A long-short baseline-based interference detection and suppression implementation method

Abstract

The application discloses a kind of based on long-short baseline's interference detection and inhibition implementation method, belong to satellite navigation communication in array signal processing field;Including: the M road radio frequency analog signal received by array antenna is carried out frequency conversion and A / D quantization processing, obtains M road intermediate frequency digital signal;Signal is received using two groups of long-short baseline system relative to different phase difference of element;Using long-short baseline to carry out ambiguity resolution processing;By the phase difference finally obtained, the azimuth and elevation of incident signal are solved;Using effective interference to information, based on improved particle swarm algorithm, the two-dimensional array directional diagram of received signal is constrained, and the best weight value for interference suppression in space range is obtained;M road intermediate frequency digital signal is carried out spatial filtering using the best weight value, and satellite navigation signal without interference is obtained.The algorithm complexity of the application is low, the direction-finding precision is high, and the convergence speed of interference suppression best weight value is fast.

Description

Technical Field

[0001] This invention relates to a method for interference detection and suppression based on long and short baselines, belonging to the field of array signal processing in satellite navigation and communication. Background Technology

[0002] With the increasing complexity of the electromagnetic environment for satellite navigation applications and the continuous development of navigation interference technology, the continuity, integrity, and availability of navigation systems face enormous challenges. How to efficiently detect and suppress interference has become a research hotspot in the field of satellite navigation applications.

[0003] Interferometer direction finding methods offer advantages such as high accuracy, low hardware resource requirements, good real-time performance, and wide bandwidth adaptability, making them widely used in fields such as radio monitoring and electronic countermeasures. The phase interferometer direction finding algorithm based on defuzzification technology first solves for the fuzziness of the phase difference between array elements to obtain the actual phase difference of the array signal. Finally, it uses this actual phase difference to inversely solve for the incident direction of the incident signal. Due to its low computational complexity and simple implementation, it has significant advantages in the field of miniaturized arrays.

[0004] Interference suppression based on particle swarm optimization (PSO) algorithm obtains the optimal array signal processing weights by adaptively searching through particle swarm optimization, processing direction finding results and spatial null suppression. Compared to traditional synthesis algorithms, it is gaining increasing attention due to its simplicity, efficiency, and superior performance in handling multiple targets and nonlinear synthesis. PSO can quickly and effectively find the optimal solution, offering faster convergence and higher accuracy compared to traditional methods. Summary of the Invention

[0005] This invention proposes an interference detection and suppression method based on long and short baselines, which can solve the problems of poor direction finding accuracy and high computational complexity in the construction process of existing miniaturized array antennas, and effectively avoid the "phase ambiguity" problem of long baseline interference detection. It is applicable to receiver terminals of various satellite navigation systems.

[0006] The basic idea of ​​the technical solution of this invention is: to solve the direction of interference using a phase interferometer direction-finding algorithm based on defuzzification technology, and then to use the interference direction information as input, and to use an improved particle swarm algorithm to constrain the array pattern to achieve the purpose of interference suppression.

[0007] This invention is achieved through the following technical solution:

[0008] An interference detection and suppression method based on long and short baselines includes the following steps:

[0009] Step 1: Given that the antenna array consists of... It consists of several antenna array elements. m is a positive integer; the M radio frequency analog signals received by the antenna array are down-converted and A / D quantized to obtain M intermediate frequency digital signals;

[0010] Step 2: arrive This indicates the positions of the M antenna array elements. arrive and arrive Forming two short baselines, length , arrive and arrive Forming two long baselines, length , Integer and >1, The signal wavelength is used; the phase difference between the interference signal and the received signals of different antenna elements is obtained using two sets of long and short baselines;

[0011] (1)

[0012] (2)

[0013] (3)

[0014] (4)

[0015] in , , and They are arrive , , and The phase difference measured at the baseline, The azimuth angle at which the interference signal is incident. The elevation angle at which the interference signal is incident;

[0016] Step 3: Deblurring is performed using long and short baselines;

[0017] Step 301: Calculate based on equations (5) and (6) arrive and arrive Approximate value of baseline phase difference and :

[0018] (5)

[0019] ; (6)

[0020] Step 302: Establish an approximate value for the baseline phase difference. and Phase difference with actual measurement and The relationship between them:

[0021] , (7)

[0022] , ; (8)

[0023] Step 303: Change Find the closest approximation based on the value. and of and That is arrive and arrive Precise value of baseline phase difference and ;

[0024] Step 4: Based on the final calculated phase difference and Calculate the azimuth angle of the incident interference signal and pitch angle They are respectively:

[0025] (9)

[0026] ; (10)

[0027] Step 5: Utilize the azimuth angle of the incident interference signal and pitch angle By constraining the two-dimensional array pattern of the received signal using an improved particle swarm optimization algorithm, the optimal weights for interference suppression within the spatial range are obtained. ;

[0028] Step 6: Use optimal weights By weighting the received signal, the interference-suppressed navigation signal can be obtained.

[0029] Furthermore, step 5 is as follows:

[0030] Step 501: Initialize particle information: Determine the population size as N, and each particle position consists of M amplitudes and M phases, where the amplitudes are represented as... Phase is represented as The range of amplitude is The phase range is Randomly initialize the positions and velocities of N particles; corresponding to the first... The weight expression for the next iteration is: , is an M-dimensional complex vector; initially k=1;

[0031] Step 502: Calculate the fitness function: Use the disturbance obtained in step 4 to... Interference from distance exist and Setting the zero-depression depth value in the spatial region ; for array pattern The fitness function C2 with constraints is expressed as:

[0032] (11)

[0033] (12)

[0034] (13)

[0035] in, The main beamwidth is related to the number of antenna elements and the spacing between elements. and The range of zero traps is characterized by its specific characteristics, which are determined based on the dynamic application scenario. and These are the defined sidelobe level and null depth, respectively;

[0036] The first The weight of the next iteration Substituting into formula (12), the result is calculated. ,Will Substituting into formula (11) yields the fitness function value for this iteration. By calculating the fitness function This yields the historical optimal position of the particle with the best fitness. This is the learning example, in which Represents particles Which particle's historical best value will be studied?

[0037] Step 503: Update the particle velocity and position: Introduce the particle growth rate and a probability-based perturbation factor to perturb the particles, so that stagnant particles can gain momentum to search for new extreme values.

[0038] The growth rate of particles is expressed as The smoothing coefficient , and They represent particles respectively No. generation and first The extreme fitness value of an individual in a generation. It is a preset positive integer, depending on the convergence speed of the particles to be evaluated;

[0039] Utilizing the historical best positions of all particles Perform speed and location updates:

[0040] (14)

[0041] (15)

[0042] in, For the first Particle velocity in the next iteration For the first The current position of the particle in the next iteration. As a learning factor, and The range is random numbers, These are inertial weights, used to balance the algorithm's local and global search capabilities. ,in and These are the initial and final weights for the iteration, respectively. The maximum number of iterations, , This is a preset threshold used to determine whether the particle growth rate meets the iteration requirements; This represents the position of the particle farthest from the origin. This represents the position of the particle closest to the origin.

[0043] Step 504: Determine if the convergence condition is met. If the condition is not met, then let k = k + 1, return to step 502, and recalculate the fitness function value using the updated particle velocity and particle position. If the convergence condition is met, proceed to step 505.

[0044] Step 505: When When the algorithm converges after iterative iteration, the optimal particle position is the output of the particle swarm optimization algorithm. The first M values ​​are the amplitudes, and the last M values ​​are the phases. The optimal weights are composed of the amplitudes and phases. This is the optimal weight for spatial interference suppression.

[0045] The beneficial effects achieved by this invention compared with the prior art are as follows:

[0046] This invention proposes a method for interference detection and suppression using both long and short baselines. The algorithm addresses the "phase ambiguity" problem in interference detection by employing both long and short baselines, overcoming the aperture limitations of anti-interference array antennas. Simultaneously, it utilizes a particle swarm optimization algorithm to effectively constrain the array pattern, achieving interference suppression. This invention features low algorithm complexity, high direction-finding accuracy, and fast convergence speed for the optimal interference suppression weights. Attached Figure Description

[0047] Figure 1 This is a flowchart of the interference detection process of the present invention.

[0048] Figure 2 This is a flowchart of the interference suppression process of the present invention.

[0049] Figure 3 This is a schematic diagram of the array antenna layout of the present invention. Detailed Implementation

[0050] To better illustrate the purpose and advantages of the present invention, the technical solution of the present invention will be further described below in conjunction with the accompanying drawings and examples.

[0051] To further reduce the problem size for the purpose of describing the algorithm, an orthogonal L-shaped matrix is ​​chosen as the input condition. However, this reduction in size does not affect the demonstration process in this example. The implementation process of the interference detection method based on long and short baselines is as follows: Figure 1 As shown, the specific steps are as follows:

[0052] Step 1: Preprocess the received signal;

[0053] The known array is composed of Composed of array elements, Where m is a positive integer. The M channels of RF analog signals received by the antenna array are down-converted and A / D quantized to obtain the M channels of intermediate frequency digital signals, which can be expressed as follows: .

[0054] Step 2: Phase difference calculation;

[0055] arrive This indicates the positions of the M antenna array elements. arrive and arrive Forming two short baselines, length , arrive and arrive Forming two long baselines, length , Integer and >1, The wavelength is the signal wavelength. A schematic diagram of the two sets of long and short baselines is shown below. Figure 3 As shown.

[0056] Using two sets of long and short baselines, the phase difference between the interference signal and the received signals of different antenna elements is obtained. The signal direction and phase difference are related as follows:

[0057] (1)

[0058] (2)

[0059] (3)

[0060] (4)

[0061] in , , and They are arrive , , and The phase difference measured at the baseline, The azimuth angle at which the interference signal is incident. The elevation angle for the incident interference signal.

[0062] Step 3: Deblurring is performed using long and short baselines;

[0063] Step 301: According to equations (5) and (6), we can obtain arrive and arrive Approximate value of baseline phase difference and :

[0064] (5)

[0065] ; (6)

[0066] Step 302: Establish an approximate value for the baseline phase difference. and Phase difference with actual measurement and The relationship between them:

[0067] , (7)

[0068] , ; (8)

[0069] Step 303: Change Find the closest approximation based on the value. , of , That is arrive and arrive The only precise value of the baseline phase difference and .

[0070] Step 4: Solve for the incident signal angle;

[0071] From the final phase difference and The azimuth angle of the incident interference signal can be obtained. and pitch angle They are respectively:

[0072] (9)

[0073] (10)

[0074] Step 5: Utilize the azimuth angle of the incident interference signal and pitch angle By constraining the two-dimensional array pattern of the received signal using an improved particle swarm optimization algorithm, the optimal weights for interference suppression within the spatial range are obtained. .

[0075] Improved particle swarm optimization algorithm processing flow as follows Figure 2 As shown, the specific steps are as follows:

[0076] Step 501: Initialize particle information:

[0077] The population size is determined to be N, meaning N particles are initialized. Each particle's position consists of M amplitudes and M phases, where the amplitudes are represented as... Phase is represented as The range of amplitude is The phase range is Randomly initialize the positions and velocities of N particles; corresponding to the first... The weight expression for the next iteration is: , is an M-dimensional complex vector; initially k=1.

[0078] Step 502: Calculate the fitness function:

[0079] Interference direction at distance exist and Setting the zero-depression depth value in the spatial region ; for array pattern The fitness function C2 with constraints is expressed as:

[0080] (11)

[0081] (12)

[0082] (13)

[0083] in, The main beamwidth is related to the number of array elements and the spacing between array elements. and The range of zero traps is characterized by its specific characteristics, which are determined based on the dynamic application scenario. and These are the defined sidelobe level and null depth, respectively;

[0084] The first The weight of the next iteration Substituting into formula (12), the result is calculated. ,Will Substituting into formula (11) yields the fitness function value for this iteration. By calculating the fitness function This yields the historical optimal position of the particle with the best fitness. This is the learning example, in which Represents particles Which particle's historical best value will be studied?

[0085] Step 503: Update particle velocity and position:

[0086] The particle growth rate and a probability-based perturbation factor are introduced to perturb the particles, giving the stagnant particles some momentum to search for new extreme values.

[0087] The growth rate of particles is expressed as The smoothing coefficient , and They represent particles respectively No. generation and first The extreme fitness value of an individual in a generation. This is a preset positive integer, determined by the convergence rate of the particles to be evaluated. The algorithm utilizes the historical best positions of all particles. Perform speed updates:

[0088] (14)

[0089] (15)

[0090] in, For the first Particle velocity in the next iteration For the first The current position of the particle in the next iteration, with a learning factor of [missing value]. , and The range is random numbers, It is inertial weight: ,in and These are the initial and final weights for the iteration, with values ​​of 0.4 and 0.9 respectively. The maximum number of iterations, , This is a preset threshold used to determine whether the particle growth rate meets the iteration requirements; This represents the position of the particle farthest from the origin. This represents the position of the particle closest to the origin.

[0091] Step 504: Determine whether the convergence condition is met;

[0092] Determine if the convergence condition is met. If the condition is not met, then let k = k + 1, return to step 502, and recalculate the fitness function value using the updated particle velocity and particle position. If the convergence condition is met, proceed to step 505.

[0093] Step 505: Output the optimal weights;

[0094] when When the algorithm converges after iterative iteration, the optimal particle position is the output of the particle swarm optimization algorithm. The first M values ​​are the amplitudes, and the last M values ​​are the phases. The optimal weights are composed of the amplitudes and phases. This is the optimal weight for spatial interference suppression.

[0095] Step 6: Weighting the received signal

[0096] Use optimal weights By weighting the received signal, the interference-suppressed navigation signal can be obtained.

[0097] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, the scope of protection of the present invention is not limited thereto. For those skilled in the art, several modifications and improvements can be made without departing from the principles of the present invention, and these should also be considered to fall within the scope of protection of the present invention.

Claims

1. A method for interference detection and suppression based on long and short baselines, characterized in that, It includes the following steps: Step 1: Given that the antenna array consists of It consists of several antenna array elements. m is a positive integer; The M radio frequency analog signals received by the antenna array are down-converted and A / D quantized to obtain M intermediate frequency digital signals; Step 2: arrive This indicates the positions of the M antenna array elements. arrive and arrive Forming two short baselines, length , arrive and arrive Forming two long baselines, length , Integer and >1, The signal wavelength is used; the phase difference between the interference signal and the received signals of different antenna elements is obtained using two sets of long and short baselines; （1） （2） （3） （4） in , , and They are arrive , , and The phase difference measured at the baseline, The azimuth angle at which the interference signal is incident. The elevation angle at which the interference signal is incident; Step 3: Deblurring is performed using long and short baselines; Step 301: Calculate based on equations (5) and (6) arrive and arrive Approximate value of baseline phase difference and : （5） ； （6） Step 302: Establish an approximate value for the baseline phase difference. and Phase difference with actual measurement and The relationship between them: ， （7） ， ； （8） Step 303: Change Find the closest approximation based on the value. and of and That is arrive and arrive Precise value of baseline phase difference and ; Step 4: Based on the final calculated phase difference and Calculate the azimuth angle of the incident interference signal and pitch angle They are respectively: （9） ； （10） Step 5: Utilize the azimuth angle of the incident interference signal and pitch angle By constraining the two-dimensional array pattern of the received signal using an improved particle swarm optimization algorithm, the optimal weights for interference suppression within the spatial range are obtained. ; Step 6: Use optimal weights By weighting the received signal, the interference-suppressed navigation signal can be obtained.

2. The method for interference detection and suppression based on long and short baselines according to claim 1, characterized in that, Step 5 is as follows: Step 501: Initialize particle information: Determine the population size as N, and each particle position consists of M amplitudes and M phases, where the amplitudes are represented as... Phase is represented as The range of amplitude is The phase range is Randomly initialize the positions and velocities of N particles; corresponding to the first... The weight expression for the next iteration is: , is an M-dimensional complex vector; initially k=1; Step 502: Calculate the fitness function: Use the disturbance obtained in step 4 to... Interference from distance exist and Setting the zero-depression depth value in the spatial region ; for array pattern The fitness function C2 with constraints is expressed as: （11） （12） （13） in, The main beamwidth is related to the number of antenna elements and the spacing between elements. and The range of zero traps is characterized by its specific characteristics, which are determined based on the dynamic application scenario. and These are the defined sidelobe level and null depth, respectively; The first The weight of the next iteration Substituting into formula (12), the result is calculated. ,Will Substituting into formula (11) yields the fitness function value for this iteration. By calculating the fitness function This yields the historical optimal position of the particle with the best fitness. This is the learning example, in which Represents particles Which particle's historical best value will be studied? Step 503: Update the particle velocity and position: Introduce the particle growth rate and a probability-based perturbation factor to perturb the particles, so that stagnant particles can gain momentum to search for new extreme values. The growth rate of particles is expressed as The smoothing coefficient , and They represent particles respectively No. generation and first The extreme fitness value of an individual in a generation. It is a preset positive integer, depending on the convergence speed of the particles to be evaluated; Utilizing the historical best positions of all particles Perform speed and location updates: （14） （15） in, For the first Particle velocity in the next iteration For the first The current position of the particle in the next iteration. As a learning factor, and The range is random numbers, These are inertial weights, used to balance the algorithm's local and global search capabilities. ,in and These are the initial and final weights for the iteration, respectively. The maximum number of iterations, , This is a preset threshold used to determine whether the particle growth rate meets the iteration requirements; This represents the position of the particle farthest from the origin. This represents the position of the particle closest to the origin. Step 504: Determine if the convergence condition is met. If the condition is not met, then let k = k + 1, return to step 502, and recalculate the fitness function value using the updated particle velocity and particle position. If the convergence condition is met, proceed to step 505. Step 505: When When the algorithm converges after iterative iteration, the optimal particle position is the output of the particle swarm optimization algorithm. The first M values ​​are the amplitudes, and the last M values ​​are the phases. The optimal weights are composed of the amplitudes and phases. This is the optimal weight for spatial interference suppression.

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