Cognitive multi-target TAS tracking time energy resource optimization method

Abstract

The invention relates to a cognitive multi-target TAS tracking time energy resource optimization method, and belongs to the technical field of multi-functional phased array radar cognitive multi-target tracking. Comprising the following steps: performing adaptive scheduling and optimization by adjusting a target accumulation point number; the method specifically comprises the steps of setting a target detection signal-to-noise ratio threshold value based on a target tracking precision requirement; based on a target detection signal-to-noise ratio threshold value, calculating halfway times of the target accumulation point number; and calculating the target residence time based on the halfway times of the target accumulation point number. By adopting the multi-target tracking time energy resource scheduling method based on the TAS system provided by the invention, tracking parameters can be dynamically adjusted and target tracking residence time can be reduced according to the detection signal-to-noise ratio of the position of the target under the condition of ensuring the target tracking precision, so that target tracking time energy resources are saved, and the target tracking efficiency is improved. And the method has relatively high engineering application value.

Description

Technical Field

[0001] The invention belongs to the technical field of multi-functional phased array radar cognitive multi-target tracking, and specifically is a multi-target tracking time energy resource scheduling method based on a TAS system. Background Art

[0002] Time and energy resource management is an important part of the design of multi-function radar systems. When radars work in the same environment and adopt different time and energy scheduling strategies, the detection performance of the system may vary greatly.

[0003] In the traditional TAS tracking method, in order to ensure the tracking accuracy during the target tracking process, fixed tracking parameters such as pulse width and accumulation points are usually designed according to the minimum target reflection cross-sectional area and the maximum tracking distance. Although this design method ensures the signal-to-noise ratio of tracking, it will cause a waste of time and energy resources due to the large target reflection cross-sectional area and the distance being lower than the maximum tracking distance, which has great potential to be explored. Summary of the invention

[0004] The technical problems to be solved by the present invention are:

[0005] In view of the problem of low time energy resource utilization in the current radar multi-target TSA tracking method, the present invention provides a cognitive multi-target TAS tracking time energy resource optimization method.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A cognitive multi-objective TAS tracking time energy resource optimization method, characterized in that adaptive scheduling and optimization are performed by adjusting target accumulation points; comprising:

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

[0009] Calculate the number of times the target accumulation points can be halved based on the target detection signal-to-noise ratio threshold;

[0010] The target dwell time is calculated based on the number of times the target's accumulated points can be halved.

[0011] A further technical solution of the present invention is as follows: the target detection signal-to-noise ratio threshold is set based on the target tracking accuracy requirement, specifically:

[0012] Obtaining a curve graph of angle measurement accuracy versus SNR, wherein the abscissa of the curve graph of angle measurement accuracy versus SNR is the signal-to-noise ratio, and the ordinate is the angle measurement accuracy;

[0013] Based on the target tracking accuracy requirements, find the signal-to-noise ratio corresponding to the target tracking accuracy requirements on the angle measurement progress vs. SNR curve.

[0014] A further technical solution of the present invention is as follows: based on the target tracking accuracy requirement, the signal-to-noise ratio corresponding to the target tracking accuracy requirement is searched on the angle measurement progress versus SNR change curve, specifically:

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

[0016] Find the signal-to-noise ratio corresponding to the azimuth angle measurement accuracy on the angle measurement progress vs. SNR curve;

[0017] Find the signal-to-noise ratio corresponding to the pitch angle measurement accuracy on the angle measurement progress vs. SNR curve;

[0018] 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 value of the two.

[0019] A further technical solution of the present invention is as follows: the number of times the target accumulation points can be halved based on the target detection signal-to-noise ratio threshold is calculated, specifically:

[0020] Calculate the signal-to-noise ratio margin in the target tracking process based on the target detection signal-to-noise ratio threshold;

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

[0022] A further technical solution of the present invention is as follows: the number of times the target accumulation points can be halved is determined based on the signal-to-noise ratio margin in the target tracking process, specifically:

[0023] The number of times the target accumulation points can be halved is the signal-to-noise ratio margin during the target tracking process divided by pw2db(n), where n is the number of targets.

[0024] A further technical solution of the present invention is: the target residence time is calculated based on the number of times the target accumulated points can be halved, specifically:

[0025]

[0026] Among them, n t The number of times the accumulated points M are halved can be adjusted.

[0027] A further technical solution of the present invention 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, and 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-mentioned method.

[0029] A computer-readable storage medium, characterized in that it stores computer-executable instructions, which are used to implement the above method when executed.

[0030] A computer program product, characterized by comprising computer executable instructions, wherein the instructions are used to implement the above method when executed.

[0031] The beneficial effects of the present invention are:

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

[0033] By adopting the multi-target tracking time energy resource scheduling method based on the TAS system proposed in the present invention, the tracking parameters can be dynamically adjusted according to the detection signal-to-noise ratio of the target location while ensuring the target tracking accuracy, thereby reducing the target tracking residence time, thereby saving target tracking time energy resources, and having strong engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0035] Figure 1 It is a schematic diagram of a curve showing the relationship between angle measurement accuracy and detection signal-to-noise ratio described in the present invention.

[0036] Figure 2 It is a schematic diagram of a curve showing the relationship between the detection signal-to-noise ratio and the tracking distance when ensuring the tracking accuracy used in the present invention.

[0037] Figure 3 A schematic diagram of a curve showing the relationship between the detection signal-to-noise ratio and the target reflection cross-sectional area when ensuring tracking accuracy used in the present invention.

[0038] Figure 4 This is a schematic diagram of the relationship between the number of times the accumulated points are halved and the tracking distance when ensuring the tracking accuracy used in the present invention.

[0039] Figure 5 It is a schematic diagram of the relationship between the number of times the accumulated points are halved and the target reflection cross-sectional area when ensuring the tracking accuracy used in the present invention.

[0040] Figure 6 It is a schematic diagram of a curve showing the relationship between target tracking dwell time and tracking distance after using the present invention.

[0041] Figure 7It is a schematic diagram of a curve showing the relationship between the target tracking dwell time and the target reflection cross-sectional area change after using the present invention.

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

[0043] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention 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 invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0045] In the traditional design method, the tracking parameters are designed according to the fixed mode and the maximum tracking distance condition. The time and energy resources of the radar for tracking the target are independent of 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, target reflection cross-sectional area, etc. during operation, the tracking parameters (waveform, PRI, accumulated points, tracking interval, etc.) can be adaptively scheduled and optimized according to the combat mission requirements during the target tracking process.

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

[0048]

[0049] Where: R max is the maximum target tracking distance, P av is the average transmission power, A t is the transmitting antenna gain, A r is the receiving antenna gain, σ is the radar cross-sectional area of ​​the target, t ris the target tracking dwell time, λ is the working wavelength, k is the Boltzmann constant, T 0 Standard room temperature, F n is the noise factor, D 0 (1) is the detection factor, C B is the bandwidth correction factor and L is the loss coefficient.

[0050] Assume that the target tracking dwell time t r The number of accumulated points at different distances is M, and the pulse repetition period is T, that is, t r =∑MT, so formula (1) can be expressed as:

[0051]

[0052] Among them, R is the target tracking distance, SNR r The detection signal-to-noise ratio threshold to ensure target tracking accuracy is related to the radar mission mode.

[0053] Assuming that the radar works in TAS mode, the detection signal-to-noise ratio threshold that meets the target tracking accuracy requirement is SNR r When tracking a single target, it is assumed that the target reflection cross-sectional area σ remains unchanged, and the target tracking dwell time t r and tracking distance R 4 When multiple targets are tracked at the same distance, the target tracking dwell time t r It is inversely proportional to the target reflection cross-sectional area σ; when simultaneously tracking multiple targets at different distances, the tracking dwell time should be further analyzed based on the actual situation.

[0054] Since the target tracking dwell time t r It is proportional to the accumulated points M and the tracking interval T. Therefore, for single target and simultaneous multi-target tracking, the target tracking residence time can be optimized by dynamically adjusting the accumulated points and tracking interval during the tracking process.

[0055] Consider the signal-to-noise ratio attenuation caused by tracking distance or target RCS during target tracking:

[0056] SNR 1 =-40*log10(R) (3)

[0057] SNR 2 =10*log10(σ) (4)

[0058] Among them, SNR 1 is the signal-to-noise ratio attenuation caused by tracking distance, SNR 2 The signal-to-noise ratio degradation caused by the target RCS.

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

[0060] ΔSNR 1 = -40*log10(R)-SNR r (5)

[0061] ΔSNR 2 =10*log10(σ)-SNR r (6)

[0062] Where ΔSNR 1 is the signal-to-noise ratio margin generated by tracking distance, ΔSNR 2 The signal-to-noise margin generated for the target RCS.

[0063] Combined with the above analysis, assuming that in the target tracking process, based on different signal-to-noise ratio margins, the target tracking residence time designed by the method proposed in the present invention can adjust the accumulated points M to be halved n times compared with the traditional design method t (n t =0,1,2,3…).

[0064] Assume that in the traditional design method, the target tracking dwell time t r is 1, then the target tracking residence time t designed by the method of the present invention r It can be expressed as:

[0065]

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

[0067]

[0068] The present invention adopts dynamic time energy resource scheduling technology based on target tracking accuracy requirements and without affecting radar detection performance. The target tracking accumulation points or tracking interval are adjusted in an adaptive manner based on target distance, speed, acceleration, reflection cross-sectional area and other information. The target tracking residence time is optimized. While ensuring the tracking signal-to-noise ratio, the tracking time resource overhead of large targets, close-range and low-speed targets is significantly reduced, thereby improving the time energy resource utilization rate of the radar system.

[0069] The present invention provides a cognitive multi-target TAS tracking time energy resource optimization method, which estimates the reflection cross-sectional area of ​​the target in real time, dynamically adjusts and optimizes the target tracking parameters according to the detection signal-to-noise ratio of the target location while ensuring that the target tracking accuracy requirements are met, thereby reducing the tracking residence time of near-field targets or targets with larger RCS at the same distance and position, and saving radar system time energy resources.

[0070] like Figure 8 As shown, adaptive scheduling and optimization are performed by adjusting the target accumulation points; the following steps are included:

[0071] Set the target detection signal-to-noise ratio threshold based on the target tracking accuracy requirements;

[0072] Calculate the number of times the target accumulation points can be halved based on the target detection signal-to-noise ratio threshold;

[0073] The target dwell time is calculated based on the number of times the target's accumulated points can be halved.

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

[0075] Embodiment 1:

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

[0077] Step 2: According to equations (5) and (6), calculate the number of times the target accumulated points can be halved while ensuring the target tracking accuracy. 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 normalized value of time and energy resources saved after dynamically designing radar parameters using the method of the present invention 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 experimental results show that by designing radar tracking parameters through the cognitive multi-target TAS tracking time energy resource optimization method proposed in the present invention, the time energy resources of radar single target tracking can be saved by 27% under the premise of ensuring target tracking accuracy; the time energy resources of multi-target tracking at the same tracking distance can be saved by 37.4%, thereby significantly improving the radar time energy resource utilization rate and having strong engineering application value.

[0083] For different radar design parameters, the upper limit of the dynamic adjustment of the pulse accumulation points 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 subsequent steps of the method; the time and energy resources of the radar system saved by using this method can be used to enhance the radar power or multi-target capability, etc.

[0084] The tracking time energy resource scheduling method proposed in the present invention is applicable to any target type tracked by radar, such as projectiles, aircraft, unmanned aerial vehicles, etc.

[0085] Table 1 Radar parameter description

[0086] Working Mode Azimuth receive beamwidth Elevation receive beam width Azimuth angle measurement accuracy Pitch angle measurement accuracy TAS 2.8° 2.41° 0.1° 0.1°

[0087] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should be included in the protection scope of the present invention.

Claims

1. A cognitive multi-objective TAS tracking time energy resource optimization method, characterized in that: Adaptive scheduling and optimization by adjusting target accumulation points; including: Set the target detection signal-to-noise ratio threshold based on the target tracking accuracy requirements; Calculate the number of times the target accumulation points can be halved based on the target detection signal-to-noise ratio threshold; The target dwell time is calculated based on the number of times the target's accumulated points can be halved.

2. According to the cognitive multi-objective TAS tracking time energy resource optimization method of claim 1, it is characterized in that: The target detection signal-to-noise ratio threshold is set based on the target tracking accuracy requirement, specifically: Obtaining a curve graph of angle measurement accuracy versus SNR, wherein the abscissa of the curve graph of angle measurement accuracy versus SNR is the signal-to-noise ratio, and the ordinate is the 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 progress vs. SNR curve.

3. According to claim 2, a cognitive multi-objective TAS tracking time energy resource optimization method is characterized in that: Based on the target tracking accuracy requirement, the signal-to-noise ratio corresponding to the target tracking accuracy requirement is found on the angle measurement progress versus SNR change curve, specifically: The target tracking accuracy requirement includes 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 progress vs. SNR curve; Find the signal-to-noise ratio corresponding to the pitch angle measurement accuracy on the angle measurement progress vs. 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 value of the two.

4. According to claim 1, a cognitive multi-objective TAS tracking time energy resource optimization method is characterized in that: The number of times the target accumulation points can be halved based on the target detection signal-to-noise ratio threshold is calculated as follows: Calculate the signal-to-noise ratio margin in the target tracking process based on the target detection signal-to-noise ratio threshold; The number of times the target accumulation points can be halved is determined based on the signal-to-noise ratio margin during target tracking.

5. According to claim 4, a cognitive multi-objective TAS tracking time energy resource optimization method is characterized in that: The number of times the target accumulation points can be halved is determined based on the signal-to-noise ratio margin in the target tracking process, specifically: The number of times the target accumulation points can be halved is the signal-to-noise ratio margin during the target tracking process divided by pw2db(n), where n is the number of targets.

6. According to claim 1, a cognitive multi-objective TAS tracking time energy resource optimization method is characterized in that: The target residence time is calculated based on the number of times the target accumulated points can be halved, specifically: Among them, n t The number of times the accumulated points M are halved can be adjusted.

7. According to claim 1, a cognitive multi-objective TAS tracking time energy resource optimization method is characterized in that: It also includes adaptive scheduling and optimization by adjusting waveform, PRI or tracking interval.

8. A computer system, characterized in that include: One or more processors, and 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 are enabled to implement the method of claim 1.

9. A computer-readable storage medium, characterized in that Computer executable instructions are stored, and when the instructions are executed, they are used to implement the method of claim 1.

10. A computer program product, characterized in that The invention comprises computer executable instructions, which are used to implement the method of claim 1 when being executed.

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