt is obvious enough in the variable quantity at each moment, it will do
The point in two measurement points needed when Difference Calculation is fixed as the starting point of unmanned plane started when measuring, and is denoted as:
Keep unmanned plane height constant, then unmanned plane is in the position where moment k:
To calculate the relevant factor alpha of same difference component1k；
Observational equation can indicate are as follows:
Wherein, wkIt indicates that the measurement of k moment unmanned plane position obtains the error of received signal power, meets Gaussian Profile；h
(xk) it is that nonlinear observation function is obtained by DRSS principle:
For fixed target jamming machine, at the k moment, predicted position are as follows:
WhereinIt is the predicted position made at the k-1 moment by observation at that time；Simultaneously as jammer
It is fixed, so state-transition matrix F is equal to unit matrix；
Status predication equation:
X (k | k)=X (k | k-1)+K [z1k-h(xk|k-1)] (4.7)
Wherein, X (k) is the system mode at k moment, and X (k | k-1) is using laststate prediction as a result, K is gain matrix,
z1kTo measure the DRSS value in the case of signal power；
2.2) by finding out h (xk) partial derivative, using Taylor expansion to carry out first-order linear truncation, for this purpose, finding out h
(x) Jacobian matrix:
Go down in order to which extended Kalman filter to be enabled constantly is run until systematic procedure terminates, state is pre- under update k-state
The covariance of equation is surveyed, covariance updates at this time are as follows:
P (k | k)=[I-KH (X (k | k-1))] P (k | k-1) (4.9)
Find out gain matrix K:
K=P (k | k-1) HT(X(k|k-1))S-1 (4.10)
Wherein S are as follows:
S=H (X (k | k-1)) P (k | k-1) HT(X(k|k-1))+R(k) (4.11)
Wherein R (k) is the measurement error at k moment, meets Gaussian Profile；
Covariance P (k+1 | k) is updated again, is had:
P (k+1 | k)=FP (k | k) FT+Q (4.12)
Q is process noise, is taken:
It is assumed that process noise is not present in positive location problem, then there is q11=0, to guarantee that P is nonsingular, q22It is set as one not
The minimum positive number for being zero；
The status predication equation for finding out the k moment, obtains accurately position location.
5. according to the method described in claim 1, it is characterized in that the method is after step 2, further includes:
3) unmanned plane is scanned for and is tracked to jammer.
6. according to the method described in claim 5, it is characterized in that the step 3) specifically:
3.1) in the search phase, to avoid interference from removing uncertainty, and more possible positions are provided to carry out more accurately
DRSS positioning makes unmanned plane keep serpentine path operation, controls course angle；
3.2) in tracking phase, it is contemplated that jammer is the lift of unknown object or hostility target and fixed-wing unmanned plane
Characteristic is needed using target point as the center of circle, and certain distance is used in tracking phase as a result, to spiral on the circuit orbit of radius
Literary Liapunov vector field navigation；
Liapunov vector field:
Wherein, r is distance of the aircraft with target, rdFor desired distance, v0For the speed of unmanned plane,It is the point in x-axis direction
On desired speed, ydFor the desired speed of point on the y axis；
Kept by controlling the course angle of unmanned plane unmanned plane at each point in vector field can with desired angle and
Speed flight, has:
Differential is done to above formula, obtains desired angular speed:
By Liapunov vector field navigate during, need to be corrected by continuous measurement and positioning predicted position with
Realization more accurately positions.
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| CN110333480A (en) * | 2019-06-27 | 2019-10-15 | 南京航空航天大学 | A kind of single unmanned plane multiple target AOA localization method based on cluster |
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