CN111090079B - Radar networking radiation interval optimization control method based on passive sensor cooperation - Google Patents
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- 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
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Abstract
The invention discloses a radar networking radiation interval optimization control method based on passive sensor cooperation, which comprises the following steps: (1) All airplanes in the airplane formation are loaded with an airborne phased array radar and a passive sensor, the radar networking comprises N airborne phased array radars, the passive sensor can receive radiation signals from targets, and the radiation signals of the targets are utilized to perform passive arrival time difference TDOA positioning; (2) The radar networking tracks Q dispersed maneuvering targets in a two-dimensional plane, and a movement model of the targets is constructed according to the tracking; (3) The BCRLB is used for acquiring a target state estimation error during maneuvering target tracking and is used for measuring the prediction tracking precision of the maneuvering target at the next moment; (4) The radar networking radiation interval optimization control model is constructed, so that the radar radiation frequency of the radar networking in the tracking process can be minimized on the premise of ensuring the tracking precision of all targets; and (5) solving the radiation interval optimization control model.
Description
Technical Field
The invention belongs to the electronic countermeasure technology, and particularly relates to a radar networking radiation interval optimization control method based on passive sensor cooperation.
Background
The future advanced fighter is endowed with very important combat mission, the confronted combat environment is very complex, and the fighter can only stealth and unbroken the fighter from the labyrinthine and complex defense of various advanced sensors, and can also survive in severe battlefield environments such as electronic interference and anti-radiation missiles. The purpose of stealth is to reduce the active and passive characteristics of the target so that enemy sensors cannot detect or accurately detect the target information. In modern warfare, various novel passive detection devices such as ELINT, ESM, RWR, ARM and the like are continuously put into use, and the system has the advantages of long acting distance, strong concealment, difficulty in finding, and the like, and forms a serious threat to the battlefield viability and the outburst prevention capability of the fighter, and the safety of the fighter cannot be ensured only by means of a passive stealth technology. Therefore, the active stealth technology of the aircraft, namely the radio frequency stealth technology, is increasingly valued by students and research institutions at home and abroad.
The aircraft radio frequency stealth technology is to reduce the capability of passive detection equipment to capture, sort, identify and the like of radio frequency signals by reducing the radio frequency signal characteristics of active electronic equipment such as an airborne radar, a data chain and the like. As an important active stealth technology, the radio frequency stealth technology of the aircraft aims at reducing the interception probability and interception distance of a passive detection system, improving the anti-reconnaissance and anti-interference capabilities of a radar, and guaranteeing the important means of first enemy discovery, first enemy striking and first enemy destroying of a fighter plane.
The airborne radar networking comprises a plurality of airborne phased array radars with different aircraft platforms, different working modes and different frequency bands, and the data fusion and the parameter control of the adaptive radar transmitter are carried out in the fusion center, so that the searching, positioning, tracking and identifying tasks of targets can be better completed. The radio frequency stealth performance of the airborne radar networking can be improved from the perspective of time resources, and the method is mainly embodied on the optimization control of radar radiation intervals. When a small radiation interval is adopted for tracking a target, the airborne radar networking carries out radiation on the target at a high frequency to obtain echo information and update the motion state of the target, so that higher target tracking precision can be obtained, but the probability of being found by a passive detection system is increased due to frequent radiation of the radar, and the radio frequency stealth performance of the airborne radar networking is poor at the moment. When a target is tracked by adopting a large radiation interval, the target tracking precision is lower, and meanwhile, the radar networking has good radio frequency stealth performance due to the reduction of radiation times.
Disclosure of Invention
The invention aims to provide a radar networking radiation interval optimization control method based on passive sensor cooperation, which can maximize the sampling interval of radar networking to all targets on the premise that all target prediction tracking accuracy meets constraint conditions, namely reduce radar radiation times and improve the radio frequency stealth performance of the radar networking.
The technical solution for realizing the purpose of the invention is as follows: a radar networking radiation interval optimization control method based on passive sensor cooperation comprises the following steps:
step one: all airplanes in the airplane formation are loaded with an airborne phased array radar and a passive sensor, the radar networking comprises N airborne phased array radars, the passive sensor can receive radiation signals from targets, and the radiation signals of the targets are utilized to perform passive arrival time difference TDOA positioning;
step two: the radar networking tracks Q dispersed maneuvering targets in a two-dimensional plane, and a movement model of the targets is constructed according to the tracking;
step three: the BCRLB is used for acquiring a target state estimation error during maneuvering target tracking and is used for measuring the prediction tracking precision of the maneuvering target at the next moment;
step four: the radar networking radiation interval optimization control model is constructed, so that the radar radiation frequency of the radar networking in the tracking process can be minimized on the premise of ensuring the tracking precision of all targets;
step five: and solving the radiation interval optimization control model.
Compared with the prior art, the invention has the remarkable advantages that: by optimizing radar radiation intervals when the airborne radar networking tracks multiple targets, the radar radiation times of the radar networking can be reduced while the target tracking performance is ensured, the radio frequency stealth performance of the radar networking is effectively improved, and the tracking precision of all targets is ensured.
Drawings
FIG. 1 is a flow chart of a multi-objective tracking strategy.
Fig. 2 is a target irradiation mark and radiation interval diagram when m=1, where diagram (a) is target 1 and diagram (b) is target 2.
Fig. 3 is a target irradiation mark and radiation interval diagram when m=2, where diagram (a) is target 1 and diagram (b) is target 2.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings.
The invention discloses a radar networking radiation interval optimization control method based on passive sensor cooperation, which comprises the following steps:
step one: all airplanes in the airplane formation are loaded with an airborne phased array radar and a passive sensor, the radar networking comprises N airborne phased array radars, the passive sensor can receive radiation signals from targets and perform passive arrival time difference TDOA positioning by utilizing the radiation signals of the targets, and the method comprises the following specific steps:
all aircraft platforms of the airborne radar networking are loaded with passive sensors, (x) i ,y i ) Is the position coordinate of the ith passive sensor, wherein i is more than or equal to 1 and less than or equal to N, and the distance between the target q and each passive sensor at the moment kThe method comprises the following steps:
wherein ,the position of the target q at time k. Assuming that the passive sensor 1 is the master station and the remaining passive sensors are the slaves, the time difference between arrival of the signal of the target q radiation at time k at the master station and arrival at the respective slaves ∈ ->The method comprises the following steps:
where c is the propagation velocity of the electromagnetic wave. Substitution of formula (1) into formula (2) can result in:
the above equation is N-1 hyperbola equations, according to hyperbolaCan determine the position of the target q at time k. Will beAs observations of a passive sensor networking observation model, the observation model can be expressed as:
wherein ,nonlinear transfer function, and-> Measurement error of the passive sensor network for the k moment to the target q,>is the mean value is zero, the variance is +.>Is a white gaussian noise of (a) and (b),it can be calculated as:
wherein ,is->Standard deviation of the observed error of +.>Is-> and />Is used for the correlation coefficient of the (c).
Step two: the radar networking tracks Q dispersed maneuvering targets in a two-dimensional plane, and a motion model of the targets is constructed according to the tracking maneuvering targets, wherein the method comprises the following steps of:
there are Q maneuver targets distributed in a two-dimensional plane, and the motion model of the Q-th target can be described as:
where q=1,..q,is the state vector of target q at time k, < >>Andthe position and the speed of the target q at the moment k are respectively shown, F represents a target state transition matrix, the motion track of the maneuvering target consists of a uniform speed model and a coordinated turning model, and the corresponding target state transition matrix can be respectively shown as follows:
and (3) a uniform motion model:
coordinated turning model:
where T represents an observation time interval and ω represents a turning factor. W (W) q The process noise representing the target Q can be assumed to be zero in mean and Q in variance q White gaussian noise, Q q Can be expressed as:
wherein ,process noise intensity for target q.
Step three: the BCRLB is used for acquiring the target state estimation error during maneuvering target tracking and is used for measuring the prediction tracking precision of the maneuvering target at the next moment, and specifically comprises the following steps:
obtaining a predicted Bayesian Information Matrix (BIM) of the target q state at the moment k according to the predicted probability of each model of the target q and the predicted value of the target stateThe method comprises the following steps:
wherein ,BIM for the target q-state at time k-1, -/-, for example> and />Respectively measuring predicted values of a noise covariance matrix and a Jacobian matrix of a target q by a k-moment radar i, F j State transition matrix representing object model j, Q q Is the variance of the process noise>For the prediction probability of model j, +.>Assigning an index to radar, < >>Indicating that radar i irradiates target q at time k,/->The radar i does not irradiate the target q at the moment k;
the predicted Bayesian Clamamaro boundary (BCRLB) of the target q at the moment k can be obtained by inverting the predicted BIM of the target q state
Step four: the radar networking radiation interval optimization control model is constructed, and the radar radiation times of the radar networking in the tracking process can be minimized on the premise of ensuring the tracking precision of all targets, and the method comprises the following steps:
step 4-1, irradiation target selection model
Taking the predicted tracking precision of all targets at the moment k as constraint conditions, taking the minimum number of targets to be irradiated as an objective function, and establishing an optimized control model of target irradiation indexes:
wherein ,at time kTarget q predicted position estimate lower bound of mean square error,/->Predictive BCRLB, F for k-time target max For a preset +.>Threshold value of->Is a target irradiation index, & lt, & gt>Indicating that irradiation of the target q is required at time k, < >>Indicating that the target q does not need to be irradiated at time k. Solving the above method to obtain the optimal result of all target tracking indexes at the moment k>
Step 4-2, selecting a model by radar allocation mode
According to the obtained v k,opt And (3) establishing an optimal control model of the radar allocation index by taking the highest sum of the predictive tracking precision of the target to be irradiated at the moment k as an objective function:
wherein ,assigning an index to radar, < >>The target to be irradiated at the moment k is fixedly irradiated by M radars, wherein M is more than or equal to 1 and less than or equal to N, and N is more than or equal to 1>Each radar irradiates a target at most at the same time, and the optimal control model is solved to obtain the result of the radar distribution index at the moment k> wherein />
Step five: solving a radiation interval optimization control model, wherein the method comprises the following steps of:
the first term in the predicted BIM of the maneuvering target is related to the BIM of the target state at the moment k-1 only, is irrelevant to the radar distribution mode at the moment k, and can be inverted to obtain the BCRLB of the target state estimation error when the target state is predicted by the prior information
wherein ,BIM, F in the target q state at time k-1 j State transition matrix representing object model j, Q q Is the variance of the process noise>The prediction probability of the target model j;
constructing a target position estimation mean square error lower bound based on prior informationIt can be seen that as long as +.>Hold true->Must be established, wherein->Is the lower bound of the mean square error of the target prediction position estimation, F max The irradiation target selection optimization control model can be equivalently:
wherein ,is a target irradiation index;
aiming at the specific condition that the target to be irradiated at each moment is fixed and irradiated by M radars, M is more than or equal to 1 and less than or equal to N, ifIndicating that irradiation of the target q is not required, if +.>And (3) indicating that the target q needs to be irradiated, calculating the lower limit of the prediction position estimation mean square error of all possible radar combinations on the target to be irradiated, and obtaining the optimal result of the radar allocation index by using an enumeration method.
Example 1
The simulation scene of the airborne radar networking tracking maneuvering target is designed, the number of radars in the airborne radar networking is N=6, the number of targets is Q=2, the system parameters of all the radars are the same, and the parameter settings are shown in table 1.
Table 1 airborne radar networking parameter settings
The observation time interval t=3s and the tracking process duration is 300s. The motion model set of the target comprises uniform motion, left turning motion and right turning motion, and the threshold of the target tracking precision is F max ＝30m。
Fig. 2 and fig. 3 respectively show the irradiation mark and the irradiation time interval of the airborne radar networking to each target after the method of the invention is adopted in the process of tracking a certain monte carlo experimental target when each target is irradiated by one radar and when each target is irradiated by two radars. As can be seen from simulation results, the radiation interval of the optimized airborne radar networking to the target is not fixed to be 3s, the situation that the radiation interval is larger than 3s frequently occurs, and the maximum radiation interval can reach 12s.
The simulation result can be obtained, and the radar radiation times of the airborne radar networking in the tracking process can be reduced by adopting the method of the invention on the premise of ensuring the target tracking precision, thereby improving the radio frequency stealth performance of the multi-target tracking of the airborne radar networking system.
Claims (1)
1. A radar networking radiation interval optimization control method based on passive sensor cooperation is characterized by comprising the following steps:
step one: all airplanes in the airplane formation are loaded with an airborne phased array radar and a passive sensor, the radar networking comprises N airborne phased array radars, the passive sensor can receive radiation signals from targets, and the radiation signals of the targets are utilized to perform passive arrival time difference TDOA positioning;
step two: the radar networking tracks Q dispersed maneuvering targets in a two-dimensional plane, and a movement model of the targets is constructed according to the tracking;
step three: the BCRLB is used for acquiring a target state estimation error during maneuvering target tracking and is used for measuring the prediction tracking precision of the maneuvering target at the next moment;
step four: the radar networking radiation interval optimization control model is constructed, so that the radar radiation frequency of the radar networking in the tracking process can be minimized on the premise of ensuring the tracking precision of all targets;
step five: solving a radiation interval optimization control model;
in the first step, all airplanes in the airplane formation are loaded with an airborne phased array radar and a passive sensor, the radar networking comprises N airborne phased array radars, the passive sensor can receive radiation signals from targets, and the radiation signals of the targets are utilized to perform passive arrival time difference TDOA positioning, and the method specifically comprises the following steps:
all aircraft platforms of the airborne radar networking are loaded with passive sensors, (x) i ,y i ) Is the position coordinate of the ith passive sensor, wherein i is more than or equal to 1 and less than or equal to N, and the distance between the target q and each passive sensor at the moment kThe method comprises the following steps:
wherein ,the position of the target q at the moment k; assuming that the passive sensor 1 is the master station and the remaining passive sensors are the slaves, the time difference between arrival of the signal of the target q radiation at time k at the master station and arrival at the respective slaves ∈ ->The method comprises the following steps:
wherein c is the propagation speed of electromagnetic waves; substitution of formula (1) into formula (2) can result in:
the above formula is N-1 hyperbola equations, and the position of the target q at the moment k can be determined according to the focal point of the hyperbola; will beAs observations of a passive sensor networking observation model, the observation model can be expressed as:
wherein ,nonlinear transfer function, and-> Measurement error of the passive sensor network for the k moment to the target q,>is the mean value is zero, the variance is +.>Is Gaussian white noise, < >>It can be calculated as:
wherein ,is->Standard deviation of the observed error of +.>Is-> and />Is a correlation coefficient of (2);
in the second step, the radar networking tracks Q dispersed maneuvering targets in a two-dimensional plane, and a target motion model is constructed according to the tracking, wherein the method comprises the following steps:
there are Q maneuver targets distributed in a two-dimensional plane, and the motion model of the Q-th target can be described as:
where q=1,..q,is the state vector of target q at time k, < >> and />The position and the speed of the target q at the moment k are respectively shown, F represents a target state transition matrix, the motion track of the maneuvering target consists of a uniform speed model and a coordinated turning model, and the corresponding target state transition matrix can be respectively shown as follows:
and (3) a uniform motion model:
coordinated turning model:
wherein T represents an observation time interval, and ω represents a turning factor; w (W) q The process noise representing the target Q can be assumed to be zero in mean and Q in variance q White gaussian noise, Q q Can be expressed as:
wherein ,process noise intensity for target q;
in the third step, the BCRLB of the target state estimation error during maneuvering target tracking is obtained and is used for measuring the prediction tracking precision of the maneuvering target at the next moment, and the method specifically comprises the following steps:
obtaining the prediction of the target q state at the k moment according to the prediction probability of each model of the target q and the target state prediction valueThe method comprises the following steps:
wherein ,BIM for the target q-state at time k-1, -/-, for example> and />Respectively measuring predicted values of a noise covariance matrix and a Jacobian matrix of a target q by a k-moment radar i, F j State transition matrix representing object model j, Q q Is the variance of the process noise>For the prediction probability of model j, +.>Assigning an index to radar, < >>Indicating that radar i irradiates target q at time k,/->The radar i does not irradiate the target q at the moment k;
the predicted BIM of the state of the target q is inverted to obtain the predicted BCRLB of the target q at the moment k:
in the fourth step, a radar networking radiation interval optimization control model is constructed, and the radar radiation times of the radar networking in the tracking process can be minimized on the premise of ensuring the tracking precision of all targets, and the method specifically comprises the following steps:
step 4-1, irradiation target selection model
Taking the predicted tracking precision of all targets at the moment k as constraint conditions, taking the minimum number of targets to be irradiated as an objective function, and establishing an optimized control model of target irradiation indexes:
wherein ,the lower bound of the mean square error is estimated for the target q predicted position at time k,predictive BCRLB, F for k-time target max For a preset +.>Threshold value of->Is an index of the irradiation of the target,indicating that irradiation of the target q is required at time k, < >>Indicating that irradiation of the target q is not required at the time k; solving the above method to obtain the optimal result of all target tracking indexes at the moment k>
Step 4-2, selecting a model by radar allocation mode
According to the obtained v k,opt And (3) establishing an optimal control model of the radar allocation index by taking the highest sum of the predictive tracking precision of the target to be irradiated at the moment k as an objective function:
wherein ,assigning an index to radar, < >>The target to be irradiated at the moment k is fixedly irradiated by M radars, wherein M is more than or equal to 1 and less than or equal to N, and N is more than or equal to 1>Each radar irradiates a target at most at the same time, and the optimal control model is solved to obtain the result of the radar distribution index at the moment k> wherein />
In the fifth step, solving the radiation interval optimization control model, specifically as follows:
the first term in the predicted BIM of the maneuvering target is related to the BIM of the target state at the moment k-1 only, is irrelevant to the radar distribution mode at the moment k, and can be inverted to obtain the target state estimation error when the target state is predicted by the prior information
wherein ,BIM, F in the target q state at time k-1 j State transition matrix representing object model j, Q q Is the variance of the process noise>The prediction probability of the target model j;
constructing a target position estimation mean square error lower bound based on prior informationIt can be seen that as long as +.>Hold true->Must be established, wherein->Is the lower bound of the mean square error of the target prediction position estimation, F max The irradiation target selection optimization control model can be equivalently:
wherein ,is a target irradiation index;
aiming at the specific condition that the target to be irradiated at each moment is fixed and irradiated by M radars, M is more than or equal to 1 and less than or equal to N, ifIndicating that irradiation of the target q is not required, if +.>Indicating that target q needs to be advancedAnd (3) performing row irradiation, calculating a lower limit of a predicted position estimation mean square error of all possible radar combinations on the target to be irradiated, and obtaining an optimal result of the radar allocation index by using an enumeration method.
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