CN113311398A - Tracking method for high maneuvering dim small target with strong clutter complex background - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/414—Discriminating targets with respect to background clutter
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/66—Radar-tracking systems; Analogous systems
- G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
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Abstract
The invention discloses a tracking method of a small high-maneuvering target with a strong clutter complex background, which has the advantages of high calculation speed, less calculation amount and high radar time resource utilization rate; the invention is realized by the following technical scheme: the radar signal processing unit detects a target signal by adopting three-path parallel processing on a received echo signal, then forms a target track through angle calculation and sends the target track to the radar data processing unit, and the track processing software carries out secondary processing on the received target track to form a target track; after the target track is successfully started, the target track enters a track fitting module in a sliding window mode, one-dimensional second-order fitting is carried out on target track information in a rectangular coordinate system, the target fitting module resolves the target maneuvering acceleration difference and then sends the target maneuvering acceleration difference to a track filtering module, the target motion parameters are resolved continuously in real time, the target motion state parameters at the next moment are estimated, iterative updating of a filter gain coefficient is completed, and stable tracking of a high maneuvering target with a strong clutter complex background is achieved in a self-adaptive mode.
Description
Technical Field
The invention relates to the technical field of radar data processing, and is suitable for real-time tracking of a high maneuvering target under a strong clutter background.
Background
The detection of high maneuvering weak and small targets is one of the key problems that radars face frequently and need to be solved urgently, such as fixed-wing unmanned planes, small multi-rotor unmanned planes and other aircrafts. The multi-rotor unmanned aerial vehicle is a typical low-slow target with low flying height, small radar reflection area and low flying speed; in the movement process, due to factors such as artificial control or random interference, the multi-rotor unmanned aerial vehicle has the characteristic of high maneuverability with fast speed, height and course change. Many rotor unmanned aerial vehicle low price, easily control, mobility are strong, owing to lack effective control, have leaded to "black flight" incident frequently, have caused very big hidden danger for public safety, become the difficult problem of city security to the supervision and the management and control of this kind of unmanned aerial vehicle. For the detection and tracking of such high-mobility small targets, radar is still the mainstream sensor of the current detection technology. Under the urban complex background with strong interference and high noise, the radar system has weak echo signal, low signal-to-noise ratio and small power range to the target of the unmanned aerial vehicle, so that the detection efficiency is reduced. Therefore, the real-time continuous and stable tracking of the high maneuvering weak and small targets with low detection probability becomes a difficult point in the technical field of radar tracking.
The most key problem of the radar in the tracking process of the high maneuvering target is the real-time estimation of the motion state of the target at the next moment by using target measurement information, and the method is essentially a problem of a parameter estimation algorithm. The radar tracking of the target is realized by acquiring measurement information of the target at the current moment by using a radar echo signal, such as: the method comprises the steps of measuring distance, pitching, direction, speed and the like, removing false information influenced by environmental noise and interfered by a non-cooperative target to the maximum extent by utilizing a measurement information and data association rule to obtain a real measured value of the target, establishing a motion model and a filtering system suitable for the motion characteristic of the target to calculate the motion characteristic parameters of the target and estimate the motion state of the target, forming a target track by utilizing a track association rule, and finally realizing the tracking of the target. The radar tracking of the high maneuvering target mainly comprises motion characteristic modeling of the target and design of a filter, wherein motion model modeling of the target describes the motion state of the target in a motion equation form, and filtering acquires a filtering value of the target state by weighting a target state estimation value and a state observation value by adopting a certain strategy. The target tracking performance evaluation indexes mainly comprise tracking precision, tracking calculated quantity (instantaneity), tracking stability (track service life) and the like. The detection and tracking of small and medium targets in a complex background are always important components of a monitoring and warning system, and in order to obtain more sufficient early warning time and have higher requirements on the tracking instantaneity of small and high-mobility targets, the problems of the complexity of a tracking method, the operation efficiency of a hardware carrying platform and the like are mainly concerned in the practical engineering implementation, and how to balance the tracking instantaneity, the stability and the tracking precision becomes a practical problem in engineering application. On the premise of ensuring the tracking accuracy, the tracking method with extremely strong real-time performance becomes one of the key problems facing radar data processing.
The motion models currently used for highly mobile objects are: singer model, Jerk model, "current" statistical (CS) model, interactive multi-model (IMM), etc. Singer model algorithm with target acceleration covariance of sigma2＝am/3(1+4pm-p0) Wherein a ism、pmAnd p0Respectively estimating the maximum acceleration of the target, the probability of the maximum acceleration and the probability when the acceleration is zero; this requires accurate estimation of target maximum acceleration, uniform acceleration, and maximum acceleration probabilities, limiting its applicability. The Jerk model algorithm is added to four dimensions on the basis of the target acceleration model, estimation has no time lag, but practical prior assumption needs to be made on the maneuvering characteristics of the target, and the applicability is limited. An interactive multi-model algorithm (IMM) is a more commonly applied algorithm, different levels are set for process noise, a filter is respectively established for each model, and the IMM filter has good estimation performance, but the calculation amount is large, so that the rapid tracking of a target cannot be met.
In order to improve the utilization rate of radar time resources, improve the real-time performance of target tracking and obtain more abundant early warning time, a target tracking method with less calculation amount, high applicability, strong stability and long duration is needed.
Disclosure of Invention
In order to overcome the problems, the invention aims to provide a target tracking method which has the advantages of high calculation speed, small calculated amount, high radar time resource utilization rate, good tracking real-time performance and strong stability, aiming at the characteristics of high maneuvering small targets and the defects of the prior art.
The technical scheme adopted by the invention is as follows: a tracking method for a strong clutter complex background high maneuvering small target is characterized in that: the radar signal processing unit carries out three-way parallel processing on received echoes and path signals, the first path adaptively selects a Fast Fourier Transform (FFT) or Finite Impulse Response (FIR) filter processing mode to complete target signal detection according to the residence time of radar beams, the second path carries out target signal detection by adopting a zero-speed filter plus clutter map method, the third path carries out target signal detection by adopting a Kalmus filter plus clutter map method, after the three-way parallel processing completes the target signal detection, a target point track containing information such as target distance, direction, pitching and the like is formed through angle calculation and is sent to the radar data processing unit, track processing software carries out secondary processing on the received target point track, and a target track is formed through point track aggregation, residual clutter map detection and related processing; after the target track is successfully started, track data enters a track fitting module in a sliding window mode, and one-dimensional second-order fitting is carried out on target track information with the length of a sliding window being n in a rectangular coordinate system; the flight path fitting module forms a matrix A according to the discrete serial number, a matrix B according to the target point path measurement value and a matrix beta according to the fitting coefficient, and a normal equation linear regression principle formula beta is utilized to obtain (A multiplied by A)T)-1×ATThe xAB is used for resolving a second-order fitting coefficient through one-time matrix operation to obtain a target maneuvering acceleration variance, then the target maneuvering acceleration variance is sent to a track filtering module, Kalman filtering of a current CS model is adopted, target movement parameters are resolved continuously in real time, target movement state parameters at the next moment are estimated, iterative updating of a filter gain coefficient is completed, and high maneuvering targets under a strong clutter complex background are adaptively achievedTarget stable tracking, where T denotes matrix transpose.
Compared with the existing tracking method, the method has the remarkable advantages that:
the tracking real-time performance is good and the stability is strong. The radar signal processing unit completes target signal detection on received echo signals in a three-way parallel processing mode, completes signal detection by a method of adaptively selecting FFT or FIR filtering according to radar beam residence time, and simultaneously detects targets near zero Doppler frequency by a method of adding clutter maps through a Kalmus filter and optimized zero-speed filter, thereby realizing fixed clutter suppression and signal accumulation. By the clutter suppression and enhancement filtering technology, clutter interference is effectively suppressed, the purpose of enhancing target signals is achieved, the problem of low-speed small target detection under a strong clutter background is solved, and searching and tracking of the target are more accurate. The invention adopts Kalman filtering of a Current (CS) model, continuously resolves target motion parameters in real time and estimates target motion state parameters at the next moment, completes iterative updating of a filter gain coefficient and completes target track updating in real time. The method has the advantages that the method can adaptively track the tracks of the high maneuvering targets with the strong clutter complex background, realizes the stable tracking of the high maneuvering small targets, and is good in tracking real-time performance and strong in stability.
The calculation amount is small, and the utilization rate of time resources is high. The target track is formed by performing secondary processing on the received target track, and performing point track condensation, residual clutter map detection and related processing; after the target track is successfully started, track data enter a track fitting module in a sliding window mode, one-dimensional second-order fitting is carried out on target track information with the length of a sliding window being n in a rectangular coordinate system, and a normalization formula of a normal equation linear regression principle is utilized, so that the target maneuvering acceleration variance can be obtained through one-time matrix operation. The processing mode utilizing the normal equation linear regression principle greatly reduces the calculated amount, saves the time resources of a radar system, improves the real-time performance of knowing and calculating the motion parameters of the target and the utilization rate of the radar time resources, obtains more abundant early warning time, has simple program, is easy to realize in engineering and has strong usability.
The method provided by the invention is not limited by a radar system and a radar data rate, is suitable for various hardware platforms and various track extrapolation filters, and can also be suitable for tracking non-maneuvering targets.
Drawings
FIG. 1 is a block diagram of the flow of the present invention for a high maneuvering weak small target with a complex background of strong clutter.
The invention is further described below with reference to the accompanying drawings.
Detailed Description
See fig. 1. According to the invention, a radar signal processing unit carries out three-way parallel processing on received echo and path signals, a first path adaptively selects a Fast Fourier Transform (FFT) or finite impulse response Filter (FIR) processing mode to complete target signal detection according to the residence time of radar beams, a second path adopts a zero-speed filter plus clutter map method to detect the target signals, a third path adopts a Kalmus filter plus clutter map method to detect the target signals, after the three-way parallel processing completes the target signal detection, a target track containing information such as target distance, direction, pitching and the like is formed by angle resolving and sent to a radar data processing unit, track processing software carries out secondary processing on the received target track, and the target track is formed by point track aggregation, residual clutter map detection and related processing; after the target track is successfully started, track data enters a track fitting module in a sliding window mode, one-dimensional second-order fitting is carried out on target track information with the length of n sliding windows in a rectangular coordinate system, the track fitting module forms a matrix A according to the discrete serial number, forms a matrix B according to the target track measured value and forms a matrix beta according to fitting coefficients, and the formula beta is changed into (A multiplied by A) according to the principle of normal equation linear regressionT)-1×ATAnd the xAB is used for resolving a second-order fitting parameter through one-time matrix operation to obtain a target maneuvering acceleration variance, then the target maneuvering acceleration variance is sent to a track filtering module, the current CS model Kalman filtering is adopted, target movement parameters are resolved continuously in real time, target movement state parameters at the next moment are estimated, iterative updating of a filter gain coefficient is completed, stable tracking of a high maneuvering target under a strong clutter complex background is realized in a self-adaptive manner, and T represents matrix transposition.
The flight path fitting module utilizes a normalization formula beta as (A multiplied by A) according to a matrix A formed by discrete serial numbers, a matrix B formed by target point path measurement values and a matrix beta formed by fitting coefficientsT)-1×ATAnd solving the coefficient a of the second-order fitting equation by the multiplied by B, wherein the target current acceleration variance is 2a and is sent to the track filtering module.
Wherein, the matrix A formed by the discrete serial numbers is:
wherein n is the length of the sliding window.
The matrix B formed by the target trace measurement values is as follows:
where y is the target trace measurement.
The matrix beta formed by the fitting coefficients is:
wherein a is a second-order fitting coefficient, b is a first-order fitting coefficient, and c is a fitting constant.
And a track filtering module in the radar data processing unit adopts a Kalman filtering algorithm of a Current Statistical (CS) model, continuously solves target motion parameters in real time by using the received target maneuvering acceleration variance 2a and the target track information of the current period, estimates target motion state parameters at the next moment, completes iterative updating of a filter gain coefficient and realizes track filtering smooth extrapolation.
The track fitting module selects sliding window length n to be 5 or n to be 8 aiming at the multi-rotor unmanned aerial vehicle under the city strong clutter background, and the target is filtered and tracked in real time, and finally the target is stably tracked in real time.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof. Within.
Claims (9)
1. A tracking method for a strong clutter complex background high maneuvering small target is characterized in that: the radar signal processing unit carries out three-way parallel processing on received echoes and path signals, the first path adaptively selects a Fast Fourier Transform (FFT) or Finite Impulse Response (FIR) filter processing mode to complete target signal detection according to the residence time of radar beams, the second path carries out target signal detection by adopting a zero-speed filter plus clutter map method, the third path carries out target signal detection by adopting a Kalmus filter plus clutter map method, after the three-way parallel processing completes the target signal detection, a target point track containing information such as target distance, direction, pitching and the like is formed through angle calculation and is sent to the radar data processing unit, track processing software carries out secondary processing on the received target point track, and a target track is formed through point track aggregation, residual clutter map detection and related processing; after the target track is successfully started, track data enters a track fitting module in a sliding window mode, one-dimensional second-order fitting is carried out on the target track with the length of n sliding windows in a rectangular coordinate system, the track fitting module forms a matrix A according to the discrete serial number, forms a matrix B according to the target track measured value and forms a matrix beta according to fitting coefficients, and the formula beta is changed into (A multiplied by A) according to the principle of normal equation linear regressionT)-1×ATThe xAB is used for resolving a second-order fitting coefficient through one-time matrix operation to obtain a target maneuvering acceleration variance, then the target maneuvering acceleration variance is sent to a track filtering module, Kalman filtering of a current CS model is adopted, target movement parameters are resolved continuously in real time, target movement state parameters at the next moment are estimated, iterative updating of a filter gain coefficient is completed, and high maneuvering targets under a strong clutter complex background are adaptively achievedWherein T denotes a matrix transpose.
2. The method for tracking a strongly clutter complex background high maneuvering small target according to claim 1, characterized by: and the radar data processing unit forms a target navigation through point trace condensation, residual clutter map detection, correlation processing, filtering and smooth extrapolation in secondary processing of the received target point trace.
3. The method for tracking a strongly clutter complex background high maneuvering small target according to claim 2, characterized by: after the target track is successfully started, the radar data processing unit sends the target track to a track fitting module in a sliding window mode, and the track fitting module sends target track information with the length of a sliding window n to an equation Y (aX) under a rectangular coordinate system through data processing software2+ bX + c to perform second-order fitting; wherein, Y is the target trace measurement value, a is the second order fitting coefficient, b is the first order fitting coefficient, c is the fitting constant, and X is the discrete serial number.
4. The method for tracking the small high-maneuvering target with the strong clutter complex background as claimed in claim 3, characterized in that: and the track fitting module is used for solving a fitting coefficient a of a second-order fitting equation by utilizing a normalization principle of a normal equation according to a matrix A formed by the discrete serial numbers, a matrix B formed by the target track measurement values and a matrix beta formed by the fitting coefficients, so that the target maneuvering acceleration variance is 2a and is sent to the track filtering module.
5. The method for tracking a strongly clutter complex background high maneuvering small target according to claim 4, characterized by: the matrix A formed by the discrete serial numbers is as follows:
wherein n is the length of the sliding window.
6. The method for tracking a strongly clutter complex background high maneuvering small target according to claim 4, characterized by: a matrix B formed by the target trace measurement values is;
where y is the target trace measurement.
7. The method for tracking a strongly clutter complex background high maneuvering small target according to claim 4, characterized by: the fitting coefficient forming matrix beta is:
wherein a is a second-order fitting coefficient, b is a first-order fitting coefficient, and c is a fitting constant.
8. The method for tracking a strongly clutter complex background high maneuvering small target according to claim 1, characterized by: and a track filtering module in the radar data processing unit adopts a Kalman filtering algorithm of a Current Statistical (CS) model, continuously solves target motion parameters in real time by using the received target maneuvering acceleration variance 2a and the target track information at the current moment, estimates target motion state parameters at the next moment, finishes iterative updating of a filter gain coefficient and realizes track filtering smooth extrapolation.
9. The method for tracking a strongly clutter complex background high maneuvering small target according to claim 1, characterized by: the track fitting module selects the sliding window length n to be 5 or n to be 8 aiming at the multi-rotor unmanned aerial vehicle under the city strong clutter background, completes the filtering extrapolation of the target track, and finally realizes the real-time stable tracking of the target.
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