Multi-target angle tracking method suitable for broadband phased array system

Abstract

The invention provides a multi-target angle tracking method suitable for a broadband phased array system, and belongs to the technical field of wireless communication. The method comprises the following steps: acquiring an initial angle of each target through initial angle search; generating a digital baseband signal; calculating the time delay compensation amount of each target in each array element receiving signal in a data field; performing time delay-phase joint weighting on the digital baseband signal to generate a weighted signal; synthesizing a multi-target beam directional diagram through a beam former; and through a peak detection algorithm, an angle maximum value is extracted from the multi-target beam directional diagram, the angle value of each target is updated, and loop iteration is continued. According to the method, the problems of channel inconsistency, low angle resolution and high calculation complexity of a traditional phased array system during multi-target tracking are solved. The method can track a plurality of targets at the same time, and is suitable for unmanned aerial vehicle communication and high-speed motion platforms.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication and radar, and particularly relates to a multi-target angle tracking method applicable to a broadband phased array system. Background Art

[0002] The existing phased array tracking technology has the following defects:

[0003] (1) Strong dependence on channel calibration: Traditional methods need to calibrate the amplitude-phase characteristics of channels with the help of a vector voltmeter, and cannot compensate for array errors in real time;

[0004] (2) Insufficient broadband processing ability: Uniform linear arrays are prone to grating lobes in broadband scenarios, resulting in the failure of multi-target resolution;

[0005] (3) Limited tracking accuracy: Conventional Newton iteration methods are prone to falling into local extrema at low signal-to-noise ratios, and main lobe interference will significantly reduce the angle measurement accuracy;

[0006] (4) High resource consumption: The narrowband and broadband alternating scheduling mode increases the system power consumption and is difficult to meet the lightweight requirements of unmanned aerial vehicles.

[0007] Although the angle tracking algorithms in the existing technologies can estimate the initial angle, they fail to solve the data association problem in dynamic tracking and do not integrate a closed-loop feedback mechanism. Summary of the Invention

[0008] In view of this, the present invention provides a multi-target angle tracking method applicable to a broadband phased array system. The present invention can solve the problems of channel inconsistency, low angle resolution, and high computational complexity existing in traditional phased array systems during multi-target tracking.

[0009] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0010] A multi-target angle tracking method applicable to a broadband phased array system, where the broadband phased array system has N array elements arranged at equal intervals along a straight line, and includes the following steps:

[0011] Step 1, each array element respectively receives radio frequency signals from M targets to obtain N received signals, and each received signal contains information of M targets;

[0012] Step 2, set the iteration number k = 0 and initialize the angle values of each target

[0013] Step 3, perform analog-to-digital conversion on the received signals to generate digital baseband signals s n (t), n = 1, 2,..., N, where t is a time variable;

[0014] Step 4, calculate the time delay compensation amount τ corresponding to the m-th target in the received signal of the n-th array element in the data domain n,m :

[0015]

[0016] where d n is the distance between the n-th array element and the starting array element, c is the speed of light, β is the convergence factor, and 0.1 ≤ β ≤ 0.5; when k = 0,

[0017] Step 5, perform M times of time delay-phase joint weighting on s n (t) to generate M weighted signals s' n,m (t);

[0018] Step 6, input the N weighted signals corresponding to the m-th target into the beamformer to synthesize the multi-target beam pattern B m (θ):

[0019]

[0020] where w n is the array element amplitude weight, and θ represents the scanning angle parameter of beamforming;

[0021] Step 7, through the peak detection algorithm, extract the angle maxima θ'1, θ'2, …, θ' m , …, θ' M ; update the iteration number k = k + 1, and set

[0022] Step 8, continue to obtain the received signals of each array element, and repeat Steps 3 - 7 to achieve continuous tracking of multiple target angles simultaneously.

[0023] Furthermore, in Step 2, obtain the initial angle estimates of each target through initial angle search, which is the initial angle estimate of the m-th target.

[0024] Furthermore, in Step 4, d n satisfies:

[0025] d n = (n - 1)λ / 2

[0026] where λ is the signal wavelength.

[0027] Furthermore, the specific method of Step 5 is:

[0028]

[0029] Among them, f c is the signal carrier frequency, j is the imaginary unit, and α n is the adaptive weighting factor, and α n is dynamically adjusted by the Goldschmidt iteration algorithm.

[0030] Furthermore, in step 6, the beamformer optimizes the amplitude weight w n using the minimum variance distortionless response criterion, and the constraint conditions are:

[0031]

[0032] Among them, R is the covariance matrix, that is, the N×N-dimensional signal statistical matrix; a(θ m ) is the steering vector, which is a complex vector used to describe the beam direction; the superscript H represents the conjugate transpose; w is the N-dimensional complex weight vector composed of w n .

[0033] Furthermore, the peak detection algorithm in step 7 uses an improved Newton iteration method based on curvature constraint, and its iteration formula is:

[0034]

[0035] Among them, γ is the curvature compensation factor, B m '(θ (p) ) is the first derivative of B m (θ (p) ), B m ”(θ (p) ) is the second derivative of B m (θ (p) ), and p is the number of iterations;

[0036] The calculation method of the curvature compensation factor γ is:

[0037]

[0038] Among them, d max is the maximum aperture of the array, λ is the signal wavelength, and SNR is the signal-to-noise ratio.

[0039] The beneficial effects of the present invention are as follows:

[0040] 1. Multi-target capacity improvement: It supports simultaneous tracking of multiple targets, can improve the angular resolution, and is especially suitable for multi-beam joint optimization and dynamic closed-loop control under orthogonal frequency division multiplexing (OFDM) modulation signals;

[0041] 2. Enhanced anti-interference: The MVDR criterion improves the main lobe interference suppression ratio;

[0042] 3. Real-time optimization: The Goldschmidt algorithm reduces the processing delay;

[0043] 4. Environmental adaptability: The tracking of maneuvering targets is improved through the β factor, making it more adaptable to the UAV maneuvering scenario. Specific implementation manner

[0044] The technical solution of the present invention will be further described below. Obviously, these are only a part of the embodiments of the present invention, rather than all embodiments. Based on the following embodiments, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] A multi-target angle tracking method applicable to a broadband phased array system, which is applied to a broadband phased array system with N array elements, and each array element is connected with an analog-digital hybrid processing unit; the N array elements form a uniform linear array, that is, all array elements are arranged at equal intervals along a straight line, and are numbered 1, 2,..., N in sequence starting from the starting array element, and the distance d between the nth array element and the 1st array element n is:

[0046] d n =(n - 1)λ / 2

[0047] where λ is the signal wavelength.

[0048] This method includes the following steps:

[0049] Step 1: Receive the radio frequency signals transmitted by M targets through N array elements, and the received signals of each array element contain the information of M targets;

[0050] Step 2: Set the iteration number k = 0; obtain the initial estimated values of the angles of M targets through initial angle search Set the angle value θ of each target m , for θ m Initialization: m = 1, 2,..., M;

[0051] Next, enter the iteration link:

[0052] Step 3: Perform analog-to-digital conversion on the received signal of the corresponding array element through the analog-digital hybrid processing unit to generate a digital baseband signal s n (t), where n = 1, 2,..., N, and t is the time variable;

[0053] Step 4: Calculate the delay compensation amount τ corresponding to the mth target in the received signal of the nth array element in the data domain n,m :

[0054]

[0055] where d n is the spacing between the nth element and the starting element, c is the speed of light, β is the convergence factor, and the value range is 0.1 ≤ β ≤ 0.5; when k = 0,

[0056] Step 5: Perform M times of time-delay and phase joint weighting on s n (t) to generate M weighted signals s' n,m (t):

[0057]

[0058] where f c is the signal carrier frequency, j is the imaginary unit, α n is the adaptive weighting factor, and α n is dynamically adjusted through the Goldschmidt iterative algorithm and is used to compensate for the channel inconsistency between elements.

[0059] Step 6: Input the N weighted signals corresponding to the mth target into the beamformer to synthesize the multi-target beam pattern B m (θ):

[0060]

[0061] where w n is the element amplitude weight, and θ represents the scanning angle parameter of beamforming;

[0062] The beamformer can optimize the amplitude weight w using the MVDR (Minimum Variance Distortionless Response) criterion n , and its constraint condition is:

[0063]

[0064] where R is the covariance matrix, that is, the N×N-dimensional signal statistical matrix; a(θ m ) is the steering vector, which is a complex vector used to describe the beam direction; the superscript H represents the conjugate transpose; w is the N-dimensional complex weight vector composed of w n .

[0065] Step 7: Through the peak detection algorithm, extract an angle maximum value from each multi-target beam pattern, and a total of M angle values are obtained: θ'1, θ'2, …, θ' m , …, θ' M ; update the iteration number k = k + 1, and set

[0066] Among them, the peak detection algorithm can adopt an improved Newton iteration method based on curvature constraint, and its iteration formula is:

[0067]

[0068] Among them, γ is the curvature compensation factor, B m '(θ (p) ) is the first derivative of B m (θ (p) ), B m ”(θ (p) ) is the second derivative of B m (θ (p) ), and p is the number of iterations;

[0069] The calculation method of the curvature compensation factor γ is:

[0070]

[0071] Among them, d max is the maximum aperture of the array, λ is the signal wavelength, and SNR is the signal-to-noise ratio.

[0072] Continue to obtain the received signals of each array element, and repeat steps 3 - step 7 to achieve continuous tracking of multiple target angles simultaneously.

[0073] Next, for the mid - low altitude UAV communication scenario, verify the robustness of the multi - target angle tracking method under strong multipath interference. The system architecture and test environment are as follows:

[0074]

[0075] In this embodiment, through the dynamic adjustment of α n in step 5, the main lobe offset caused by channel inconsistency in the 32 - element system can be reduced from 2.1° to 0.3°.

[0076] In this embodiment, through the optimization of w n in step 6, the power consumption ratio per unit angle accuracy can reach 3.2 mW / ° under a 100 MHz bandwidth, which is better than the traditional method.

[0077] In this embodiment, according to the SNR, the γ value in step 7 is adjusted in real - time. When SNR = 25 dB, γ = 0.0026, and this mechanism reduces the false alarm rate in the low - SNR scenario.

[0078] Through the verification method combining microwave anechoic chamber and field measurement, the following quantitative results are obtained:

[0079] Table 1 Multi - target tracking accuracy test

[0080]

[0081] Table 2 Comparison of System Resource Consumption

[0082] Indicator Traditional method This invention Improvement range Processing delay (4 targets) 18.7 ms 11.2 ms 40％↓ Dynamic power consumption (@100 MHz) 9.8W 6.3W 35.7％↓ Memory occupancy 512 MB 287 MB 44％↓ Angle refresh rate 50 Hz 83 Hz 66％↑

[0083] Table 3 Anti-Jamming Performance Test

[0084] Interference type Error of traditional method (°) Error of this invention (°) Suppression gain No interference 0.11 0.07 - Narrowband co-channel interference 1.87 0.23 8.1 dB Wideband blocking interference 2.35 0.51 6.7 dB Multipath coherent interference 1.02 0.15 8.3 dB

[0085] This embodiment verifies the engineering feasibility of the method in a complex electromagnetic environment, and its core advantages are reflected in:

[0086] (1) The tracking accuracy in the low-altitude multipath scenario is improved by 5 times through the curvature compensation mechanism;

[0087] (2) The system can maintain an angle measurement accuracy of 0.1° level in an interference environment through MVDR optimization;

[0088] (3) The exponential convergence characteristic of angle estimation is achieved through the closed-loop iterative algorithm.

[0089] The present invention can be used in vehicle-mounted phased array platforms. By efficiently converting radio frequency signals and combining time delay-phase joint weighting and improved Newton iteration method, it solves the problems of channel inconsistency, low angle resolution and high computational complexity existing in traditional phased array systems during multi-target tracking. The present invention adopts time delay compensation based on initial angle estimation, signal weighting processing with introduction of adaptive weighting factors, beamforming optimized by MVDR criterion, and closed-loop iterative update mechanism.

[0090] In summary, the technical solution of the present invention significantly improves multi-target angle tracking, signal stability and anti-jamming ability, solves the problems of peripheral signal interference, low angle tracking efficiency and high delay in conventional phased array systems, and is applicable to the fields of UAV communication, radar detection and satellite communication.

[0091] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A multi-target angle tracking method applicable to a broadband phased array system, characterized in that, The broadband phased array system has N array elements arranged at equal intervals along a straight line, and includes the following steps: Step 1, each array element respectively receives radio frequency signals from M targets to obtain N received signals, and each received signal contains information of M targets; Step 2, set the number of iterations k = 0 and initialize the angular values of each target Step 3: Perform analog-to-digital conversion on the received signal to generate a digital baseband signal s n (t), n = 1, 2, ..., N, where t is the time variable; Step 4, calculate the delay compensation amount τ corresponding to the m-th target in the received signal of the n-th array element in the data domain n,m : where d n is the distance between the nth array element and the starting array element, c is the speed of light, β is the convergence factor, 0.1 ≤ β ≤ 0.5; when k = 0, Step 5, perform M times of time-delay and phase joint weighting on s n (t) to generate M weighted signals s' n,m (t); Step 6: Input the N weighted signals corresponding to the m-th target into a beamformer to synthesize a multi-target beam pattern B m (θ): where, w n is the element amplitude weight, and θ represents the scanning angle parameter of beamforming; Step 7: By using the peak detection algorithm, respectively extract the angular maxima θ'1, θ'2, …, θ' from the M multi-target beam patterns m , …, θ' M ; Update the iteration number k = k + 1, and set Step 8, continue to obtain the received signals of each array element, and repeat Steps 3 - 7 to achieve continuous tracking of multiple target angles simultaneously.

2. The multi-target angle tracking method applicable to a broadband phased array system according to claim 1, wherein In step 2, the initial angle estimates of each target are obtained through initial angle search, which is the initial angle estimate of the m-th target.

3. A multi-target angle tracking method applicable to a broadband phased array system according to claim 1, characterized in that In step 4, d n satisfies: d n =(n - 1)λ / 2 Where λ is the signal wavelength.

4. A multi-target angle tracking method applicable to a broadband phased array system according to claim 1, characterized in that, The specific method of Step 5 is: Among them, f c is the signal carrier frequency, j is the imaginary unit, and α n is the adaptive weighting factor, and α n is dynamically adjusted by the Goldschmidt iteration algorithm.

5. A multi-target angle tracking method applicable to a broadband phased array system according to claim 1, characterized in that, In step 6, the beamformer optimizes the amplitude weight w using the minimum variance distortionless response criterion n , and the constraint condition is: where R is the covariance matrix, i.e., the N×N-dimensional signal statistical matrix; a(θ m ) is the steering vector, which is a complex vector used to describe the beam direction; the superscript H represents the conjugate transpose; w is the N-dimensional complex weight vector composed of w n .

6. The multi-target angle tracking method applicable to a broadband phased array system according to claim 1, characterized in that, The peak detection algorithm in Step 7 adopts an improved Newton iteration method based on curvature constraint, and its iteration formula is: where γ is the curvature compensation factor, B m '(θ (p) ) is the first derivative of B m (θ (p) ), B m ”(θ (p) ) is the second derivative of B m (θ (p) ), and p is the number of iterations; The calculation method of the curvature compensation factor γ is: where d max is the maximum aperture of the array, λ is the signal wavelength, and SNR is the signal-to-noise ratio.

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