CN112612064A - Method for detecting and tracking infrared dynamic flying target on space basis - Google Patents
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Abstract
The invention aims to provide a space-based method for detecting and tracking an infrared dynamic flying target, which can meet the positioning requirement of a low-frequency dynamic radiation source target and ensure the dynamic capture and tracking of the target. The method comprises the following steps: step S1: calculating the infrared characteristic of the target; step S2: selecting a detection mode; step S3: determining a detection spectrum; step S4: analyzing and determining the signal to noise ratio of the image to be obtained; step S5: determining a space-based infrared detection load index; step S6: and accumulating and tracking the motion situation of the target. The beneficial effects are that: a set of method is formed by using feature description, load matching, frequency spectrum screening, data accumulation of target motion and the like, and the detection capability of the target can be effectively improved.
Description
Technical Field
The invention belongs to a method for detecting an aerial infrared low-radiation-source target, in particular relates to a method for detecting and tracking an infrared dynamic flying target on a space basis, and is particularly suitable for accurately detecting and tracking an aerial dynamic infrared radiation source target in an accumulation manner by detecting an aerial vehicle and a target motion situation through an infrared system.
Background
At present, a relatively complete space-ground integrated early warning system is formed abroad, and the system has certain detection capability on aerial targets. Particularly, the latest space-based infrared low-radiation-source target system in the United states comprises a space base and a foundation, wherein the space base mainly comprises an SBIRS high-orbit infrared missile detection early warning system and a future medium-low orbit infrared system, and the foundation is mainly realized by early warning radars deployed in various parts of the world and on the sea surface.
The American land/sea-based air target early warning system is mainly applied to determining threats of incoming airplanes and missiles, providing corresponding flight missile path data for intercepting the missiles, and realizing global detection by cooperating with a space-based system. The current system mainly comprises two devices of an early warning radar and a tracking guidance radar, the detection wave band covers X, L, P and other wave bands, and the system has multilayer reverse guidance capability in most areas with north latitude of 30 degrees.
The "Space tracking and monitoring System" (STSS) built in the United states can provide end-to-end infrared tracking over the entire flight of a missile. Each satellite carries 2 detectors, one is a capture detector, and the other is a wide-field scanning short-wave infrared detector which can be used for observing missile tail flames in a boosting stage. Once the search probe has locked on to a target, the information is passed to another probe, the tracking probe. The latter is a narrow-field, high-precision staring multi-spectral band (medium wave, medium and long wave, long wave infrared and visible light) detector, which can lock a target and track the whole trajectory middle section and reentry stage. The capture detector is used to search, detect and track the missile in the boosting section. The tracking sensor receives information from the capture sensor and continues to track missiles at the mid-and re-entry segments with greater accuracy. The processing system on the satellite and on the ground can predict the final missile trajectory and the landing point of the warhead. This data is transmitted to the missile army for intercepting the incoming missile or warhead.
The design target of the system is missile with obvious infrared characteristic, but the detection capability of fighters, transporters, early warning machines, anti-submergence vehicles and the like flying in the atmosphere is very limited. The reason is mainly divided into three aspects:
firstly, in the aspect of a detection system, cameras used for detection are mainly linear array scanning cameras and small area array cameras, and large area array staring cameras are lacked, so that the aircraft targets are difficult to track and capture.
Secondly, in the aspect of detecting spectral bands, the current camera spectral bands are detected by bicolor infrared bands (2.7 μm and 4.3 μm), the airplane detection capability of the atmosphere with less obvious infrared characteristics is limited, and the development to the comprehensive detection direction of multi-spectral bands of medium-wave infrared, long-wave infrared and visible light is required gradually.
Thirdly, in the aspect of application capacity, the tracking and handover capacity, weapon hinging capacity and low-altitude tactical target detection capacity of the space-based system platform to the target are not mature, and when the infrared characteristics of the target are not obvious and the speed is high, particularly under the conditions of complex battlefield environment, complex weather and a large amount of background target characteristic data, the dynamic target is difficult to distinguish and track.
Disclosure of Invention
The invention aims to provide a space-based method for detecting and tracking an infrared dynamic flying target, which can meet the positioning requirement of a low-frequency dynamic radiation source target and ensure the dynamic capture and tracking of the target.
The technical scheme of the invention is as follows: a method for detecting and tracking an infrared dynamic flying target based on space comprises the following steps:
step S1: calculating the infrared characteristic of the target;
step S2: selecting a detection mode;
step S3: determining a detection spectrum;
step S4: analyzing and determining the signal to noise ratio of the image to be obtained;
step S5: determining a space-based infrared detection load index;
step S6: and accumulating and tracking the motion situation of the target.
The step S1 includes that the target infrared characteristics include solar radiation energy reflected by the target, radiation energy reflected by the target on the earth, and radiation energy of the target itself;
the method comprises the following steps of calculating a target transient temperature field by combining a target internal heat source according to the space position and posture of a target, the illumination condition and sparse external heat source irradiation angle, and calculating the self radiation energy of the target by using the emission characteristic of a black body target;
under the default state of the bidirectional reflection distribution function of the target, the lambert reflection model is utilized to calculate the reflection characteristic of the target, calculate the reflectivity of the surface of the target, and calculate the solar radiation energy reflected by the target and the radiation energy reflected by the earth by the target.
The step S2 includes the steps of,
when the satellite, the target and the earth surface are in the same straight line, earth detection is adopted, and environmental factors influencing the detection comprise: radiation from the earth and the atmosphere generated vertically upwards; transmittance in the vertical direction of the atmosphere;
when the satellite, the target and the earth surface are not in a straight line, the method adopts adjacent edge detection, and the environmental factors influencing the detection comprise: radiation generated by the atmosphere along the tangential direction of the earth surface; atmospheric permeability in the tangential direction to the earth's surface.
The step S4 includes the steps of,
step S41: constraints on image signal-to-noise ratio
In single-frame detection, a thermal infrared point target detection system is regarded as a narrow-band system, and the false alarm rate P is determined according to a binary signal detection theoryfAnd probability of detection PdSatisfy the following relationship
Where erfc is the complementary error function, SNRTRepresenting the image signal-to-noise ratio;
in multi-frame detection, the multi-frame detection probability using the M-to-N criterion is as follows:
wherein p isiIs the probability that the target falls into the associated wave gate during the detection of the ith frame, p in the simulationiNumerical value 1, CM iIs a coefficient, Pd (1)Is the probability of detection of the first frame;
the expression of the multi-frame false alarm rate of the M-to-N criterion is as follows:
wherein
Is the multi-frame false alarm rate, N, of a single detection unitPThe expression is the following for the number of frames:
wherein the content of the first and second substances,
the single frame threshold crossing rate can be equivalent to a single frame false alarm rate, and S is the number of pixels in the associated wave gate;
according to the star-eye relative movement velocity v, the detection distance L, the instrument focal length F and the pixel size AdDetermining the pixel number S of the associated wave gate by the detection frequency f;
calculating the relation between the target detection probability and the false alarm rate under the condition of different image signal-to-noise ratio requirements by combining with the detection criterion constraint;
step S42: determining the signal-to-noise ratio of the image to be acquired
Calculating image signal-to-noise ratio SNRT，
In the formula, a1As a concentration of energy, a2(s) is the influence over the pixels, asFor scan loss, JTIs the target radiation intensity;
the image signal-to-noise ratio can be expressed as: sigmacResidual standard deviation for background suppression, given SNRTThen, the equivalent noise radiation intensity J can be obtained from the above formulanComprises the following steps:
further, the conversion relation between the statistical camera signal-to-noise ratio and the image signal-to-noise ratio can be obtained as follows:
the conversion relation between the peak value camera signal-to-noise ratio and the camera signal-to-noise ratio is obtained as follows:
the step S4 includes the steps of,
the relationship between the peak camera signal-to-noise ratio and each relevant parameter is as follows:
wherein SNR ismaxFor peak camera signal-to-noise ratio, JTIs the intensity of the target radiation, tauaIs the atmospheric transmittance, tau0For optical system transmittance, δ is the loading signal process factor, D is the camera entrance pupil diameter (the higher the value, the better the system performance), and Δ f is the detector bandwidth.
The invention has the beneficial effects that: due to the existence of the short plate of the space-based detection system, the detection capability of the existing detection load on the infrared signal target with unobvious characteristics and short signal existence time is insufficient, the tracking and handover capability, the system hinge capability and the low-altitude tactical target detection capability of a space-based system platform on the target are immature, and particularly under the conditions of complex battlefield environment and a large amount of background target characteristic data, the target is difficult to distinguish and track. The invention provides a detection method for an aerial dynamic infrared target on the basis of analysis and modeling of the characteristics of the detected target, and a set of method is formed by using feature description, load matching, frequency spectrum screening, data accumulation of target motion and the like, so that the detection capability of the target can be effectively improved.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
The invention provides a method for detecting and tracking an infrared dynamic flying target based on a passive seeker resolving method, which comprises the following steps:
step S1: target infrared characteristic calculation
The target infrared characteristics include the solar radiant energy reflected by the target, the radiant energy reflected by the target on the earth, and the radiant energy of the target itself.
The method comprises the steps of calculating a target transient temperature field by combining a target internal heat source according to the space position, the posture, the illumination condition and sparse external heat source irradiation angle of a target, and calculating the self radiation energy of the target by using the emission characteristic of a blackbody target.
In a Bidirectional Reflectance Distribution Function (BRDF) default state of the target, the reflection characteristics of the target are calculated by a Lambert reflection model, the reflectivity of the surface of the target is calculated, and the solar radiation energy reflected by the target and the radiation energy reflected by the earth by the target are calculated.
Step S2: selecting a probing mode
According to the space geometric position of the spacecraft and the detected target, two modes of ground detection and edge detection are adopted.
When the satellite, the target and the earth surface are in the same straight line, earth detection is adopted, and the environmental factors influencing the detection mainly comprise: radiation from the earth and the atmosphere generated vertically upwards; transmittance in the vertical direction of the atmosphere.
And when the satellite, the target and the earth surface are not in a straight line, adopting adjacent edge detection. During the edge detection, the environmental factors influencing the detection mainly include: radiation generated by the atmosphere along the tangential direction of the earth surface; atmospheric permeability in the tangential direction to the earth's surface.
Step S3: determining a detection spectral range
And comprehensively analyzing and determining the detection spectral band by combining the atmospheric characteristics. The determination principle is as follows: the attenuation of the light wave through the atmosphere is related to the contents of atmospheric components and the respective gas components encountered by the light wave in its transmission path.
Among them, atmospheric components affect radiation and transmittance. The content of each gas component is influenced by various factors, including geographical location, season, air temperature, altitude of the observed object, and the like.
In this example, table 1 is selected to determine the detection spectrum by combining the spectrum designed for the atmospheric transmission window and the existing detection in wide application.
TABLE 1 selection of simulated spectral bands for atmospheric radiance and transmittance
Step S4: analyzing and determining the signal-to-noise ratio of the image to be acquired
The imaging quality of the space-based infrared detection system depends on the signal-to-noise ratio of the image to be obtained.
Step S41: constraints on image signal-to-noise ratio
In single-frame detection, a thermal infrared point target detection system is regarded as a narrow-band system, and the false alarm rate P is determined according to a binary signal detection theoryfAnd probability of detection PdSatisfy the following relationship
Where erfc is the complementary error function, SNRTRepresenting the image signal-to-noise ratio.
In multi-frame detection, the multi-frame detection probability using the M-to-N criterion is as follows:
wherein p isiIs the probability that the target falls into the associated wave gate during the detection of the ith frame, p in the simulationiNumerical value 1, CM iIs a coefficient, Pd (1)Is the probability of detection of the first frame.
The expression of the multi-frame false alarm rate of the M-to-N criterion is as follows:
wherein
Is the multi-frame false alarm rate, N, of a single detection unitPThe expression is the following for the number of frames:
wherein the content of the first and second substances,
the single frame threshold crossing rate can be equivalent to a single frame false alarm rate, and S is the number of pixels in the associated wave gate.
According to the star-eye relative movement velocity v, the detection distance L, the instrument focal length F and the pixel size AdAnd determining the pixel number S of the associated wave gate by the detection frequency f, wherein the relation is as shown in the formula (5).
And calculating the relation between the target detection probability and the false alarm rate under the condition of different image signal-to-noise ratio requirements by combining with the detection criterion constraint (N frames are selected from M frames).
And (4) according to a formula (5), taking the detection distance, the star-eye relative motion speed and the detection frequency in the mission planning as input, and combining load indexes such as focal length, pixel size and the like to provide the image signal-to-noise ratio requirement through detection probability and false alarm rate analysis.
Step S42: determining the signal-to-noise ratio of the image to be acquired
Wherein the SNRTThe expression of image signal-to-noise ratio is the core index for measuring the detection capability, and in application, the SNR is usedTThe value is used as an index requirement of the detection capability of the system. The calculation method is shown as formula (6), and the calculation model is as follows: taking into account the energy concentration a1Cross pixel influence a2(s) and scan loss asWhen, JTFor the target radiation intensity, the image signal-to-noise ratio can be expressed as:
in the formula, σcResidual standard deviation for background suppression, given SNRTThen, the equivalent noise radiation intensity J can be obtained from the above formulanComprises the following steps:
further, the conversion relation between the statistical camera signal-to-noise ratio and the image signal-to-noise ratio can be obtained as follows:
the scaling relationship between the peak camera signal-to-noise ratio and the camera signal-to-noise ratio obtained by the same method is as follows:
step S5: determining space-based infrared detection load index
The determination of space-based infrared detection load indexes is more limited by the bearing capacity and the carrying and launching capacity of a spacecraft platform, and the space-based infrared detection load mainly depends on the value range of each relevant parameter influencing the signal-to-noise ratio of a camera on the premise of determining the signal-to-noise ratio of an image to be obtained.
The relationship between the peak camera signal-to-noise ratio and each relevant parameter is as follows:
wherein SNR ismaxFor peak camera signal-to-noise ratio, JTIs the intensity of the target radiation, tauaIs the atmospheric transmittance, tau0For optical system transmittance, δ is the loading signal process factor, D is the camera entrance pupil diameter (the higher the value, the better the system performance), and Δ f is the detector bandwidth.
Step S6: accumulation and tracking of target motion situation
In order to complete the multi-target continuous observation, the method is realized by accumulating and tracking the target motion situation, and comprises the following specific implementation steps:
step S61: modeling accumulation and tracking of the target motion situation;
and correspondingly defining nodes and connecting lines in the target motion situation based on a graph theory, and solving the model by using the graph theory so as to obtain an optimal solution. The nodes represent observation targets, connecting lines between the nodes represent conversion processes between different observation targets, and the weight of the connecting lines can be the cost of the conversion.
Step S62: target infrared point source positioning information on-satellite processing
After the satellite detects the target, it needs to be positioned. In the embodiment, the space of the target is positioned by using the double-star angle measurement, the position information of the target in the inertial space is obtained according to the shooting angle information of the double stars, the position information of the inertial space is converted into the geocentric inertial coordinate, and the space position of the target in the geocentric inertial system can be obtained under the condition that the satellite orbit position and the angle measurement information are known.
Step S63: target motion trajectory fitting based on discrete points
And obtaining a plurality of discrete target infrared point source position information through on-satellite processing.
Determining effective discrete point target positioning information according to the motion direction and speed of the target to be described, and resolving and determining effective discrete positioning points according to the target flight speed and the satellite positioning precision.
Discrete points are then selected that determine the effective location, which are then fitted to form the motion trajectory of the target. The curve fitting method based on discrete points can adopt least squares, spline curves and the like
Step S64: detection of moving direction and speed
And after the target motion track fitting based on the discrete points is completed, estimating the course and the navigational speed of the target according to the target position difference of the front effective discrete point and the rear effective discrete point.
Claims (5)
1. A method for detecting and tracking an infrared dynamic flying target based on space is characterized by comprising the following steps:
step S1: calculating the infrared characteristic of the target;
step S2: selecting a detection mode;
step S3: determining a detection spectrum;
step S4: analyzing and determining the signal to noise ratio of the image to be obtained;
step S5: determining a space-based infrared detection load index;
step S6: and accumulating and tracking the motion situation of the target.
2. The method for space-based detection and tracking of infrared dynamic flying targets of claim 1, wherein: the step S1 includes that the target infrared characteristics include solar radiation energy reflected by the target, radiation energy reflected by the target on the earth, and radiation energy of the target itself;
the method comprises the following steps of calculating a target transient temperature field by combining a target internal heat source according to the space position and posture of a target, the illumination condition and sparse external heat source irradiation angle, and calculating the self radiation energy of the target by using the emission characteristic of a black body target;
under the default state of the bidirectional reflection distribution function of the target, the lambert reflection model is utilized to calculate the reflection characteristic of the target, calculate the reflectivity of the surface of the target, and calculate the solar radiation energy reflected by the target and the radiation energy reflected by the earth by the target.
3. The method for space-based detection and tracking of infrared dynamic flying targets of claim 1, wherein: the step S2 includes the steps of,
when the satellite, the target and the earth surface are in the same straight line, earth detection is adopted, and environmental factors influencing the detection comprise: radiation from the earth and the atmosphere generated vertically upwards; transmittance in the vertical direction of the atmosphere;
when the satellite, the target and the earth surface are not in a straight line, the method adopts adjacent edge detection, and the environmental factors influencing the detection comprise: radiation generated by the atmosphere along the tangential direction of the earth surface; atmospheric permeability in the tangential direction to the earth's surface.
4. The method for space-based detection and tracking of infrared dynamic flying targets of claim 1, wherein: the step S4 includes the steps of,
step S41: constraints on image signal-to-noise ratio
In single-frame detection, a thermal infrared point target detection system is regarded as a narrow-band system, and the false alarm rate P is determined according to a binary signal detection theoryfAnd probability of detection PdSatisfy the following relationship
Where erfc is the complementary error function, SNRTRepresenting the image signal-to-noise ratio;
in multi-frame detection, the multi-frame detection probability using the M-to-N criterion is as follows:
wherein p isiIs the probability that the target falls into the associated wave gate during the detection of the ith frame, p in the simulationiNumerical value 1, CM iIs a coefficient, Pd (1)Is the probability of detection of the first frame;
the expression of the multi-frame false alarm rate of the M-to-N criterion is as follows:
wherein
Is the multi-frame false alarm rate, N, of a single detection unitPThe expression is the following for the number of frames:
wherein the content of the first and second substances,
the single frame threshold crossing rate can be equivalent to a single frame false alarm rate, and S is the number of pixels in the associated wave gate;
according to the relative movement velocity v of the star and the eye, the detection distance L, the focal length F of the instrument and the pixel size AdDetermining the pixel number S of the associated wave gate by the detection frequency f;
calculating the relation between the target detection probability and the false alarm rate under the condition of different image signal-to-noise ratio requirements by combining with the detection criterion constraint;
step S42: determining the signal-to-noise ratio of the image to be acquired
Calculating image signal-to-noise ratio SNRT，
In the formula, a1As a concentration of energy, a2(s)For influence across pixels, asFor scan loss, JTIs the target radiation intensity;
the image signal-to-noise ratio can be expressed as: sigmacResidual standard deviation for background suppression, given SNRTThen, the equivalent noise radiation intensity J can be obtained from the above formulanComprises the following steps:
further, the conversion relation between the statistical camera signal-to-noise ratio and the image signal-to-noise ratio can be obtained as follows:
the conversion relation between the peak value camera signal-to-noise ratio and the camera signal-to-noise ratio is obtained as follows:
5. the method for space-based detection and tracking of infrared dynamic flying targets of claim 1, wherein: the step S4 includes the steps of,
the relationship between the peak camera signal-to-noise ratio and each relevant parameter is as follows:
wherein SNR ismaxFor peak camera signal-to-noise ratio, JTIs the intensity of the target radiation, tauaIs the atmospheric transmittance, tau0For optical system transmittance, δ is the loading signal process factor, D is the camera entrance pupil diameter (the higher the value, the better the system performance), and Δ f is the detector bandwidth.
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| CN115952646B (en) * | 2022-11-25 | 2024-01-16 | 中国科学院微小卫星创新研究院 | Satellite dynamic capturing capability evaluation system and method for precision chain and time chain |
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