A cognitive multi-target TAS tracking time-energy resource optimization method

Abstract

The present application relates to a kind of cognitive multi-target TAS tracking time energy resource optimization method, belong to the technical field of multi-functional phased array radar cognitive multi-target tracking.It includes by adjusting target accumulation point number to carry out adaptive scheduling and optimization;Specifically, target detection signal-to-noise ratio threshold is set based on target tracking accuracy requirement;Target accumulation point number can be halved times are calculated based on target detection signal-to-noise ratio threshold;Target dwell time is calculated based on target accumulation point number can be halved.Through using the multi-target tracking time energy resource scheduling method based on TAS system proposed in the present application, under the condition of guaranteeing target tracking accuracy, according to the detection signal-to-noise ratio of target location, dynamically adjust tracking parameter, reduce target tracking dwell time, thereby save target tracking time energy resource, have strong engineering application value.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of multi-target tracking of multi-function phased array radar, and particularly relates to a multi-target tracking time-energy resource scheduling method based on a TAS system. BACKGROUND

[0002] Time-energy resource management is an important content of multi-function radar system design. When radars work in the same environment, the detection performance of the system can be quite different by adopting different time-energy scheduling strategies.

[0003] In the traditional TAS tracking mode, in order to ensure tracking accuracy, fixed pulse width and accumulation point number and other tracking parameters are designed according to the minimum target reflection cross-sectional area and the maximum tracking distance during target tracking. Although this design ensures the signal-to-noise ratio of tracking, it can cause time-energy resource waste due to the large target reflection cross-sectional area and the distance lower than the maximum tracking distance, and has great potential to be tapped. SUMMARY

[0004] The technical problem to be solved by the application is:

[0005] In view of the low time-energy resource utilization rate of the current radar multi-target TSA tracking mode, the application provides a cognitive multi-target TSA tracking time-energy resource optimization method.

[0006] To solve the above technical problems, the application adopts the following technical scheme:

[0007] A cognitive multi-target TSA tracking time-energy resource optimization method, characterized by adjusting target accumulation point number for adaptive scheduling and optimization, and comprising:

[0008] Setting a target detection signal-to-noise ratio threshold based on target tracking accuracy requirements;

[0009] Calculating the number of times of halving the target accumulation point number based on the target detection signal-to-noise ratio threshold;

[0010] Calculating the target residence time based on the number of times of halving the target accumulation point number.

[0011] The further technical scheme of the application is that the target detection signal-to-noise ratio threshold is set based on target tracking accuracy requirements, and specifically:

[0012] An angle measurement accuracy versus SNR curve graph is obtained, wherein the horizontal coordinate of the angle measurement accuracy versus SNR curve graph is signal-to-noise ratio, and the vertical coordinate is angle measurement accuracy;

[0013] Based on target tracking accuracy requirements, the signal-to-noise ratio corresponding to the target tracking accuracy requirements is found on the angle measurement accuracy versus SNR curve graph.

[0014] The further technical solution of the present application is: based on the target tracking accuracy requirement, the signal-to-noise ratio corresponding to the target tracking accuracy requirement is found on the curve graph of the angle measurement progress changing with the SNR, and specifically:

[0015] The target tracking accuracy requirement includes azimuth angle measurement accuracy and elevation angle measurement accuracy.

[0016] The signal-to-noise ratio corresponding to the azimuth angle measurement accuracy is found on the curve graph of the angle measurement progress changing with the SNR.

[0017] The signal-to-noise ratio corresponding to the elevation angle measurement accuracy is found on the curve graph of the angle measurement progress changing with the SNR.

[0018] The signal-to-noise ratio corresponding to the azimuth angle measurement accuracy is compared with the signal-to-noise ratio corresponding to the elevation angle measurement accuracy, and the maximum value of the two is taken.

[0019] The further technical solution of the present application is: based on the target detection signal-to-noise ratio threshold, the number of times that the target accumulation point number can be halved is calculated, and specifically:

[0020] The signal-to-noise ratio margin in the target tracking process is calculated based on the target detection signal-to-noise ratio threshold.

[0021] The number of times that the target accumulation point number can be halved is determined based on the signal-to-noise ratio margin in the target tracking process.

[0022] The further technical solution of the present application is: based on the signal-to-noise ratio margin in the target tracking process, the number of times that the target accumulation point number can be halved is determined, and specifically:

[0023] The number of times that the target accumulation point number can be halved is the signal-to-noise ratio margin in the target tracking process divided by pw2db(n), wherein n is the target quantity.

[0024] The further technical solution of the present application is: based on the number of times that the target accumulation point number can be halved, the target residence time is calculated, and specifically:

[0025]

[0026] Wherein, n t is the number of times that the adjustable accumulation point number M is halved.

[0027] The further technical solution of the present application is: it also includes adaptive scheduling and optimization by adjusting the waveform, PRI or tracking interval.

[0028] A computer system, characterized in that it comprises: one or more processors, a computer readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0029] A computer readable storage medium storing computer executable instructions that, when executed, implement the method described above.

[0030] A computer program product comprising computer executable instructions that, when executed, implement the method described above.

[0031] The present application has the following advantages:

[0032] The present application provides a cognitive multi-target TAS tracking time energy resource optimization method, which is based on target distance, speed, acceleration, and reflection cross-sectional area information, and adaptively optimizes target tracking residence time, thereby saving radar tracking time energy resources.

[0033] By adopting the multi-target tracking time energy resource scheduling method based on the TAS system, the tracking parameters can be dynamically adjusted according to the detection signal-to-noise ratio of the target position under the condition of ensuring the target tracking accuracy, the target tracking residence time is reduced, the target tracking time energy resources are saved, and the method has strong engineering application value. BRIEF DESCRIPTION OF DRAWINGS

[0034] The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:

[0035] Figure 1 is a schematic diagram of the relationship between the angle measurement accuracy and the detection signal-to-noise ratio according to the present application.

[0036] Figure 2 is a schematic diagram of the relationship between the detection signal-to-noise ratio and the tracking distance when ensuring tracking accuracy according to the present application.

[0037] Figure 3 is a schematic diagram of the relationship between the detection signal-to-noise ratio and the target reflection cross-sectional area when ensuring tracking accuracy according to the present application.

[0038] Figure 4 is a schematic diagram of the relationship between the number of accumulated points and the tracking distance when ensuring tracking accuracy according to the present application.

[0039] Figure 5 is a schematic diagram of the relationship between the number of accumulated points and the target reflection cross-sectional area when ensuring tracking accuracy according to the present application.

[0040] Figure 6 is a schematic diagram of the relationship between the target tracking residence time and the tracking distance after using the present application.

[0041] Figure 7is a schematic diagram of a target tracking residence time and target reflection cross-sectional area change relationship curve after using the present application.

[0042] Figure 8 is a flow chart of the method of the present application. DETAILED DESCRIPTION

[0043] In order to make the objects, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0044] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged as appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0045] In the conventional design method, the tracking parameters are designed according to the fixed mode and the maximum tracking distance condition, and the time and energy resources of the radar for target tracking are irrelevant to the distance, speed and reflection cross-sectional area of the target.

[0046] Since the radar can obtain information such as target distance, speed, acceleration, target type and target reflection cross-sectional area during operation, the tracking parameters (waveform, PRI, accumulation point number, tracking interval, etc.) can be adaptively scheduled and optimized according to the combat task requirements during target tracking.

[0047] In the present application, according to the tracking radar equation

[0048]

[0049] In the formula, R is the maximum tracking distance of the target, P is the average transmission power, A is the transmission antenna gain, A is the reception antenna gain, σ is the radar scattering cross-sectional area of the target, t is the target tracking residence time, and f is the target tracking frequency. max In the formula, R is the maximum tracking distance of the target, P is the average transmission power, A is the transmission antenna gain, A is the reception antenna gain, σ is the radar scattering cross-sectional area of the target, t is the target tracking residence time, and f is the target tracking frequency. av In the formula, R is the maximum tracking distance of the target, P is the average transmission power, A is the transmission antenna gain, A is the reception antenna gain, σ is the radar scattering cross-sectional area of the target, t is the target tracking residence time, and f is the target tracking frequency. t In the formula, R is the maximum tracking distance of the target, P is the average transmission power, A is the transmission antenna gain, A is the reception antenna gain, σ is the radar scattering cross-sectional area of the target, t is the target tracking residence time, and f is the target tracking frequency. r In the formula, R is the maximum tracking distance of the target, P is the average transmission power, A is the transmission antenna gain, A is the reception antenna gain, σ is the radar scattering cross-sectional area of the target, t is the target tracking residence time, and f is the target tracking frequency. rFor target tracking dwell time, λ is working wavelength, k is Boltzmann constant, T0 is standard room temperature, F n is noise factor, D0(1) is detection factor, C B is bandwidth correction factor, L is loss coefficient.

[0050] Assuming that in target tracking dwell time t r , the accumulated point number at different distance positions is M, and the pulse repetition period is T, i.e. t r =∑MT, so formula (1) can be expressed as:

[0051]

[0052] Where R is target tracking distance, SNR r is detection signal-to-noise ratio threshold value when target tracking accuracy is ensured, which is related to radar task mode.

[0053] Assuming that the radar works in TAS mode, the detection signal-to-noise ratio threshold value that meets target tracking accuracy requirement is SNR r . When single target tracking, it is considered that target reflection cross-sectional area σ is constant, so target tracking dwell time t r is proportional to tracking distance R 4 ; when multiple targets at the same distance position are tracked, target tracking dwell time t r is inversely proportional to target reflection cross-sectional area σ; when multiple targets at different distance positions are tracked simultaneously, tracking dwell time needs to be further analyzed in combination with actual situation.

[0054] Since target tracking dwell time t r is proportional to accumulated point number M and tracking interval T, so for single target and simultaneous multiple target tracking, target tracking dwell time can be optimized by dynamically adjusting accumulated point number and tracking interval in tracking process.

[0055] Considering signal-to-noise ratio attenuation caused by tracking distance or target RCS in target tracking process:

[0056] SNR1=-40*log10(R) (3)

[0057] SNR2=10*log10(σ) (4)

[0058] Where SNR1 is signal-to-noise ratio attenuation caused by tracking distance, and SNR2 is signal-to-noise ratio attenuation caused by target RCS.

[0059] Under the premise of ensuring target tracking accuracy, the signal-to-noise ratio margin in target tracking process can be expressed as:

[0060] ΔSNR1=-40*log10(R)-SNR r(5)

[0061] ΔSNR2 = 10*log10(σ)-SNR r (6)

[0062] Wherein, ΔSNR1 is the signal-to-noise ratio margin generated with tracking distance, and ΔSNR2 is the signal-to-noise ratio margin generated with target RCS.

[0063] In combination with the above analysis, it is assumed that in the target tracking process, based on different signal-to-noise ratio margins, the target tracking residence time designed by the method can adjust the halving frequency of the accumulated point number M to n t (n t =0,1,2,3…) compared with the traditional design method.

[0064] It is assumed that the target tracking residence time t r is 1 under the traditional design method, and the target tracking residence time t r designed by the method can be expressed as:

[0065]

[0066] Compared with the traditional extensive design, the time and energy resources saved by the method can be expressed as:

[0067]

[0068] According to the target tracking accuracy requirement, the method of the application adjusts the target tracking accumulated point number or tracking interval in an adaptive manner to optimize the target tracking residence time based on the target distance, speed, acceleration, and reflection cross-sectional area information without affecting the radar detection performance, thereby significantly reducing the tracking time resource consumption of large targets, close-range targets, and low-speed targets, and improving the time and energy resource utilization rate of the radar system.

[0069] The method for optimizing the time and energy resources of cognitive multi-target TAS tracking provided by the application adjusts and optimizes the target tracking parameters dynamically according to the detection signal-to-noise ratio of the target position based on the real-time estimation of the reflection cross-sectional area of the target under the premise of meeting the target tracking accuracy requirement, thereby reducing the tracking residence time of close-range targets or targets with large RCS at the same distance position, and saving the time and energy resources of the radar system.

[0070] As shown in FIG. Figure 8 , the adaptive scheduling and optimization are performed by adjusting the target accumulated point number; the method comprises the following steps:

[0071] Setting a target detection signal-to-noise ratio threshold based on the target tracking accuracy requirement;

[0072] The target accumulation point number can be halved based on the target detection signal-to-noise ratio threshold value.

[0073] The target residence time is calculated based on the number of times the target accumulation point number can be halved.

[0074] The application also includes adaptive scheduling and optimization by adjusting the waveform, PRI or tracking interval.

[0075] Embodiment 1:

[0076] Step 1: Assuming that the radar works in TAS mode, set the target detection signal-to-noise ratio threshold value SNR r according to the target tracking accuracy requirement. For example, under the design parameters shown in Table 1, set the target detection signal-to-noise ratio threshold value SNR r = 26 dB, see Figure 1 .

[0077] Step 2: Calculate the number of times the target accumulation point number can be halved under the premise of ensuring target tracking accuracy according to formula (5) and formula (6), see Figure 2 , Figure 3 and Figure 4 , Figure 5 .

[0078] Step 3: Calculate the target residence time according to formula (7), see Figure 6 , Figure 7 .

[0079] Combined with formula (8), the time and energy resource normalized value saved after dynamically designing the radar parameters using the method described in the application can be calculated as:

[0080] Single target tracking: Δt r (%) = 27%;

[0081] Multiple target tracking at the same distance: Δt r (%) = 37.4%.

[0082] The test results show that by using the cognitive multi-target TAS tracking time and energy resource optimization method proposed in the application to design the radar tracking parameters, the time and energy resources of single target tracking can be saved by 27% under the premise of ensuring target tracking accuracy; the time and energy resources of multiple target tracking at the same tracking distance can be saved by 37.4%, thereby significantly improving the utilization rate of radar time and energy resources, and having strong engineering application value.

[0083] For different radar design parameters, the upper limit of the pulse accumulation point number dynamically adjusted can be preset according to the radar detection performance; the target detection signal-to-noise ratio threshold can be adaptively adjusted according to the specific requirements of tracking accuracy, without affecting the use of the subsequent steps of the method; the time and energy resources saved by the method can be used to improve the radar power or multi-target capability, etc.

[0084] The tracking time and energy resource scheduling method provided by the application is suitable for any target type of radar tracking, such as projectile type, aircraft type, unmanned aerial vehicle type, etc.

[0085] Table 1 radar parameter description

[0086] Operating mode Azimuth receive beamwidth Elevation receive beamwidth Azimuth angle measurement accuracy Elevation angle measurement accuracy TAS 2.8° 2.41° 0.1° 0.1°

[0087] The above is only a specific embodiment of the application, but the protection scope of the application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the application, and these modifications or replacements should be covered within the protection scope of the application.

Claims

1. A cognitive multi-objective TAS tracking time energy resource optimization method, characterized in that, Adaptive scheduling and optimization are performed by adjusting the target accumulation point number; including: Setting a target detection signal-to-noise ratio threshold based on target tracking accuracy requirements; Calculating the number of times the target accumulation point number can be halved based on the target detection signal-to-noise ratio threshold; specifically: Calculating the signal-to-noise ratio margin in the target tracking process based on the target detection signal-to-noise ratio threshold; Determining the number of times the target accumulation point number can be halved based on the signal-to-noise ratio margin in the target tracking process; specifically: The target accumulation point number can be halved for a number of times of signal-to-noise ratio margin in the target tracking process divided by pw2db(n), wherein, n is a target number; Calculating the target dwell time based on the number of times the target accumulation point number can be halved.

2. The cognitive multi-objective TAS tracking time energy resource optimization method of claim 1, wherein, The target detection signal-to-noise ratio threshold is set based on the target tracking accuracy requirements, specifically: Obtain an angle measurement accuracy versus SNR curve, with the horizontal axis of the angle measurement accuracy versus SNR curve being signal-to-noise ratio and the vertical axis being angle measurement accuracy; Based on the target tracking accuracy requirements, find the signal-to-noise ratio corresponding to the target tracking accuracy requirements on the angle measurement accuracy versus SNR curve.

3. The cognitive multi-objective TAS tracking time energy resource optimization method of claim 2, wherein, The target detection signal-to-noise ratio threshold is set based on the target tracking accuracy requirements, specifically: The target tracking accuracy requirements include azimuth angle measurement accuracy and elevation angle measurement accuracy; Find the signal-to-noise ratio corresponding to the azimuth angle measurement accuracy on the angle measurement accuracy versus SNR curve; Find the signal-to-noise ratio corresponding to the elevation angle measurement accuracy on the angle measurement accuracy versus SNR curve; Compare the signal-to-noise ratio corresponding to the azimuth angle measurement accuracy and the signal-to-noise ratio corresponding to the elevation angle measurement accuracy, and take the maximum of the two.

4. The cognitive multi-objective TAS tracking time energy resource optimization method of claim 1, wherein, The target dwell time is calculated based on the number of times the target accumulation point number can be halved, specifically: wherein, is the number of times the accumulation point is halved. is the number of times the accumulation point is halved.

5. The cognitive multi-objective TAS tracking time energy resource optimization method of claim 1, wherein, Further including adaptive scheduling and optimization by adjusting waveforms, PRI, or tracking intervals.

6. A computer system, characterized by Including: One or more processors, a computer readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method of claim 1.

7. A computer readable storage medium characterized by Computer executable instructions are stored, which when executed, are used to implement the method of claim 1.

8. A computer program product, characterised in that Including computer executable instructions, which when executed, are used to implement the method of claim 1.

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