A spatial domain based anti-jamming method for iterative variable step size navigation receiver

Abstract

The present application relates to satellite navigation communication technical field, particularly to a kind of anti-interference method of navigation receiver based on space iteration variable step length navigation.This application includes the intermediate frequency signal after down-conversion of each road array element input of M-dimensional array antenna;Calculate stationary factor and error feedback factor;Get variable step length factor;Variable step length factor is normalized to obtain normalized iteration variable step length factor;Optimal weight vector is obtained by iteration in the way of stochastic gradient descent;The normalized iteration variable step length factor is brought into the iterative expression of optimal weight vector, and the iterative expression of optimal weight vector based on variable step length is obtained;Each optimal weight vector of iteration update is multiplied by the intermediate frequency signal after down-conversion to realize weighting processing, and each array output signal is obtained.This application realizes faster convergence speed and lower steady-state error, and effectively alleviates the influence of step length too fast reduction on steady-state error.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of satellite navigation communication technology, in particular to a space-based iterative variable step navigation receiver anti-jamming method. BACKGROUND

[0002] The distance between navigation satellites and the earth is far, and the signal will be attenuated in the transmission process. The actual power reaching the ground is only-155-160dBW, and at the same time, in the complex electromagnetic environment on the ground, the navigation signal is easily disturbed, thereby affecting the positioning accuracy of the receiver.

[0003] The performance of the adaptive anti-jamming algorithm is directly affected by the step size. When the traditional anti-jamming algorithm uses a fixed step size to iteratively solve the weight, it cannot adapt to the requirements of convergence speed and steady-state error at different iteration periods, often causing slow convergence speed or large steady-state error, and even making the system diverge. Therefore, many variable step anti-jamming algorithms are proposed to iteratively solve the weight. The main idea is to adjust the step size factor by constructing a functional relationship between the step size factor and the error signal, to ensure that a large step size is used in the early stage of the algorithm to speed up convergence, and then gradually reduce the step size to reduce the steady-state error. However, most of the variable step algorithms have the problem that the step size decreases too quickly in the convergence stage, resulting in a large steady-state error, which further affects the effectiveness of the algorithm in suppressing interference. In addition, most variable step algorithms need multiple simulations to adjust parameters to achieve optimal results, making the calculation complex and unreliable in actual scenarios. Figure 1 As shown in the figure, the step size of the fixed step algorithm and the variable step algorithm changes with the number of iterations. It can be seen that the fixed step size cannot adapt to the requirements of different iteration periods for step size, and the variable step algorithm has the problem that the step size decreases too quickly in the convergence stage.

[0004] In summary, the adaptive anti-jamming algorithm in the prior art has the following technical problems:

[0005] 1. The traditional fixed step algorithm cannot adapt to the requirements of convergence speed and steady-state error at different iteration periods when iteratively solving the weight, often causing slow convergence speed or large steady-state error, and even making the system diverge.

[0006] 2. Most variable step algorithms have the problem that the step size decreases too quickly in the convergence stage, resulting in a large steady-state error, which further affects the effectiveness of the algorithm in suppressing interference.

[0007] 3. At the same time, most existing variable step algorithms need multiple simulations to adjust parameters to achieve optimal results, making the calculation complex and unreliable in actual scenarios. SUMMARY

[0008] In view of the above-mentioned defects of the prior art, the purpose of the present application is to provide a space-based iterative variable step size navigation receiver anti-jamming method, which solves at least one of the technical problems existing in the prior art.

[0009] To achieve the above-mentioned purpose and other related purposes, the present application provides a space-based iterative variable step size navigation receiver anti-jamming method, comprising:

[0010] S1, down-converting the intermediate frequency signal of each path of the M-dimensional array antenna;

[0011] S2, calculating a stationary factor and an error feedback factor;

[0012] S3, obtaining a variable step size factor according to the stationary factor and the error feedback factor;

[0013] S4, normalizing the variable step size factor according to the down-converted intermediate frequency signal to obtain a normalized iterative variable step size factor;

[0014] S5, obtaining the optimal weight vector by random gradient descent based on the PI algorithm of the linearly constrained minimum variance criterion;

[0015] S6, bringing the normalized iterative variable step size factor into the iterative expression of the optimal weight vector, and replacing the fixed step size factor with the normalized iterative variable step size factor to obtain the iterative expression of the optimal weight vector based on the variable step size;

[0016] S7, multiplying each path of the optimal weight vector updated by iteration with the down-converted intermediate frequency signal to realize weighted processing and obtain each array output signal.

[0017] In an embodiment of the present application, the step S1 of down-converting the intermediate frequency signal of each path of the M-dimensional array antenna comprises:

[0018] The down-converted intermediate frequency signal received by each element is represented as: x(n)=[x1(n),x2(n),…xM(n)]T. M T ;

[0019] Wherein, x m (n)=S(n)+J(n)+N(n), S(n) is a navigation signal; J(n) is an interference signal, and N(n) is noise.

[0020] In an embodiment of the present application, the step S2 of calculating a stationary factor and an error feedback factor comprises:

[0021] ​The error feedback factor is Q(n) = k * Q(n-1) + (1-k) * |y(n) y(n-1)|, wherein 0 < k < 1, the stationary factor is C, and 0 < C < μ max Q(n-1) is an error feedback factor at the n-1th iteration, |y(n) y(n-1)| is an autocorrelation value of an output error signal, μ max is a maximum value of a selected fixed step.

[0022] In an embodiment of the present application, the step S3 of obtaining the variable step factor according to the stationary factor and the error feedback factor comprises:

[0023] The hyperbolic tangent function expression is: The variable step factor can be obtained by introducing the stationary factor C and the error feedback factor Q(n) based on the hyperbolic tangent function:

[0024]

[0025] |y(n)| is a modulus value of an output signal at the nth iteration, C is a stationary factor, and Q(n) is an error feedback factor.

[0026] In an embodiment of the present application, the step S4 of performing normalization processing on the variable step factor according to the down-converted intermediate frequency signal to obtain a normalized iteration variable step factor comprises:

[0027] The down-converted intermediate frequency signal received by each array element is used to calculate the real-time input signal power: δ 2 = x T (n) x(n), wherein x(n) is the down-converted intermediate frequency signal.

[0028] The variable step factor is normalized to obtain the normalized iteration variable step factor:

[0029] Wherein, ξ is a constant.

[0030] In an embodiment of the present application, the step S5 of the PI algorithm based on the linearly constrained minimum variance criterion, the optimal weight vector obtained by the random gradient descent method comprises:

[0031] Under the linear constraint condition, by adjusting the weight of other array elements, the array antenna forms a zero point in the interference direction, so as to minimize the total output power of the array, and the minimization of the total output power of the array is represented as:

[0032]

[0033] Wherein, w is a weight corresponding to each array element, R XXThe autocorrelation matrix of the down-converted intermediate frequency signal, The total output power E[|y(n)| 2 ] is the expectation of the square of the output signal module;

[0034] The solved minimum power cannot be zero, that is, all the weight values cannot be 0, and a constraint condition w H s = 1, s = [1, 0,..., 0] T , and w1 = 1 is obtained, that is, the first weight value of the array antenna remains unchanged and is always 1, a performance function L(w) is constructed by using the Lagrange multiplier method: L(w) = w H R XX w + λ(w H s-1), and the gradient of the performance function is taken, and ∇ w [L(w)] = 0, wherein ∇ w [L(w)] represents taking the gradient of the performance function;

[0035] The PI algorithm adopts a random gradient descent manner, the weight value is gradually iteratively approximated to the optimal weight value through the change in the negative gradient direction, and the iterative update of the weight value in the random gradient descent manner is represented as: w(n+1) = w(n)-μ▽ w [L(w)], and the constraint condition w(n+1) H s = 1, s = [1, 0,..., 0] T , and the iterative expression of the optimal weight vector is obtained as:

[0036]

[0037] , wherein w(n+1) is the weight vector at the n+1th iteration, w(n) is the weight vector at the nth iteration, I is a unit matrix, s is a constraint vector, μ is a fixed step factor, and the convergence condition is: , wherein tr(R xx ) represents the trace of the autocorrelation matrix of the input signal.

[0038] In an embodiment of the application, the normalized iterative variable step factor is brought into the iterative expression of the optimal weight vector in step S6, the fixed step factor is replaced by the normalized iterative variable step factor, and the iterative expression of the optimal weight vector based on the variable step length is obtained, and the iterative expression of the optimal weight vector based on the variable step length is:

[0039] The normalized iterative variable step factor is brought into the iterative expression of the optimal weight vector, the fixed step factor is replaced by the normalized iterative variable step factor, and the iterative expression of the optimal weight vector based on the variable step length is obtained, and the iterative expression of the optimal weight vector based on the variable step length is:

[0040]

[0041] In an embodiment of the present application, the weighted processing is realized by multiplying each of the optimal weight vector updated iteratively in step S7 with the down-converted intermediate frequency signal to obtain each array output signal, which includes:

[0042] The weight coefficient vector corresponding to each array element is represented as: w(n)=[w1(n),w2(n),…w M (n)] T The output signal of the antenna array after the weighted processing is represented as: y(n)=w H (n)x(n).

[0043] The present application also provides a space-based iterative variable step size navigation receiver anti-interference device, which includes:

[0044] An input module is configured to input a down-converted intermediate frequency signal to each array element of an M-dimensional array antenna.

[0045] A calculation module is configured to calculate a stationary factor and an error feedback factor.

[0046] A variable step size factor calculation module is configured to obtain a variable step size factor based on the stationary factor and the error feedback factor.

[0047] A processing module is configured to normalize the variable step size factor based on the down-converted intermediate frequency signal to obtain a normalized iterative variable step size factor.

[0048] An optimal weight vector acquisition module is configured to obtain an optimal weight vector iteratively by means of a random gradient descent based on a PI algorithm of a linearly constrained minimum variance criterion.

[0049] A replacement module is configured to replace the stationary step size factor with the normalized iterative variable step size factor in an iterative expression of the optimal weight vector to obtain an iterative expression of the optimal weight vector based on a variable step size.

[0050] A weighted processing module is configured to realize the weighted processing by multiplying each of the optimal weight vector updated iteratively with the down-converted intermediate frequency signal to obtain each array output signal.

[0051] As described above, the space-based iterative variable step size navigation receiver anti-interference method has the following beneficial effects:

[0052] (1) The space-based iterative variable step size navigation receiver anti-interference method of the present application combines the low error characteristics of the fixed step size algorithm in the steady state stage with the advantage of the variable step size algorithm using a large step size in the initial stage by introducing a smoothing factor and an error feedback factor, so that a large step size is used in the initial stage to accelerate convergence, and then the step size is gradually reduced to a constant small value to reduce the steady state error. While achieving faster convergence speed and lower steady state error, the influence of too fast step size reduction on the steady state error is effectively alleviated.

[0053] (2) The space-based iterative variable step size navigation receiver anti-interference method of the present application increases the irrelevance of the output error signal in the convergence stage, and the introduction of the error feedback factor enables the output error signal to maintain a faster convergence speed when it is irrelevant. Moreover, this method does not require multiple simulations to adjust the optimal parameters, has small calculation amount, and has high practical value.

[0054] (3) The space-based iterative variable step size navigation receiver anti-interference method of the present application can adaptively adjust the step size according to the input signal power after the step size factor is normalized, effectively dealing with the influence of signal power mutation on interference suppression effect. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 The step size and iteration number of the adaptive anti-interference algorithm in the prior art provided for the embodiments of the present application.

[0056] Figure 2 The flowchart of a space-based iterative variable step size navigation receiver anti-interference method provided for the embodiments of the present application. DETAILED DESCRIPTION

[0057] The embodiments of the present application are described below through specific concrete examples, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure in the specification. The present application can also be implemented or applied in different specific embodiments, and the details in the specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0058] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner, and only show the components related to the present application in the diagrams, not the number, shape and size of the components when actually implemented. The actual implementation of each component may be arbitrarily changed in type, number and proportion, and the layout pattern of the components may be more complex.

[0059] Terms such as first or second can be used to describe various components, but the components are not limited by the above terms. The above terms are used to distinguish one component from another component, for example, a first component can be referred to as a second component, and likewise, a second component can be referred to as a first component, without departing from the scope of the concept according to the present disclosure.

[0060] In addition, "connected / coupled" means that one component is electrically coupled directly to another component or indirectly electrically coupled through another component. The singular form can include the plural form as long as the context clearly indicates otherwise. In addition, "comprising / include" or "comprising / having" used in the present specification means that one or more components, steps, operations, and elements are present or have been added. The specific structure or function description of the example of the embodiment of the concept disclosed in the present specification is merely exemplified to describe the example of the embodiment according to the concept, and the example of the embodiment according to the concept can be implemented in various forms, but the description is not limited to the example of the embodiment described in the present specification.

[0061] According to the concept, various modifications and changes can be applied to the example of the embodiment, so that the example of the embodiment will be illustrated in the drawings and described in the specification. However, the example of the embodiment according to the concept is not limited to the specific embodiment, but includes all changes, equivalents, or alternatives included in the spirit and technical scope of the present disclosure.

[0062] It should be understood that when an element is described as "coupled" or "connected" to another element, it can be directly coupled or connected to the other element, or can be coupled or connected to the other element through a third element. Conversely, it should be understood that when an element is referred to as "directly connected to" or "directly coupled to" another element, no other element is interposed therebetween. Other expressions describing the relationship between components, i.e., "between" and "directly between" or "adjacent to" and "directly adjacent to", need to be interpreted in the same way.

[0063] The terms used in the present specification are used only to describe the specific example of the embodiment, and are not intended to limit the present disclosure. If there is no explicit contrary meaning in the context, the singular form can include the plural form. In the present specification, it should be understood that the term "include" or "have" indicates that the features, numbers, steps, operations, components, parts, or combinations thereof described in the specification are present, but does not preclude the possibility of adding one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0064] If not otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. If a term is defined differently in the present specification, it has the definition given in the present specification. If a term is defined in a commonly used dictionary, it should be interpreted as having a same meaning as the meaning in the context of the relevant technology, not as an ideal or overly formal meaning.

[0065] Descriptions of well-known components and processing techniques can be omitted so as not to unnecessarily obscure the embodiments of the present disclosure.

[0066] Throughout the specification, the same reference numerals refer to the same elements throughout the specification. Therefore, even if a reference numeral is not mentioned or described with reference to one drawing, it can be mentioned or described with reference to another drawing. In addition, even if a reference numeral is not shown in one drawing, it can be mentioned or described with reference to another drawing.

[0067] In addition, the logic level of a signal can be different from or opposite to the described logic level. For example, a signal described as having a logic "high" level can alternatively have a logic "low" level, and a signal described as having a logic "low" level can alternatively have a logic "high" level.

[0068] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. However, those of ordinary skill in the art can understand that, in the embodiments of the present disclosure, many technical details are presented in order to allow the reader to better understand the present disclosure. However, the technical solutions claimed by the present disclosure can be implemented even without these technical details and various changes and modifications based on the following embodiments.

[0069] Navigation signals are extremely susceptible to interference, which affects the positioning accuracy of the receiver, and therefore an effective anti-interference method is urgently needed. The intentional interference received by the navigation receiver mainly includes suppression interference and deception interference. Among them, suppression interference can interfere with all navigation signals covered by the frequency band, and is the most easily implemented interference method. The present disclosure mainly studies anti-interference against suppression interference. According to the characteristics of the interference signal, it can be divided into narrowband interference suppression and wideband interference suppression. The anti-interference technology of the navigation receiver is divided into single antenna technology and array antenna technology according to the number of antenna elements used. Since the single antenna technology has good suppression effect on a single narrowband interference, but when facing wideband interference and multiple narrowband interferences, its suppression effect is not satisfactory. However, the array antenna technology well solves the problems existing in the single antenna technology.

[0070] For this, the existing anti-jamming technology mostly adopts array antenna technology, which can realize adaptive filtering in space, controls the weighting coefficient of each array element according to the change of external environment, adjusts the shape of array directivity diagram adaptively without affecting the useful signal, forms a zero point in the array directivity diagram pointing to the interference direction, and thus realizes effective suppression of the interference signal. The whole antenna technology takes adaptive anti-jamming algorithm as the core, and common ones are sample matrix inverse algorithm (SMI), least mean square algorithm (LMS), recursive least square algorithm (RLS) and power inversion (PI) algorithm, among which the application of PI algorithm is the most extensive. It does not need any prior information, only needs to use the received signal, can automatically form a null in the interference incident direction, and the greater the interference strength, the deeper the corresponding null, while making the array output power minimum without attenuating the expected signal. Please refer to Figure 2 , Figure 2 The application provides a flow chart of an anti-interference method of an iterative variable step size navigation receiver based on space domain. The application provides an anti-interference method of an iterative variable step size navigation receiver based on space domain, which comprises the following steps:

[0071] Step S1: inputting the intermediate frequency signal after frequency down conversion to each array element of the M-dimensional array antenna.

[0072] The step S1 of inputting the intermediate frequency signal after frequency down conversion to each array element of the M-dimensional array antenna comprises the following steps:

[0073] Specifically, the signal received by each array element is the intermediate frequency signal after frequency down conversion, which is expressed as: x(n)=[x1(n),x2(n),…xM(n)]. M (n)] T ;

[0074] Wherein, x m (n) = S(n) + J(n) + N(n), S(n) is a navigation signal; J(n) is an interference signal, and N(n) is noise.

[0075] Step S2: calculating a stationary factor and an error feedback factor.

[0076] The error feedback factor is Q(n) = k x Q(n-1) + (1-k) x |y(n)y(n-1)|, wherein 0 max, Q(n-1) is the error feedback factor at the n-1th iteration, |y(n)y(n-1)| is the autocorrelation value of the output error signal, μ max is the maximum value of the selected fixed step size.

[0077] Step S3, obtaining a variable step size factor according to the stationary factor and the error feedback factor.

[0078] The general expression of the hyperbolic tangent function is: The variable step size factor can be obtained by introducing the stationary factor C and the error feedback factor Q(n) into the hyperbolic tangent function:

[0079]

[0080] Wherein, |y(n)| is the modulus value of the output signal at the nth iteration, C is the stationary factor, and Q(n) is the error feedback factor.

[0081] Step S4, normalizing the variable step size factor according to the down-converted intermediate frequency signal to obtain a normalized iterative variable step size factor.

[0082] The down-converted intermediate frequency signal received by each array element is used to calculate the real-time input signal power: δ 2 = x T (n)x(n), wherein x(n) is the down-converted intermediate frequency signal. In order to avoid x T (n)x(n) being too small to cause decimal division, a positive constant ξ is added to the real-time input signal power.

[0083] The variable step size factor is normalized to obtain the normalized iterative variable step size factor:

[0084]

[0085] The normalized iterative variable step size factor increases as the output signal increases, and decreases as the output signal decreases, following the adjustment principle of variable step size. When the output signal tends to zero, the step size factor tends to a minimum value At this time, it can be equivalent to a very small fixed step size, that is, when the algorithm tends to be stable, it can realize the characteristics of low steady-state error with fixed step size in the steady-state stage. Wherein, the larger the value of C is, the larger the normalized iterative variable step size factor is, and the faster the weight convergence speed is.

[0086] Step S5, the PI algorithm based on the linearly constrained minimum variance criterion iteratively obtains the optimal weight vector by means of stochastic gradient descent.

[0087] Specifically, under the linear constraint condition, the total output power of the array is minimized by adjusting the weight values of other array elements to form a null in the interference direction, and the minimization of the total output power of the array is expressed as:

[0088]

[0089] where w is the weight value corresponding to each array element, R XX is the autocorrelation matrix of the intermediate frequency signal after frequency down conversion, is the minimized total output power, E[|y(n| 2 ] is the expectation of the square of the modulus of the output signal;

[0090] In order to make the solution meaningful, the minimum power obtained by the solution cannot be zero, that is, the weight values of all paths cannot be zero, and therefore a constraint condition w H s=1, s=[1, 0, ···, 0] T is added. It is further obtained that w1=1, that is, the first path weight value of the antenna array remains unchanged and is always 1. According to the minimized total output power and the constraint condition described above, a performance function L(w) is constructed by using the Lagrange multiplier method: L(w)=w H R XX w+λ(w H s-1), and the gradient thereof is taken, and▽ w [L(w)]=0, where▽ w [L(w)] represents the gradient of the constructed performance function.

[0091] In order to avoid matrix inversion operation, the PI algorithm adopts a random gradient descent method, and the weight value is gradually iterated to approach the optimal weight value by changing in the negative gradient direction, and therefore the iterative update of the weight value in the random gradient descent method can be expressed as: w(n+1)=w(n)-μ▽ w [L(w)], and the constraint condition w(n+1) H s=1, s=[1, 0, ···, 0] T is added, and finally the iterative expression of the optimal weight vector is obtained as:

[0092]

[0093] where w(n+1) is the weight vector at the n+1th iteration, w(n) is the weight vector at the nth iteration, I is a unit matrix, s is a constraint vector, and μ is a fixed step factor. In order to ensure the convergence of the PI algorithm, the convergence condition is: where tr(R xx ) represents the trace of the autocorrelation matrix of the input signal.

[0094] Step S6, the normalized iteration variable step length factor is brought into the iteration expression of the optimal weight vector, and the fixed step length factor is replaced by the normalized iteration variable step length factor, so that the iteration expression of the optimal weight vector based on variable step length is obtained.

[0095] Specifically, the normalized iteration variable step length factor in step S4 is brought into the iteration expression of the optimal weight vector in step S5, and the fixed step length factor is replaced by the normalized iteration variable step length factor in step S4, so that the iteration expression of the optimal weight vector based on variable step length is finally obtained.

[0096]

[0097] Step S7, each array output signal is obtained by multiplying the optimal weight vector of each channel updated by iteration and the intermediate frequency signal after frequency conversion.

[0098] Specifically, the weight coefficient vector corresponding to each array element is represented as: w(n)=[w1(n),w2(n),…w M (n)] T The output signal after antenna array weight processing is represented as: y(n)=w H (n)x(n).

[0099] Specifically, since the fixed step length PI algorithm cannot well balance the contradiction between convergence speed and steady state error, when the fixed step length is large, fast convergence speed can be achieved but large steady state error is generated; when the fixed step length is small, small steady state error can be achieved but the convergence speed is slow. Therefore, various variable step length adjustment modes are proposed, that is, a large step length is used in the initial stage to speed up convergence, and then a small step length is gradually used to reduce the steady state error, but most of them have the problem of large steady state error caused by the rapid decrease of the step length in the convergence stage.

[0100] The application also provides an anti-interference device of a navigation receiver based on an iteration variable step length in space, which comprises:

[0101] An input module is configured to input an intermediate frequency signal after frequency conversion to each array element of an M-dimensional array antenna.

[0102] A calculation module is configured to calculate a stationary factor and an error feedback factor.

[0103] A variable step length factor calculation module is configured to obtain a variable step length factor according to the stationary factor and the error feedback factor.

[0104] A processing module is configured to normalize the variable step length factor according to the intermediate frequency signal after frequency conversion, so as to obtain a normalized iteration variable step length factor.

[0105] An optimal weight vector obtaining module is configured to obtain the optimal weight vector through a random gradient descent manner based on a PI algorithm of a linear constraint minimum variance criterion;

[0106] A substitution module is configured to bring the normalized iterative variable step length factor into an iterative expression of the optimal weight vector, and replace a fixed step length factor with the normalized iterative variable step length factor to obtain an iterative expression of the optimal weight vector based on a variable step length.

[0107] A weighting processing module is configured to multiply each of the optimal weight vectors updated iteratively with the intermediate frequency signal after frequency conversion to implement weighting processing and obtain an array output signal.

[0108] The application further provides an electronic device, which comprises a processor and a memory, and the memory stores program instructions, and the processor runs the program instructions to implement the above-mentioned space domain-based iterative variable step length navigation receiver anti-interference method. The processor can be a general processor, including a central processing unit (CPU), a network processor (NP), etc., and can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The memory can contain a random access memory (RAM) and can also contain a non-volatile memory (NVM), such as at least one disk memory. The memory can also be an internal memory of a random access memory (RAM) type, and the processor and the memory can be integrated into one or more independent circuits or hardware, such as an application specific integrated circuit (ASIC). It should be noted that the computer program in the above-mentioned memory can be realized in the form of a software functional unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the application can be embodied in the form of a software product, and the computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, an electronic device, or a network device, etc.) to execute all or part of the steps of the method of each embodiment of the application.

[0109] The application further provides a computer readable storage medium storing computer instructions for causing a computer to execute the above-mentioned anti-jamming method for a space-based iterative variable step size navigation receiver. The computer readable storage medium can be an electronic medium, a magnetic medium, an optical medium, an electromagnetic medium, an infrared medium, or a semiconductor system or a propagation medium. The computer readable storage medium can also include a semiconductor or solid state memory, a magnetic tape, a removable computer disk, a random access memory (RAM), a read-only memory (ROM), a hard disk, and an optical disk. The optical disk can include a compact disk-read only memory (CD-ROM), a compact disk-read / write (CD-RW), and a DVD.

[0110] In summary, the anti-jamming method for a space-based iterative variable step size navigation receiver of the present application combines the low error characteristics of the fixed step size algorithm in the steady state stage and the advantage of the variable step size algorithm using a large step size in the initial stage by introducing a smoothing factor and an error feedback factor. The large step size is used in the initial stage to accelerate convergence, and then the step size is gradually reduced to a constant small value to reduce the steady state error. The faster convergence speed and lower steady state error are achieved, and the influence of the too fast reduction of the step size on the steady state error is effectively alleviated.

[0111] The above-mentioned embodiments only exemplarily illustrate the principles and effects of the present application, and are not used to limit the present application. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes completed by those skilled in the art without departing from the spirit and technical thought of the present application should be covered by the claims of the present application.

Claims

1. A spatially-based, iterative, variable step size navigation receiver anti-jamming method characterized by, The method comprises the following steps: S1, down-converting the intermediate frequency signal input by each array element of an M-dimensional array antenna; S2, calculating a stationary factor and an error feedback factor; S3, obtaining a variable step size factor according to the stationary factor and the error feedback factor; S4, normalizing the variable step size factor according to the down-converted intermediate frequency signal to obtain a normalized iterative variable step size factor; S5, obtaining an optimal weight vector by means of random gradient descent according to a PI algorithm based on a linearly constrained minimum variance criterion; S6, bringing the normalized iterative variable step size factor into an iterative expression of the optimal weight vector, replacing a fixed step size factor with the normalized iterative variable step size factor, and obtaining an iterative expression of the optimal weight vector based on a variable step size; S7, multiplying each of the optimal weight vectors updated iteratively by the down-converted intermediate frequency signal to realize weighted processing and obtain an array output signal. The step S2 of calculating the stationary factor and the error feedback factor comprises the following steps: the error feedback factor is wherein, the stationary factor is , , is the error feedback factor at the n-1th iteration, is the autocorrelation value of the output error signal, is the maximum value of the selected fixed step size; The step S3 of obtaining the variable step size factor according to the stationary factor and the error feedback factor comprises the following steps: The hyperbolic tangent function expression is: The stationary factor is introduced on the basis of the hyperbolic tangent function And the error feedback factor That is, the variable step size factor can be obtained ， wherein is the output signal of the th iteration, is the smoothing factor, is the error feedback factor.

2. The anti-jamming method based on spatially iterative variable step size navigation receiver of claim 1, wherein, The step S1 of down-converting the intermediate frequency signal input by each array element of the M-dimensional array antenna comprises the following steps: The down-converted intermediate frequency signals received by each array element are represented as: ; wherein , is a navigation signal; is an interference signal, is noise.

3. The anti-jamming method for spatially-based iterative variable step size navigation receiver of claim 1, wherein, The step S4 of normalizing the variable step size factor according to the down-converted intermediate frequency signal to obtain the normalized iterative variable step size factor comprises the following steps: Real-time input signal power is calculated using the down-converted intermediate frequency signals received by each array element: wherein is the down-converted intermediate frequency signal; The step S4 of normalizing the variable step size factor according to the down-converted intermediate frequency signal to obtain the normalized iterative variable step size factor comprises the following steps: wherein, is a constant.

4. The anti-jamming method for a spatially-based iterative variable step size navigation receiver of claim 3 wherein, The step S5 of obtaining the optimal weight vector by means of random gradient descent according to the PI algorithm based on the linearly constrained minimum variance criterion comprises the following steps: Under the linear constraint condition, the weight of other array elements is adjusted to form a zero point in the interference direction of the array antenna, so as to minimize the total output power of the array, and the minimization of the total output power of the array is represented as: wherein, is a weight corresponding to each array element, is an autocorrelation matrix of the down-converted intermediate frequency signal, is the minimized total output power, is an expectation of the square of the output signal modulus; The minimum power to be solved cannot be zero, that is, each path weight cannot be all 0, and a constraint condition needs to be added , , it is obtained that the first path weight of the array antenna remains unchanged and is always 1, a performance function is constructed by using a Lagrange multiplier method: , and the gradient of the performance function is taken, and =0, wherein indicates that the gradient of the performance function is taken; The PI algorithm adopts a random gradient descent manner, and gradually iteratively approaches the optimal weight value by changing in a negative gradient direction. The iterative update of the weight value in the random gradient descent manner is represented as: , and the constraint condition is added , , and the iterative expression of the optimal weight vector is obtained as ， wherein is the weight vector at the n-th iteration, is the weight vector at the n-th iteration, is the weight vector at the n-th iteration, is the weight vector at the n-th iteration, is the identity matrix, is the constraint vector, is a fixed step size factor, and the convergence condition is: wherein denotes the trace of the autocorrelation matrix of the input signal.

5. The anti-jamming method for a spatially-based iterative variable step size navigation receiver of claim 4 wherein, The step S6 of bringing the normalized iterative variable step size factor into the iterative expression of the optimal weight vector, replacing the fixed step size factor with the normalized iterative variable step size factor, and obtaining the iterative expression of the optimal weight vector based on the variable step size comprises the following steps: The step S6 of bringing the normalized iterative variable step size factor into the iterative expression of the optimal weight vector, replacing the fixed step size factor with the normalized iterative variable step size factor, and obtaining the iterative expression of the optimal weight vector based on the variable step size comprises the following steps: 。 6. The anti-jamming method for a spatially-based iterative variable step size navigation receiver of claim 5 wherein, The step S7 of multiplying each of the optimal weight vectors updated iteratively by the down-converted intermediate frequency signal to realize weighted processing and obtain an array output signal comprises the following steps: The weighting coefficient vector corresponding to each array element is represented as: The output signal of the antenna array after weighting processing is represented as: .

7. A spatially-based, iterative, variable step size navigation receiver anti-jam apparatus characterized by, The method comprises the following steps: An input module is configured to down-convert the intermediate frequency signal input by each array element of an M-dimensional array antenna; A calculation module is configured to calculate a stationary factor and an error feedback factor; A variable step size factor calculation module is configured to obtain a variable step size factor according to the stationary factor and the error feedback factor; A processing module is configured to normalize the variable step size factor according to the down-converted intermediate frequency signal to obtain a normalized iterative variable step size factor; An optimal weight vector acquisition module is configured to obtain an optimal weight vector by means of random gradient descent according to a PI algorithm based on a linearly constrained minimum variance criterion. The replacement module is used for bringing the normalized iterative variable step length factor into an iterative expression of the optimal weight vector, replacing a fixed step length factor with the normalized iterative variable step length factor, and obtaining an iterative expression of the optimal weight vector based on a variable step length. The weighting processing module is used for multiplying each of the optimal weight vectors updated iteratively with the intermediate frequency signals after frequency down-conversion to realize weighting processing, and obtaining array output signals. The calculation module is used for calculating a stationary factor and an error feedback factor, and includes: the error feedback factor is wherein, the smoothing factor is , , is the error feedback factor at the n-1th iteration, is the autocorrelation value of the output error signal, is the maximum value of the selected fixed step size; The variable step length factor calculation module is used for obtaining a variable step length factor according to the stationary factor and the error feedback factor, and includes: The variable step length factor calculation module is used for obtaining a variable step length factor according to the stationary factor and the error feedback factor, and includes: The hyperbolic tangent function expression is: The stationary factor is introduced on the basis of the hyperbolic tangent function And the error feedback factor That is, the variable step size factor can be obtained ， wherein is the output signal of the th iteration, is the smoothing factor, is the error feedback factor.

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