CN114114240A - Three-dimensional target tracking method and device of ultra-sparse array under influence of grating lobes - Google Patents
Three-dimensional target tracking method and device of ultra-sparse array under influence of grating lobes Download PDFInfo
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- G—PHYSICS
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- 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 three-dimensional target tracking method of the ultra-sparse array radar under the grating lobe fuzzy comprises the following steps: establishing a radar state equation and a radar measurement equation of the ultra-sparse array radar under grating lobe ambiguity; establishing a Kalman filter bank for distinguishing grating lobes and estimating a target state according to the radar state equation and the radar measurement equation; initializing the Kalman filter bank; acquiring radar target measurement information of the ultra-sparse array radar at each moment; the Kalman filter bank tracks the target according to the initial setting value and the radar target measurement information until the target tracking is finished; the influence of the grating lobe is eliminated before the signal processing process, so that the angle measurement fuzzy phenomenon is eliminated, more accurate target azimuth estimation information is obtained, and more accurate three-dimensional target tracking is realized.
Description
Technical Field
The invention belongs to the technical field of radar data processing, and particularly relates to a three-dimensional target tracking method of an ultra-sparse array under the influence of grating lobes.
Background
Target tracking is the main function of the radar system, and grating lobes are factors which have a large influence on the accuracy of target track estimation. The generation of grating lobes is inseparable from the design of the radar antenna itself. When there is a high requirement for the resolution of the radar system, it is often necessary to increase the aperture of the antenna array, which is equivalent to reducing the beam width in the array signal processing, so as to improve the accuracy of the azimuth estimation. The most intuitive method is to set the array element spacing according to the upper limit of the frequency band of the processing signal, and increase the array aperture by directly increasing the number of the array elements, however, the arrangement mode of the dense array elements with large area is too high in cost and is often difficult to realize. In order to reduce the manufacturing cost and complexity of the antenna array, the design of sparsely arranged antenna units can be only adopted, and the method realizes that a larger array aperture is achieved by using a smaller number of array elements and a lower cost, and the expected array performance is achieved by using a lower cost through a mode of reducing the number of the array elements and the number of channels. However, the larger array element spacing can cause the occurrence of grating lobe effect, and the grating lobe can also generate corresponding incoming waves, so that the azimuth ambiguity occurs when the target is tracked, and the arrival direction of the target cannot be accurately determined. When multiple targets exist, grating lobes of a strong target cause serious interference to detection and estimation of a target with weak signal characteristics, and even a weak target cannot be detected. In the three-dimensional target tracking, the azimuth angle and the pitch angle are influenced by grating lobe multivalues, and the phenomenon of angle measurement multivalues occurs.
The method commonly adopted by radar for angle measurement is a phase interferometer direction finding positioning technology, and the direction is obtained by comparing the phases of two antennas. The principle of single baseline phase interference is shown in figure 1.
If the angle between the incoming wave direction and the normal direction is phi, the time when the plane wave front reaches the antenna unit 1 and the antenna unit 2 is prior, and the phase difference exists on the fixed frequency signal. In order to improve the estimation accuracy of the angle of arrival, a method of increasing the length of the base line is generally adopted. However, when the length of the base line is longer than half wavelength, the real direction of the incoming wave cannot be distinguished due to the existence of grating lobe, and the phenomenon that the incoming wave is truly directed can be generatedThis is an angular blurring phenomenon, among many possible consequences. In this case, the phase difference is an observed value blurred by 2 pi, and the angle observed value and the actual value have a corresponding relation
Where k is a number of unknown ambiguity values, the number of which is related to the number of grating lobes. The occurrence of this situation means that k +1 observed values can occur at the same time at the same sampling point, wherein one observed value is an actual value, and the other observed values are fuzzy values. Considering that the grating lobes often appear in pairs and are symmetrically distributed on two sides of the main lobe, the value of k is also often a positive or negative number in pairs.
Let the array element spacing be d, and the wavelength be λ, φ0For the main lobe corresponding angle, when, there are grating lobes in the directions corresponding to phi, the grating lobes have the same amplitude as the main lobe. In order to avoid grating lobes, it is necessary to require. Suppose the angle between the target azimuth and the array is phisThe position and width of the grating lobe can be predicted according to the natural directivity function, and the m-th grating lobe appears in the direction
φm＝arccos(cosφs±mλ/d),m＝1,2,…,0°＜θm＜180°
φm＝arccos(cos(2π-φs)±mλ/d),m＝1,2,…,180°＜θm＜360°
Or
φm＝arcsin(sinφs±mλ/d),m＝1,2,…,-90°＜θm＜90°
φm＝arcsin(sin(2π-φs)±mλ/d),m＝1,2,…,90°＜θm＜270°
When m is 0, the position is the main maximum position, the positions where other integers m in the measuring region appear are all grating lobe positions, and a plurality of grating lobes may exist in the measuring region. When the array element spacing is half the wavelength, i.e. d ═ λ/2, then no grating lobes exist. When d is 2 λ, and the scan range is 0. To 180. The corresponding m is-2, -1, 1, 2, for a total of 4 grating lobes, corresponding to an angle of 180. ,120. ,60. ,0. . When d is 3 λ, there are 6 grating lobes, and so on.
When the dimension of the target position change is three-dimensional, two angles are used for representing the target position, one is an azimuth angle, the other is a pitch angle, and the grating lobes can generate angle measurement multivalue influence on both the azimuth angle and the pitch angle.
The main processing idea for dealing with the angle measurement ambiguity caused by the grating lobe at present is to optimize the array element arrangement mode of the radar antenna, so as to avoid the occurrence of the grating lobe, which is to solve the problem of the influence of the grating lobe on the idea of directly eliminating the grating lobe. The array grating lobe influence suppression is realized by the antenna array element optimized arrangement mode, and the method comprises the following methods.
One common method is to avoid grating lobe influence caused by sparse antenna arrays by means of a combined array method or an optimized sparse array technology. Studies on such methods include: 1) the grating lobe influence caused by the sparse array is reduced by adopting a mode of optimizing the array surface arrangement of the antennas and forming non-periodic arrangement of array elements. The method is to make the directional diagram of the sub-array be as close to the factor directional diagram of the flat array as possible in the electric scanning range, and no energy radiation exists outside the scanning range, so as to disperse the energy of the grating lobe again, for example, the sub-array with random dislocation prevents the generation of the grating lobe, or the random arrangement of the sub-array is used to break the periodicity of the arrangement of the array surface, or the form of the unequal-interval arrangement of the units is used to break the regularity of the sub-array, and the receiving array and the transmitting array with different array element intervals are used to avoid the generation of the grating lobe. 2) By optimally designing the sparse array, the grating lobe spectrum level is suppressed to a range meeting the requirement by using the minimum number of active array elements. The periodic structure of the array surface is disturbed to form a non-periodic array, the radiation units in the array are designed into high-efficiency radiation units, and the high-efficiency radiation units are organically combined to jointly inhibit grating lobes from appearing. Such methods require the shape of the array to be designed, the performance of which against grating lobe effects is mainly determined by the array shape.
The other method is to reduce the array sparsity degree to resist the grating lobe influence caused by the array sparsity in the signal processing through a virtual interpolation array element method, virtualize a larger number of effective virtual array element numbers through reasonably designing the layout of the transceiving array elements, achieve more effective array element numbers through the method, and realize the inhibition of the grating lobe by utilizing the optimal configuration of the array and the method of the virtual aperture. Other processing methods include a processing method suitable for a split sub-array combining a time domain cross-correlation method and a spatial domain processing method, an array intensity meter method, and a grating lobe influence suppression algorithm for a step signal, a spatial filter for suppressing power transmission of electromagnetic waves at a grating lobe position, and the like.
The above mentioned methods solve the problem at the root and avoid the problem by eliminating the grating lobes, however, in some cases, the occurrence of the grating lobes cannot be avoided, such as a mobile distributed radar, and the radar system can be regarded as a giant antenna array with time-varying array element arrangement, and the shape of the giant antenna array is difficult to maintain without the grating lobes. In addition, array element arrangement optimization is to perform corresponding setting for specific wavelengths, and the wavelengths of radar beams are not fixed and unchanged, and when the wavelengths are shortened and the original array is not changed, grating lobes may also occur. When the grating lobe appears, the consideration of eliminating the angle measurement blur caused by the grating lobe is needed, and the angle measurement corresponding to the main lobe is identified, so that the radar can still realize continuous tracking of the target under the influence of the grating lobe, and higher estimation precision is ensured.
Disclosure of Invention
Aiming at the defects, a target tracking method based on a Kalman filter bank is provided, and different expressions of a Kalman filter are used for distinguishing angle measurement corresponding to a main lobe and a grating lobe, so that the tracking precision is improved.
The three-dimensional target tracking method of the ultra-sparse array radar under the grating lobe fuzzy condition is characterized by comprising the following steps:
respectively establishing a radar state equation and a radar measurement equation of the ultra-sparse array radar under grating lobe ambiguity;
establishing a Kalman filter for distinguishing grating lobes and estimating a target state according to the radar state equation and the radar measurement equation;
initializing the Kalman filter;
acquiring radar target measurement information of the ultra-sparse array radar at each moment;
and tracking the target according to the initial setting value, the radar state equation and the radar measurement equation until the target tracking is finished.
The three-dimensional target tracking device of the ultra-sparse array radar under the grating lobe fuzzy condition is characterized by comprising the following components:
the equation building module is used for respectively building a radar state equation and a radar measurement equation of the ultra-sparse array radar under the grating lobe fuzzy condition;
the Kalman filter construction module is used for establishing a Kalman filter for distinguishing grating lobes and estimating a target state according to the radar state equation and the radar measurement equation;
an initialization module for initializing the Kalman filter;
the measurement information acquisition module is used for acquiring radar target measurement information of the ultra-sparse array radar at each moment;
and the target tracking module is used for tracking the target according to the initial setting value and the radar target measurement information until the target tracking is finished.
The invention has the advantages that:
the angle measurement fuzzy resolution method based on the Kalman filter bank is realized in a manner that a plurality of Kalman filters are used for carrying out parameter estimation on a plurality of angle measurements generated by grating lobes and main lobes together in a one-to-one correspondence mode respectively, and the main lobes and the grating lobes are distinguished according to deviation values obtained by different Kalman filters, so that information corresponding to the main lobes is directly used in subsequent signal processing, and influence caused by the grating lobes is avoided. According to the method, the influence of the grating lobe can be eliminated before the signal processing process is carried out, so that the angle measurement fuzzy phenomenon is eliminated, more accurate target direction estimation information is obtained, and more accurate three-dimensional target tracking is realized.
Drawings
FIG. 1 is a single baseline phase interference schematic;
FIG. 2 is a schematic diagram of a system architecture;
FIG. 3 is a diagram showing simulation results;
Detailed Description
The present invention will be further described with reference to the accompanying drawings, it being understood that the description herein is illustrative and explanatory only and is not restrictive of the invention, as claimed.
The application scenario of the method is that when the grating lobe occurs unavoidably, the Kalman filter set is used for distinguishing the signals corresponding to the main lobe and the grating lobe, so that the influence caused by the grating lobe is eliminated before the signal processing process is started, the three-dimensional target tracking is realized, and the estimation result of the target position is ensured to have higher precision. The problem that a coping method after a grating lobe appears is lacked in the prior art is solved, and a guarantee is provided for three-dimensional target tracking of a distributed ultra-sparse array.
According to a grating lobe forming mechanism, Kalman filtering is utilized to distinguish which angle measurement is an observed value corresponding to a main lobe from an angle measurement fuzzy phenomenon, and the angle measurement fuzzy phenomenon needs to be modeled so as to be combined with a Kalman filter. The angle measurement fuzzy phenomenon can be regarded as that a plurality of different measurement angles exist at the same sampling point, but in actual measurement, due to the limitation of the scanning range of the radar antenna, the number of the measurement angles existing at the same sampling point does not meet the condition that fuzzy values appear in pairs, and the random distribution characteristic is certain.
In the actual working process of the radar antenna, due to the relation between the radar scanning range and the positions of the main lobe and the grating lobe, the following three conditions may occur: 1. the measured values obtained at a sampling point comprise the measured values corresponding to the main lobes, and the measured values corresponding to the grating lobes appear in pairs; 2. the measured values obtained on one sampling point comprise the measured values corresponding to the main lobes, and the measured values corresponding to the grating lobes do not appear in pairs; 3. the measurements taken at a sample point do not contain measurements corresponding to the main lobe. For the kalman filter bank, only which main lobe is identified can be distinguished, so that the target position is calculated according to the main lobe information, but the position of the target cannot be obtained from the grating lobe information, and therefore, the main lobe identification work can be completed only for the former two cases. For the third case, when it is considered that it is necessary to ensure that the main lobe is always within the scanning range when the target tracking is performed, and when no main lobe information is available, the target tracking may be considered as impossible to perform, so the third case is not considered in the algorithm of the present application.
For the change of the angle measurement quantity caused by the change of the radar scanning range, different Kalman filter sets can be constructed according to different scanning ranges. Considering that the number and distribution of grating lobes can be calculated according to a correlation formula, on the premise that the radar structural mode or the distribution mode of a multi-radar combination can be determined, the number of grating lobes contained in a scanning range can be directly calculated, so that the number of Kalman filters is determined, and the subsequent algorithm design work is completed. In the Kalman filter estimation process, the radar scanning range should be maintained in an initial setting state, otherwise, the change of the scanning range can cause the Kalman filter bank to be incapable of corresponding to the actual situation, and the estimation work cannot be finished.
The Kalman filter generally used in anomaly detection is essentially a class of anomaly detection methods based on a simulation algorithm, and the implementation process of the method comprises two parts: on the one hand, residual errors occur; another aspect is residual decision. The occurrence of the residual error represents the amount of abnormal events based on one or a group of functions established by the device model; the residual decision represents that based on the determined residual value, a proper threshold value and a decision standard are established to explore the source of the anomaly, which is the basic flow of the residual decision. The abnormality detection flow based on the Kalman filter comprises the following contents: firstly, the deviation between the predicted value and the actual measured value of the Kalman filter is compared to obtain residual signals, namely, residual Weighted Square Sums (WSSR), and the quantity of the residual signals represents the quantity of abnormal states, so that the device is judged to have no fault. Secondly, evaluating on the basis of the abnormality of the residual error, wherein if the abnormality does not exist in the device, the residual error value of the device is in a certain small range in theory more stably; if the abnormal condition exists, the residual error is about to have a large error, whether the abnormal condition exists or not can be judged by using the residual value, and the type, the range and other related data of the abnormal condition are judged, so that the abnormal condition is detected.
The method aims at the problem of angle measurement multivalued anomaly detection, and a corresponding method is used for processing a Kalman filter bank constructed on the basis of Kalman filters with different input information. The Kalman filter bank covers n Kalman filters, the part related to the fuzzy value k is removed from the augmented state variable of each Kalman filter, wherein n is the total number of angle measurement sets (each angle measurement set comprises an azimuth angle and a pitch angle) received by the radar, and each Kalman filter receives the detection value of 1 angle measurement set. Whether the main lobe or the grating lobe corresponding to any angle measurement group is judged through 1 Kalman filter corresponding to the main lobe or the grating lobe. After the Kalman filter bank measures and calculates the residual errors, n residual errors are obtained. When the ith angle measurement is the angle measurement corresponding to the main lobe, the information obtained by the ith Kalman filter does not contain the interference of a fuzzy value, the estimation result is more accurate, and the residual error value is approximately equal to 0. However, the angle measurement corresponding to other kalman filters is the angle measurement corresponding to the grating lobe, and the estimation process is interfered by the fuzzy value, so that the final estimation result has a certain deviation from the measurement value, and the residual value is increased due to the prediction deviation. According to the comparison of different magnitudes of residual values of the n Kalman filters, the minimum value can be identified, and the magnitude difference exists between the minimum value and other residual values, so that the angle measurement corresponding to the main lobe can be determined.
When the Kalman filter bank carries out angle measurement fuzzy processing, the value of the threshold is the key for judging the main lobe. Setting the threshold corresponding to the WSSR at a smaller value can improve the probability of successful main lobe resolution, but due to noise interference, the probability of erroneous judgment is increased, the main lobe is also judged as a grating lobe, and setting the threshold at a larger value can lead to an opposite result, so that the setting of the threshold has a great influence on the table of the main lobe resolution function of the hybrid kalman filter bank. A reasonable threshold setting mode is to establish a series of corresponding thresholds according to the difference of the measurement precision of the radar in different monitoring areas, so that the accuracy of the angle measurement fuzzy processing is improved.
In view of the foregoing, the present application provides a target tracking method based on a distributed sparse array under the influence of grating lobe angle measurement blur, which includes:
step S00: and establishing a radar state equation under the condition of the influence of the grating lobes.
When the grating lobe exists, the radar can generate a plurality of angle measurement values due to the influence of the grating lobe, different angle measurement values can correspond to different fuzzy numbers, and the fuzzy numbers do not influence the state parameters of the monitored object, so that the established state equation does not set the fuzzy numbers, and the corresponding state equation of the monitored object can be expressed as shown in the following formula
Where x, y and z are the positional information of the monitored target,
and
for the x-axis, y-axis and z-axis velocity components of the monitored object,
the state equation contains the system process noise W with a covariance matrix of
Q＝E(WWT)
Step S01: and establishing a measurement equation of the distributed array radar with the grating lobes.
Parameters that can be measured by the radar include target distance, target velocity, and multiple goniometers due to grating lobes. Since it is initially not possible to determine which of the main lobes and which of the grating lobes are, the goniometry of a plurality of different lobes may be numbered, with the output equation for the ith lobe being
Whereinr is the distance between the monitored object and the radar,
is the speed of the object being monitored and,
is the azimuth angle of the monitored object, theta is the pitch angle of the monitored object, kfw,iAnd kfy,iFuzzy values corresponding to an azimuth angle and a pitch angle respectively, measurement noise W is contained in an output equation, and a covariance matrix is
R＝E(WWT)
Step S02: and establishing a Kalman filter model for distinguishing grating lobes and estimating the state of the target according to the state equation of the step S00 and the measurement equation of the step S01. And constructing a Kalman filter for each angle measurement, and then forming a Kalman filter bank.
The state space model of the kalman filter corresponding to the ith measurement angle is constructed on the basis of the state equation of step S00 and the measurement equation of step S01. The structure of the Kalman filter for measuring the angle corresponding to the ith lobe is
Wherein xkal,i、ykal,iAnd zkal,iThe orientation information of the monitored target estimated for the ith kalman filter,
and
estimating the velocity components, r, of the monitored target in the x-axis, y-axis and z-axis for the ith Kalman filterkal,iMonitored object being Kalman filter estimationThe distance between the body and the radar is,
is the velocity of the monitored object estimated by the kalman filter,
is the azimuth angle, theta, of the monitored object estimated by the Kalman filterkal,iIs the pitch angle of the monitored object as estimated by the kalman filter. WSSRiThe weighted sum of squares of the residuals between the estimate and the measurement of the ith Kalman filter. And judging which filter corresponds to the main lobe angle measurement according to the difference of the residual weighted square sums calculated by different Kalman filters, thereby avoiding grating lobe influence.
It can be seen from the above equation that the augmented state variable of the kalman filter does not include two fuzzy values, because when tracking a three-dimensional target, if the kalman filter is used to directly estimate a fuzzy value caused by a grating lobe, because there are fuzzy values in the azimuth angle and the pitch angle, the kalman filter needs to estimate five augmented state variables, including the velocities corresponding to the three axes x, y, and z and two fuzzy values, and the measurable output available for the kalman filter only includes four parameters, namely, the azimuth angle, the pitch angle, the velocity, and the distance, and if the augmented state variable includes two fuzzy values, the parameter to be estimated is more than the input parameter of the kalman filter, thereby making the estimation work of the kalman filter impossible.
In order to distinguish the main lobe from the grating lobe without introducing an augmentation state variable, the method is adopted to indirectly reflect the influence caused by the fuzzy value by using other numerical values.
The calculation of the gain matrix K in this filter is as follows. Because the gain matrix of the kalman filter cannot be directly calculated by the nonlinear output equation, the nonlinear equation needs to be linearly calculated with small deviation, so as to obtain the gain matrix
Where r is the distance between the monitored object and the radar,
is the speed of the object being monitored and,
is the azimuth angle of the monitored object, θ is the pitch angle of the monitored object, and V is the measurement noise. H is an output equation
Considering the robustness of the Kalman filter, the Kalman filter should meet the corresponding quadratic index, and the gain of the Kalman filter needs to minimize the quadratic index. According to the extreme principle, an optimal gain matrix formula can be derived as
K＝PHTR-1
Wherein P is a time update matrix and can be calculated by Riccati equation corresponding to a linear system
Where Q is the covariance matrix of the system noise W, Q ═ E (WW)T) R is a covariance matrix of the measurement noise V, and R ═ E (VV)T)。
Because the system forming the Kalman filter is a nonlinear system, the gain matrix K needs to be updated according to the change of the system operation state, and the specific update equation is
P＝FPFT+Q
K＝PHT(HPH+R)-1
P＝P-KHP
Step S03: the distributed array radar observes the target to obtain an observation value of the target at each moment.
Step S04: and (5) initially setting a target tracking process according to the Kalman filter in the step S02 and the target radar observation value in the step S03.
The main purpose of this step is to perform the initialization setting of the kalman filter bank. Firstly, determining the number of the augmentation state variables of the Kalman filter according to the number of the angle measurements in the received observation value; and then endowing the Kalman filter with a group of initial values of target information before the estimation work of the Kalman filter is started, and calculating the numerical value of a gain matrix K in the Kalman filter according to the initial values.
Step S05: and tracking the target from the time k to the time k +1 according to the initial setting value of the step S04 and the Kalman filter set constructed in the step S02 until the target tracking is finished.
Since the initial value may not completely coincide with the actual state of the target and there is a certain deviation, the kalman filter needs to calculate a new estimated value in the subsequent estimation process, so that the new estimated value continuously approaches the observed value, thereby eliminating the deviation. However, only the angle measurement corresponding to the main lobe is not interfered by the fuzzy value, the estimation deviation of the kalman filter corresponding to the main lobe can reach the minimum, the weighted residual square sum WSSR calculated by the corresponding kalman filter is the minimum in magnitude, and the WSSR calculated by other kalman filters is larger in magnitude than the calculation result corresponding to the main lobe, so that which angle measurement is the angle measurement corresponding to the main lobe is distinguished.
According to the target tracking method for the distributed sparse array with the grating lobes, the main lobes and the grating lobes can be distinguished from corresponding angle measurements, and accurate target information can be obtained.
In practical application, as shown in fig. 2, a distributed base radar system can be regarded as a large sparse antenna array, due to the influence of grating lobes, a target signal received by the radar system includes a plurality of different angle measurements, a kalman filter estimates a target azimuth and a fuzzy number according to received information, and when estimated information of the filter has a high degree of matching with an observed value, state parameters estimated by the filter can reflect the current state of an object more accurately.
The method was verified by simulation experiments. The tracked target in the simulation runs at a fixed speed, and the radar continuously tracks the target. Simulation middle pair
And
process noise interference with a mean square error of 0.05 is applied, measurement noise interference with a mean square error of 1 is applied to the distance r, and the azimuth angle is
And the pitch angle theta is applied with the measurement noise interference with the mean square error of 0.05, and the velocity V is applied with the measurement noise interference with the mean square error of 0.05. The Kalman filter bank needs to process angle measurement ambiguity under noise interference, distinguish angle measurement corresponding to a main lobe and a grating lobe, and effectively position and track a target.
As can be seen from fig. 3 corresponding to the simulation result, the WSSR calculated by the kalman filter based on the main lobe information is significantly smaller than the WSSR calculated based on the grating lobe information, and which is the main lobe is determined according to the difference of the WSSRs, thereby avoiding the influence of the grating lobe. Because of no interference of fuzzy values, the estimation result of the Kalman filter corresponding to the main lobe is more accurate, the deviation is smaller, the corresponding WSSR is closer to 0, the WSSR value calculated by the Kalman filter corresponding to the grating lobe is larger, and through the difference, which is the angle measurement corresponding to the main lobe can be judged from the multiple angle measurements.
Finally, it should be noted that: although the present invention has been described in detail, it will be apparent to those skilled in the art that changes may be made in the above embodiments, and equivalents may be substituted for elements thereof. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The three-dimensional target tracking method of the ultra-sparse array radar under the grating lobe fuzzy condition is characterized by comprising the following steps:
respectively establishing a radar state equation and a radar measurement equation of the ultra-sparse array radar under grating lobe ambiguity;
establishing a Kalman filter for distinguishing grating lobes and estimating a target state according to the radar state equation and the radar measurement equation;
initializing the Kalman filter;
acquiring radar target measurement information of the ultra-sparse array radar at each moment;
and tracking the target according to the initial setting value, the radar state equation and the radar measurement equation until the target tracking is finished.
2. The method of claim 1, wherein establishing radar state equations of the ultra-sparse array radar under grating lobe ambiguity,
the radar state equation satisfies the following relation:
where x, y and z are the positional information of the monitored target,
and
for the x-axis, y-axis and z-axis velocity components of the monitored object,
the state equation contains the system process noise W.
3. The method of claim 2, wherein the radar measurement equation satisfies the following relationship: the output equation corresponding to the ith lobe is
Where r is the distance between the monitored object and the radar,
is the speed of the object being monitored and,
is the azimuth angle of the monitored object, theta is the pitch angle of the monitored object, kfw,iAnd kfy,iFuzzy values corresponding to the azimuth angle and the pitch angle respectively, and the output equation comprises measurement noise V.
4. The method of claim 3, wherein the Kalman filter satisfies the following relationship: establishing a Kalman filter bank, wherein each filter corresponds to an angle measurement, and the structure of a nonlinear Kalman filter which is constructed in the filter bank and corresponds to the angle measurement of the ith lobe is
Wherein xkal,i、ykal,iAnd zkal,iThe orientation information of the monitored target estimated for the ith kalman filter,
and
estimating the velocity components, r, of the monitored target in the x-axis, y-axis and z-axis for the ith Kalman filterkal,iIs the distance between the monitored object and the radar estimated by the kalman filter,
is the velocity of the monitored object estimated by the kalman filter,
is the azimuth angle, theta, of the monitored object estimated by the Kalman filterkal,iIs the pitch angle, WSSR, of the monitored object estimated by the Kalman filteriAnd judging which angle measurement corresponding to the filter is the main lobe angle measurement according to the difference of the residual weighted square sums calculated by different Kalman filters, so as to avoid grating lobe influence.
5. The method of claim 4, wherein the gain matrix K in the filter is calculated as follows for a non-linear output equation
Carrying out small deviation linearization calculation to obtain
Where r is the distance between the monitored object and the radar,
is the speed of the object being monitored and,
is the azimuth angle of the monitored object, theta is the pitch angle of the monitored object,v is the measurement noise, H is the corresponding linearized parameter matrix,
where H is the output equation
The gain matrix is formulated as
K＝PHTR-1
Wherein P is a time update matrix, and is calculated by Riccati equation corresponding to the linear system
Where Q is the covariance matrix of the system noise W, Q ═ E (WW)T) R is a covariance matrix of the measurement noise V, and R ═ E (VV)T)。
6. The method of claim 5, wherein the gain matrix K is updated according to the change of the system operation state, and the update equation is
P＝FPFT+Q
K＝PHT(HPH+R)-1
P＝P-KHP。
7. The method according to claim 1, characterized in that, in the initialization setting of the kalman filter bank, the number of the augmented state variables of the kalman filter is first determined according to the number of angle measurements in the received observation values; and then endowing the Kalman filter with a group of initial values of target information before the estimation work of the Kalman filter is started, and calculating the numerical value of a gain matrix K in the Kalman filter according to the initial values.
8. The method according to claim 4, wherein the angle measurement corresponding to the main lobe is not interfered by the fuzzy value, the residual Weighted Sum of Squares (WSSR) calculated by the corresponding Kalman filter is the smallest in magnitude, and the WSSR calculated by other Kalman filters is larger in magnitude than the calculation result corresponding to the main lobe, so that the angle measurement corresponding to the main lobe is distinguished.
9. The three-dimensional target tracking device of the ultra-sparse array radar under the grating lobe fuzzy condition is characterized by comprising the following components:
the equation building module is used for respectively building a radar state equation and a radar measurement equation of the ultra-sparse array radar under the grating lobe fuzzy condition;
the Kalman filter construction module is used for establishing a Kalman filter for distinguishing grating lobes and estimating a target state according to the radar state equation and the radar measurement equation;
an initialization module for initializing the Kalman filter;
the measurement information acquisition module is used for acquiring radar target measurement information of the ultra-sparse array radar at each moment;
and the target tracking module is used for tracking the target according to the initial setting value, the radar state equation and the radar measurement equation until the target tracking is finished.
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