A passive detection method, device and electronic equipment for non-Gaussian noise environments

Abstract

This invention discloses a passive detection method, apparatus, and electronic device in a non-Gaussian noise environment. The method includes constructing an M-dimensional probability density function of the Rayleigh distribution of the received signal; obtaining a first probability density function of the received signal under assumption H1 and a second probability density function under assumption H0; assumption H1 represents the assumption that the received signal includes a radiating source signal; assumption H0 represents the assumption that the received signal does not include a radiating source signal; based on the first and second probability density functions, a generalized likelihood ratio (GLR) test formula is obtained according to the GLR criterion; the derivative of the M-dimensional probability density function under assumption H1 is taken to obtain a maximum likelihood estimate of the product of the channel coefficient and the Doppler frequency shift; the maximum likelihood estimate is substituted into the GLR test formula to obtain a test calculation value; and the test calculation value is compared with a threshold value to achieve passive detection in the absence of a reference signal through a GLR test algorithm based on the test assumption.

Description

Technical Field

[0001] This invention belongs to the field of signal processing technology, specifically relating to a passive detection method, device, and electronic equipment in a non-Gaussian noise environment. Background Technology

[0002] In today's complex and ever-changing electronic warfare environment, external radiation source radar, particularly third-party signal transmission equipment, plays a crucial role, making radiation source detection paramount. Furthermore, with advancements in science and technology and the widespread use of electronic products, the electromagnetic environment in space is becoming increasingly complex. During detection, this complex electromagnetic environment can severely interfere with the detection results, affecting the accuracy of radiation source detection.

[0003] Currently, energy detection and matched filtering are the most widely used classical signal detection methods both domestically and internationally. Energy detection is a non-coherent signal detection method that determines the presence of a signal by comparing and judging the energy of the signal within a specific time period with a pre-set threshold. Cross-correlation detection is based on matched filtering theory, which accumulates target energy using the ambiguity function of the reference signal and the target echo signal to obtain target time-delay Doppler information for signal detection. However, the reference signal in matched filtering requires direct wave purification. Therefore, the acquisition and purification of the direct wave signal are crucial for detection. If a scenario exists where the direct wave cannot be received, the detection performance will drastically decrease, severely limiting the application environment of external radiation source radar. Therefore, research on external radiation source detection under zero direct wave conditions is essential. This research can not only overcome the limitations of direct wave purification technology but also make external radiation source radar detection systems applicable to more application scenarios, thus providing a theoretical basis and technical support for the development of my country's next-generation distributed external radiation source detection equipment.

[0004] However, the aforementioned traditional detection techniques cannot quickly detect low signal-to-noise ratio and blind signals in complex electromagnetic environments, and require a reference signal. Therefore, weak radiation source detection technology under Rayleigh noise background still faces many difficulties. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention provides a passive detection method, apparatus, and electronic device for non-Gaussian noise environments.

[0006] The technical problem to be solved by this invention is achieved through the following technical solution:

[0007] In a first aspect, the present invention provides a passive detection method for non-Gaussian noise environments, the passive detection method comprising:

[0008] Acquire received signals;

[0009] Construct the M-dimensional probability density function of the Rayleigh distribution of the received signal;

[0010] Obtain the first probability density function of the received signal under assumption H1 and the second probability density function of the received signal under assumption H0; the assumption H1 represents the assumption that the received signal includes a radiation source signal; the assumption H0 represents the assumption that the received signal does not include a radiation source signal.

[0011] Based on the first probability density function and the second probability density function, the generalized likelihood ratio test formula is obtained according to the generalized likelihood ratio criterion.

[0012] By differentiating the M-dimensional probability density function under assumption H1, the maximum likelihood estimate of the product of the channel coefficients and the Doppler frequency shift of the received signal is obtained.

[0013] Substituting the maximum likelihood estimate into the generalized likelihood ratio test formula yields the test value;

[0014] The calculated test value is compared with a preset threshold value to achieve passive detection.

[0015] Optionally, the M-dimensional probability density function is:

[0016]

[0017] Where f(y) represents the M-dimensional probability density function; y n The received signal represents the nth dimension; N represents the total number of dimensions of the received signal; M represents the total number of variables in the received signal; σ 2 denoted by variance; R represents the distance difference between the direct wave and the echo; y represents the received signal; the superscript H represents the conjugate transpose of the matrix.

[0018] Optionally, the first probability density function is:

[0019]

[0020] Where f(y|H1) represents the first probability density function; y n The received signal represents the nth dimension; N represents the total number of dimensions of the received signal; α represents the product of the channel coefficient and the Doppler frequency shift of the received signal; s n y represents the radiation source signal in the nth dimension; y represents the received signal; s represents the radiation source signal; R represents the distance difference between the direct wave and the echo; the superscript H represents the conjugate transpose operation of the matrix.

[0021] Optionally, the second probability density function is:

[0022]

[0023] Where f(y|H0) represents the second probability density function; y n The received signal represents the nth dimension; N represents the total number of dimensions of the received signal; y represents the received signal; R represents the distance difference between the direct wave and the echo; the superscript H represents the conjugate transpose of the matrix.

[0024] Optionally, the generalized likelihood ratio test formula is:

[0025]

[0026] Where f(y|H1) represents the first probability density function; f(y|H0) represents the second probability density function; and ζ represents the preset threshold value.

[0027] Optionally, a comparison is made between the calculated test value and a preset threshold value to achieve passive detection, including:

[0028] When the calculated test value is greater than or equal to the preset threshold value, it is determined that the received signal contains a radiation source signal;

[0029] When the calculated test value is less than the preset threshold value, it is determined that the received signal does not contain a radiation source signal.

[0030] Secondly, the present invention provides a passive detection device for non-Gaussian noise environments, the passive detection device comprising:

[0031] The first acquisition module is used to acquire the received signal;

[0032] The construction module is used to construct the M-dimensional probability density function of the Rayleigh distribution of the received signal;

[0033] The second acquisition module is used to acquire the first probability density function of the received signal under assumption H1 and the second probability density function of the received signal under assumption H0; the assumption H1 represents the assumption that the received signal includes a radiation source signal; the assumption H0 represents the assumption that the received signal does not include a radiation source signal.

[0034] The generalized likelihood ratio test formula construction module is used to obtain the generalized likelihood ratio test formula based on the first probability density function and the second probability density function, according to the generalized likelihood ratio criterion.

[0035] The maximum likelihood estimation determination module is used to differentiate the M-dimensional probability density function under the H1 assumption to obtain the maximum likelihood estimate of the product of the channel coefficients and the Doppler frequency shift of the received signal.

[0036] The test calculation value determination module is used to substitute the maximum likelihood estimate into the generalized likelihood ratio test formula to obtain the test calculation value;

[0037] The passive detection implementation module is used to compare the calculated test value with a preset threshold value to achieve passive detection.

[0038] Optionally, the M-dimensional probability density function is:

[0039]

[0040] Where f(y) represents the M-dimensional probability density function; y n The received signal represents the nth dimension; N represents the total number of dimensions of the received signal; M represents the total number of variables in the received signal; σ 2 denoted by variance; R represents the distance difference between the direct wave and the echo; y represents the received signal; the superscript H represents the conjugate transpose of the matrix.

[0041] Optionally, the first probability density function is:

[0042]

[0043] Where f(y|H1) represents the first probability density function; y n The received signal represents the nth dimension; N represents the total number of dimensions of the received signal; α represents the product of the channel coefficient and the Doppler frequency shift of the received signal; s n y represents the radiation source signal in the nth dimension; y represents the received signal; s represents the radiation source signal; R represents the distance difference between the direct wave and the echo; the superscript H represents the conjugate transpose operation of the matrix.

[0044] Optionally, the second probability density function is:

[0045]

[0046] Where f(y|H0) represents the second probability density function; y n The received signal represents the nth dimension; N represents the total number of dimensions of the received signal; y represents the received signal; R represents the distance difference between the direct wave and the echo; the superscript H represents the conjugate transpose of the matrix.

[0047] Optionally, the generalized likelihood ratio test formula is:

[0048]

[0049] Where f(y|H1) represents the first probability density function; f(y|H0) represents the second probability density function; and ζ represents the preset threshold value.

[0050] Optionally, a passive detection implementation module is specifically used to determine that the received signal contains a radiation source signal when the test calculation value is greater than or equal to the preset threshold value; and to determine that the received signal does not contain a radiation source signal when the test calculation value is less than the preset threshold value.

[0051] Thirdly, the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0052] Memory, used to store computer programs;

[0053] When the processor executes the program stored in the memory, it implements the steps of the passive detection method described in any of the above-mentioned non-Gaussian noise environments.

[0054] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the passive detection method under any of the above-described non-Gaussian noise environments.

[0055] This invention provides a passive detection method in a non-Gaussian noise environment. First, it obtains the first probability density function of the received signal under assumption H1 and the second probability density function of the received signal under assumption H0. Assumption H1 represents the assumption that the received signal includes a radiating source signal; assumption H0 represents the assumption that the received signal does not include a radiating source signal. Based on this, a generalized likelihood ratio (GMR) test formula is obtained using the GMR criterion. The maximum likelihood estimate of the product of the channel coefficient (obtained by differentiating the M-dimensional probability density function of the Rayleigh distribution of the received signal under assumption H1) and the Doppler frequency shift is substituted into the GMR test formula to obtain the test calculation value. Passive detection is achieved by comparing the test calculation value with a preset threshold value. In the absence of a reference signal, the detection of weak radiating source signals is realized through a GMR test algorithm based on the test assumptions.

[0056] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating a passive detection method in a non-Gaussian noise environment provided by an embodiment of the present invention.

[0058] Figure 2This is a schematic diagram illustrating the application scenario of the passive detection method in a non-Gaussian noise environment provided in this embodiment of the invention;

[0059] Figure 3 This is a schematic diagram of the ZMNL algorithm provided in an embodiment of the present invention;

[0060] Figure 4 This is a simulation schematic diagram of the passive detection method provided in the embodiments of the present invention;

[0061] Figure 5 This is a schematic diagram of Rayleigh distributed noise simulation provided in an embodiment of the present invention;

[0062] Figure 6 This is a schematic diagram showing the relationship between detection performance and signal-to-noise ratio and signal length;

[0063] Figure 7 This is a schematic diagram of the DTMB comparison experiment results provided in the embodiments of the present invention;

[0064] Figure 8 This is a schematic diagram of the experimental results of comparing linear frequency modulated signals provided in an embodiment of the present invention;

[0065] Figure 9 This is a schematic diagram of the structure of a passive detection device in a non-Gaussian noise environment provided in an embodiment of the present invention;

[0066] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0067] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0068] To address the limitations of traditional detection techniques in rapidly detecting low signal-to-noise ratio and blind signals in complex electromagnetic environments, while also requiring a reference signal, this invention provides a passive detection method for non-Gaussian noise environments. (See [link to relevant documentation]). Figure 1 , Figure 1 This is a flowchart illustrating a passive detection method in a non-Gaussian noise environment provided by an embodiment of the present invention, specifically including the following steps:

[0069] Step S101: Obtain the received signal.

[0070] See Figure 2 , Figure 2This is a schematic diagram illustrating an application scenario of the passive detection method in a non-Gaussian noise environment provided in this embodiment of the invention. The received signal refers to the echo signal received by the receiving station from the radiation source. The receiving station can be a base station or a signal monitoring station, etc. The received signal can be modeled by setting relevant parameters of the receiving station, which may include the number of receiving stations K, the sampling frequency f, etc. s And the initial phase is equal.

[0071] Step S102: Construct the M-dimensional probability density function of the Rayleigh distribution of the received signal.

[0072] In this embodiment of the invention, considering that Rayleigh fading is considered the most typical small-scale fading under non-Gaussian noise conditions, this embodiment of the invention studies the passive detection problem under Rayleigh noise conditions and constructs the M-dimensional probability density function of the Rayleigh distribution of the received signal.

[0073] The specific design process of the M-dimensional probability density function will be explained below:

[0074] (1) Power spectrum simulation:

[0075] The expression for the exponential power spectrum is determined as follows:

[0076]

[0077] Where W(f) represents the exponential power spectrum; a is a constant whose value should be such that W(f) = W(f) / a. 3dB / 2)=0.5, therefore f represents frequency; f d f represents the Doppler center frequency, i.e., the mean Doppler frequency; 3dB This indicates the half-power point bandwidth.

[0078] The power spectral density S is obtained from the exponential power spectrum. x (ω), S x The first complex stationary random sequence of (ω) is x(n). x(n) can be passed through a linear time-invariant system of a linear phase filter H(ω) to obtain the second complex stationary random sequence y(n), thus yielding the power spectral density S of y(n). y (ω), as follows:

[0079] Sy(ω)=|H(ω) 2 Sx(ω);

[0080] Wherein, the noise power spectral density S y (ω) represents the Gaussian spectrum; x(n) follows the N(μ,σ) spectrum. 2 Gaussian white noise, i.e., S x (ω)=σ 2ω is the angular frequency, ω = 2πf; μ represents the expected value; σ 2 Indicates variance.

[0081] In this embodiment of the invention, a complex stationary random sequence is a signal sequence with stationary properties, wherein the value of the signal is in complex form. The stationary properties mainly include the following: 1. The mean is constant, that is, the expected value of the sequence is equal at any point in time; 2. The autocovariance of the sequence is only related to the time interval and has no relation to the point in time; 3. The power spectral density is only related to the frequency and is constant with time.

[0082] Let Sy(f) = σ²|H(f) 2 Transform Sy(ω) into S y (f)=σ 2 H(f) 2 =exp[-(α(ff)] d ) / f 3dB ) 2 ], by letting f d =0 allows us to solve for H(f) and obtain α represents the product of the channel coefficient of the received signal and the Doppler frequency shift; σ represents the standard deviation.

[0083] Passing the random sequences v1 and v2 that follow N(0,1) through the designed filter H(f), the outputs u1 and u2 are the correlated Gaussian sequences with Gaussian power spectral density.

[0084] (2) Simulated environmental noise:

[0085] Generate random numbers λ1, λ2 that follow a uniform distribution in (0,1) and numbers that follow a distribution in N(μ,σ). 2 Mutually orthogonal Gaussian white noise sequences v3 and v4:

[0086]

[0087] Then, a linear phase filter H(ω) is designed using the given clutter spectral density. Finally, in this embodiment of the invention, the noise amplitude... It follows a Rayleigh distribution.

[0088] For details, see Figure 3 , Figure 3 This is a schematic diagram of the ZMNL algorithm provided in this embodiment of the invention. ZMNL (Zero Memory Nonlinearity) is an algorithm for generating random sequences. First, a random sequence, i.e., a Gaussian white noise sequence v, is generated. i v ~ N(0,1), v i power density spectrum S v(ω)=1, the output noise sequence w after processing by the linear phase filter H(ω) is... i S w (ω)=H(ω) 2 The output z is obtained through nonlinear transformation G(·). i Among them, the linear phase filter determines the power spectral density of the output noise, and G(·) determines the amplitude probability density of the noise.

[0089] See Figure 4 , Figure 4 This is a simulation schematic diagram of the passive detection method provided in this embodiment of the invention, where v 1,i and v 2,i Output u via H(z) 1,i and u 2,i v 1,i and v 2,i Both are two independent Gaussian noise sequences that conform to the N(0,1) distribution; u 1,i and u 2,i This represents the output after passing through a linear phase filter; i represents the channel. σ is introduced. s 2 with u 1,i and u 2,i Output w by dot product respectively 1,i and w 2,i and respectively w 1,i and w 2,i After squaring, sum the results, take the square root of the sum, and output z. i . w represents variance. 1,i and w 2,i Both represent uniformly distributed noise multiplied by variance; z i This indicates the final output.

[0090] Construct a signal model for the signal monitoring station based on the actual scenario.

[0091] x k (n)=b k s(nl k )exp(jΩ k n)+w k (n);

[0092] Where, x k (n) represents the Gaussian white noise of the received signal in the nth dimension received by the kth signal monitoring station; k = 1, 2, ..., K, b k Let l represent the loss coefficient of the received signal echo at the k-th signal monitoring station. k Ω represents the time delay of the k-th signal monitoring station. kThis represents the Doppler frequency difference. The noise signal is Rayleigh noise. k (n); s represents the radiation source signal; n represents the dimension of the received signal as the nth dimension; j represents a complex number.

[0093] Therefore, the M-dimensional probability density function of the Rayleigh distribution of the received signal can be calculated as follows:

[0094]

[0095] Where f(y) represents an M-dimensional probability density function; y n This represents the received signal in the nth dimension; N represents the total number of dimensions in the received signal; M represents the total number of variables in the received signal; σ 2 y represents the variance; R represents the distance difference between the direct wave and the echo; y represents the received signal; the superscript H represents the conjugate transpose of the matrix.

[0096] Step S103: Obtain the first probability density function of the received signal under assumption H1 and the second probability density function of the received signal under assumption H0; assumption H1 represents the assumption that the received signal includes the radiation source signal; assumption H0 represents the assumption that the received signal does not include the radiation source signal.

[0097] Hypothesis H1 and hypothesis H0 are two hypotheses in hypothesis testing. Hypothesis H0 is the null hypothesis, and hypothesis H1 is the alternative hypothesis.

[0098] In this embodiment of the invention, the received signal may be of two types: one is a received signal that includes the radiation source signal, and the other is a received signal that does not include the radiation source signal. Therefore, it is assumed that H1 is the received signal that includes the radiation source signal; and it is assumed that H0 is the received signal that does not include the radiation source signal.

[0099] In this context, radiation source signal refers to the signal emitted by a radiation source for transmitting information or conducting communication. Radiation sources can be devices such as electromagnetic wave transmitters, radio stations, and satellites.

[0100] In this embodiment of the invention, the radiation source signal can be represented as the product of the complex envelope of the emitted signal and its phase change portion, as specifically expressed below:

[0101]

[0102] Where s(n) represents the radiation source signal in the nth dimension; u(n) represents the complex envelope of the radiation signal source in the nth dimension; f c The carrier frequency of the transmitted signal is represented by j; a complex number is represented by t; and time is represented by t. This indicates the initial phase of the transmitted signal.

[0103] In this embodiment of the invention, the expression for the first probability density function of the received signal under assumption H1 is:

[0104]

[0105] Where f(y|H1) represents the first probability density function; y n Let represent the received signal in the nth dimension; N represents the total number of dimensions of the received signal; α represents the product of the channel coefficients and the Doppler frequency shift of the received signal; s n y represents the radiation source signal in the nth dimension; s represents the received signal; R represents the distance difference between the direct wave and the echo; the superscript H represents the conjugate transpose of the matrix.

[0106] In this embodiment of the invention, the expression for the second probability density function of the received signal under assumption H0 is:

[0107]

[0108] Where f(y|H0) represents the second probability density function; y n y represents the received signal in the nth dimension; N represents the total number of dimensions of the received signal; y represents the received signal; R represents the distance difference between the direct wave and the echo; the superscript H represents the conjugate transpose of the matrix.

[0109] Step S104: Based on the first probability density function and the second probability density function, the generalized likelihood ratio test formula is obtained according to the generalized likelihood ratio criterion.

[0110] In this embodiment of the invention, after obtaining the first probability density function and the second probability density function, the generalized likelihood ratio test formula can be obtained based on the generalized likelihood ratio. Specifically, the generalized likelihood ratio test formula is constructed by comparing the probability density functions under the two hypotheses.

[0111] In this embodiment of the invention, the generalized likelihood ratio test formula is:

[0112]

[0113] Where ζ represents the critical value, that is, the preset threshold value.

[0114] Step S105: Under the H1 assumption, differentiate the M-dimensional probability density function to obtain the maximum likelihood estimate of the product of the channel coefficients and the Doppler frequency shift of the received signal.

[0115] In this embodiment of the invention, by differentiating the M-dimensional probability density function under the H1 assumption, the maximum likelihood estimate of the product α of the channel coefficient and the Doppler frequency shift of the received signal is obtained as follows:

[0116]

[0117] Where x represents the Gaussian white noise of the received signal.

[0118] Step S106: Substitute the maximum likelihood estimate into the generalized likelihood ratio test formula to obtain the test value.

[0119] In this embodiment of the invention, the generalized likelihood ratio test formula is obtained in step S104, and the maximum likelihood estimate of the product α of the channel coefficient and the Doppler frequency shift of the received signal is calculated in step S105. Then, the maximum likelihood estimate of α is substituted into the generalized likelihood ratio test formula to obtain the test calculation value, including:

[0120]

[0121] Step S107: Compare the calculated test value with the preset threshold value to achieve passive detection.

[0122] Passive detection is a technology that detects targets by receiving electromagnetic radiation that is already present in the environment, without emitting any electromagnetic waves.

[0123] In this embodiment of the invention, passive detection can be performed by comparing the calculated test value with a preset threshold value, as follows:

[0124] When the calculated value is greater than or equal to the preset threshold, it is determined that the received signal contains a radiation source signal.

[0125] When the calculated value is less than the preset threshold, it is determined that the received signal does not contain a radiation source signal.

[0126] In this embodiment of the invention, the relationship between the calculated value and the threshold value is examined to determine whether the received signal contains a radiation source, thereby providing a basis for subsequent signal processing and analysis.

[0127] Specifically, the preset threshold value can be set by technical personnel based on theory or practical experience, and there are no restrictions here.

[0128] In this embodiment of the invention, the first probability density function of the received signal under assumption H1 and the second probability density function of the received signal under assumption H0 are first obtained. Here, assumption H1 represents the assumption that the received signal includes a radiation source signal; assumption H0 represents the assumption that the received signal does not include a radiation source signal. Based on this, a generalized likelihood ratio test formula is obtained using the generalized likelihood ratio. The maximum likelihood estimate of the product of the channel coefficient obtained by differentiating the M-dimensional probability density function of the Rayleigh distribution of the received signal under assumption H1 and the Doppler frequency shift is substituted into the generalized likelihood ratio test formula to obtain the test calculation value. By comparing the test calculation value with a preset threshold value, passive detection is achieved. In the absence of a reference signal, the detection of weak radiation source signals is realized through the generalized likelihood ratio test algorithm based on the test assumption.

[0129] The simulation experiment of the passive detection method in a non-Gaussian noise environment provided by the embodiments of the present invention is as follows. The parameters set in the simulation experiment are shown in Table 1, and the signal-to-noise ratio is -70dB to 10dB, with each SNR change interval being 1dB. The signal lengths are 1ms, 10ms, 0.1s, and 1s, respectively. The experiment was repeated 1000 times for verification.

[0130] Table 1

[0131]

[0132] See Figure 5 and Figure 6 , Figure 5 This is a schematic diagram of Rayleigh noise simulation provided in an embodiment of the present invention. Figure 5 (a) in the diagram is a time-domain plot of the noise, showing the time-domain waveform of Rayleigh noise. The horizontal axis represents the number of points, and the vertical axis represents the amplitude. Figure 5 (b) in the figure is a noise amplitude distribution diagram, which shows the noise amplitude distribution. The horizontal axis represents the amplitude and the vertical axis represents the probability density. Figure 6 This is a schematic diagram showing the relationship between detection performance and signal-to-noise ratio and signal length. Figure 6 In (a), (b), (c), and (d), the signal lengths are 1 s, 0.1 s, 0.01 s, and 0.001 s, respectively. The horizontal axis represents the signal-to-noise ratio (SNR) in decibels (dB), and the vertical axis represents the detection probability. As the SNR increases, the detection probability also increases, achieving good detection results at SNRs above -50 dB. Furthermore, longer signal lengths result in more stable detection, indicating that increasing signal length and improving SNR significantly enhance detection accuracy. In the absence of a reference signal, a generalized likelihood ratio (GPR) test algorithm based on the test hypothesis was used to detect weak radiation source signals, and this algorithm is applicable to low SNR situations.

[0133] See Figure 7 and Figure 8 , Figure 7 This is a schematic diagram of the DTMB comparison experiment results provided in the embodiments of the present invention. Figure 8 This is a schematic diagram of the experimental results of comparing linear frequency modulated signals provided in an embodiment of the present invention. Figure 7 and Figure 8 The horizontal axis represents the signal-to-noise ratio (SNR) in decibels, and the vertical axis represents the detection probability. Figure 7 and Figure 8 The passive detection method provided in the embodiments of the present invention is compared with existing time delay estimation algorithms and traditional kurtosis algorithms. The simulation results show that the detection probability of the passive detection method provided in the embodiments of the present invention is significantly greater than that of existing algorithms.

[0134] Based on the same inventive concept, this invention also provides a passive detection device for non-Gaussian noise environments, see [link to relevant documentation]. Figure 9 , Figure 9 This is a schematic diagram of a passive detection device for non-Gaussian noise environments provided in an embodiment of the present invention. The passive detection device includes:

[0135] The first acquisition module 901 is used to acquire the received signal;

[0136] Construction module 902 is used to construct the M-dimensional probability density function of the Rayleigh distribution of the received signal;

[0137] The second acquisition module 903 is used to acquire the first probability density function of the received signal under assumption H1 and the second probability density function of the received signal under assumption H0; the assumption H1 represents the assumption that the received signal includes a radiation source signal; the assumption H0 represents the assumption that the received signal does not include a radiation source signal.

[0138] The generalized likelihood ratio test formula construction module 904 is used to obtain the generalized likelihood ratio test formula based on the first probability density function and the second probability density function, according to the generalized likelihood ratio criterion.

[0139] The maximum likelihood estimation determination module 905 is used to differentiate the M-dimensional probability density function under the H1 assumption to obtain the maximum likelihood estimate of the product of the channel coefficients and the Doppler frequency shift of the received signal.

[0140] The test calculation value determination module 906 is used to substitute the maximum likelihood estimate into the generalized likelihood ratio test formula to obtain the test calculation value.

[0141] The passive detection implementation module 907 is used to compare the calculated test value with a preset threshold value to achieve passive detection.

[0142] In this embodiment of the invention, the first probability density function of the received signal under assumption H1 and the second probability density function of the received signal under assumption H0 are first obtained. Here, assumption H1 represents the assumption that the received signal includes a radiation source signal; assumption H0 represents the assumption that the received signal does not include a radiation source signal. Based on this, a generalized likelihood ratio test formula is obtained using the generalized likelihood ratio. The maximum likelihood estimate of the product of the channel coefficient obtained by differentiating the M-dimensional probability density function of the Rayleigh distribution of the received signal under assumption H1 and the Doppler frequency shift is substituted into the generalized likelihood ratio test formula to obtain the test calculation value. By comparing the test calculation value with a preset threshold value, passive detection is achieved. In the absence of a reference signal, the detection of weak radiation source signals is realized through the generalized likelihood ratio test algorithm based on the test assumption.

[0143] Optionally, the M-dimensional probability density function is:

[0144]

[0145] Where f(y) represents the M-dimensional probability density function; y n The received signal represents the nth dimension; N represents the total number of dimensions of the received signal; M represents the total number of variables in the received signal; σ 2 denoted by variance; R represents the distance difference between the direct wave and the echo; y represents the received signal; the superscript H represents the conjugate transpose of the matrix.

[0146] Optionally, the first probability density function is:

[0147]

[0148] Where f(y|H1) represents the first probability density function; y n The received signal represents the nth dimension; N represents the total number of dimensions of the received signal; α represents the product of the channel coefficient and the Doppler frequency shift of the received signal; s n y represents the radiation source signal in the nth dimension; y represents the received signal; s represents the radiation source signal; R represents the distance difference between the direct wave and the echo; the superscript H represents the conjugate transpose operation of the matrix.

[0149] Optionally, the second probability density function is:

[0150]

[0151] Where f(y|H0) represents the second probability density function; y nThe received signal represents the nth dimension; N represents the total number of dimensions of the received signal; y represents the received signal; R represents the distance difference between the direct wave and the echo; the superscript H represents the conjugate transpose of the matrix.

[0152] Optionally, the generalized likelihood ratio test formula is:

[0153]

[0154] Where f(y|H1) represents the first probability density function; f(y|H0) represents the second probability density function; and ζ represents the preset threshold value.

[0155] Optionally, a passive detection implementation module is specifically used to determine that the received signal contains a radiation source signal when the test calculation value is greater than or equal to the preset threshold value; and to determine that the received signal does not contain a radiation source signal when the test calculation value is less than the preset threshold value.

[0156] This invention also provides an electronic device, such as... Figure 10 As shown, it includes a processor 1001, a communication interface 1002, a memory 1003, and a communication bus 1004, wherein the processor 1001, the communication interface 1002, and the memory 1003 communicate with each other through the communication bus 1004.

[0157] Memory 1003 is used to store computer programs;

[0158] When the processor 1001 executes the program stored in the memory 1003, it implements the steps of the passive detection method in any of the above-mentioned non-Gaussian noise environments.

[0159] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0160] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0161] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0162] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0163] It should be noted that the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention.

[0164] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0165] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings and the disclosure in carrying out the claimed invention. In the description of the invention, the word "comprising" does not exclude other components or steps, "a" or "an" does not exclude a plurality, and "a plurality" means two or more, unless otherwise explicitly specified. Furthermore, while different embodiments may describe certain measures, this does not mean that these measures cannot be combined to produce good results.

[0166] The method provided in this invention can be applied to electronic devices. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, etc. No limitation is made herein; any electronic device that can implement this invention falls within the protection scope of this invention.

[0167] For the embodiments of the device / electronic device / storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to in the description of the method embodiments.

[0168] It should be noted that the device, electronic device and storage medium in the embodiments of the present invention are respectively the device, electronic device and storage medium for applying the passive detection method in a non-Gaussian noise environment described above. Therefore, all embodiments of the passive detection method in a non-Gaussian noise environment described above are applicable to the device, electronic device and storage medium, and can achieve the same or similar beneficial effects.

[0169] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A passive detection method for non-Gaussian noise environments, characterized in that, The passive detection method includes: Acquire received signals; Construct the M-dimensional probability density function of the Rayleigh distribution of the received signal; Acquiring the received signal under the assumption The first probability density function and the received signal under the assumption The second probability density function under the assumption; This indicates the assumption that the received signal includes a radiation source signal; the assumption This implies the assumption that the received signal does not include the radiation source signal; Based on the first probability density function and the second probability density function, the generalized likelihood ratio test formula is obtained according to the generalized likelihood ratio criterion. In the assumption The maximum likelihood estimate of the product of the channel coefficients and the Doppler frequency shift of the received signal is obtained by taking the derivative of the M-dimensional probability density function. Substituting the maximum likelihood estimate into the generalized likelihood ratio test formula yields the test value; The calculated test value is compared with a preset threshold value to achieve passive detection; The received signal is: ； in, Indicates the first The signal monitoring station received the first Gaussian white noise in the received signal of each dimension; Indicates the first The loss factor of the received signal echo at each signal monitoring station. Indicates the first Time delay of each signal monitoring station This refers to the Doppler frequency difference; Indicates Rayleigh noise; Indicates the signal from the radiation source; To represent a complex number; The calculated test value is: ； in, Represents the first probability density function; This represents the second probability density function; Indicates the first Received signals in each dimension; This represents the total number of dimensions of the received signal; This indicates the distance difference between the direct wave and the echo; This refers to the received signal; The superscript represents the product of the channel coefficient and the Doppler frequency shift of the received signal. This represents the conjugate transpose operation of a matrix.

2. The passive detection method according to claim 1, characterized in that, The M-dimensional probability density function is: ； in, Denotes the M-dimensional probability density function; Indicates the first Received signals in each dimension; This represents the total number of dimensions of the received signal; This indicates the total number of variables in the received signal; Indicates variance; This indicates the distance difference between the direct wave and the echo; Indicates the received signal; superscript This represents the conjugate transpose operation of a matrix.

3. The passive detection method according to claim 1, characterized in that, The first probability density function is: ； in, Represents the first probability density function; Indicates the first Received signals in each dimension; This represents the total number of dimensions of the received signal; This represents the product of the channel coefficients and the Doppler frequency shift of the received signal; Indicates the first Radiation source signals in multiple dimensions; This refers to the received signal; Indicates the signal from the radiation source; Indicates the distance difference between the direct wave and the echo; superscript This represents the conjugate transpose operation of a matrix.

4. The passive detection method according to claim 1, characterized in that, The second probability density function is: ； in, This represents the second probability density function; Indicates the first Received signals in each dimension; This represents the total number of dimensions of the received signal; This refers to the received signal; Indicates the distance difference between the direct wave and the echo; superscript This represents the conjugate transpose operation of a matrix.

5. The passive detection method according to claim 1, characterized in that, The generalized likelihood ratio test formula is: ； in, Represents the first probability density function; This represents the second probability density function; This represents the preset threshold value.

6. The passive detection method according to claim 1, characterized in that, The passive detection is achieved by comparing the calculated test value with a preset threshold value, including: When the calculated test value is greater than or equal to the preset threshold value, it is determined that the received signal contains a radiation source signal; When the calculated test value is less than the preset threshold value, it is determined that the received signal does not contain a radiation source signal.

7. A passive detection device for non-Gaussian noise environments, characterized in that, The passive detection device includes: The first acquisition module is used to acquire the received signal; The construction module is used to construct the M-dimensional probability density function of the Rayleigh distribution of the received signal; The second acquisition module is used to acquire the received signal under the assumption that The first probability density function and the received signal under the assumption The second probability density function under the assumption; This indicates the assumption that the received signal includes a radiation source signal; the assumption This implies the assumption that the received signal does not include the radiation source signal; The generalized likelihood ratio test formula construction module is used to obtain the generalized likelihood ratio test formula based on the first probability density function and the second probability density function, according to the generalized likelihood ratio criterion. The maximum likelihood estimation determination module is used to determine the maximum likelihood estimation function. By taking the derivative of the M-dimensional probability density function under the assumptions, the maximum likelihood estimate of the product of the channel coefficients and the Doppler frequency shift of the received signal is obtained. The test calculation value determination module is used to substitute the maximum likelihood estimate into the generalized likelihood ratio test formula to obtain the test calculation value; A passive detection implementation module is used to compare the calculated test value with a preset threshold value to achieve passive detection; The received signal is: ； in, Indicates the first The signal monitoring station received the first Gaussian white noise in the received signal of each dimension; Indicates the first The loss factor of the received signal echo at each signal monitoring station. Indicates the first Time delay of each signal monitoring station This refers to the Doppler frequency difference; Indicates Rayleigh noise; Indicates the signal from the radiation source; To represent a complex number; The test calculation value is: ； in, Represents the first probability density function; This represents the second probability density function; Indicates the first Received signals in each dimension; This represents the total number of dimensions of the received signal; This indicates the distance difference between the direct wave and the echo; This refers to the received signal; The superscript represents the product of the channel coefficient and the Doppler frequency shift of the received signal. This represents the conjugate transpose operation of a matrix.

8. The passive detection device according to claim 7, characterized in that, The M-dimensional probability density function is: ； in, Denotes the M-dimensional probability density function; Indicates the first Received signals in each dimension; This represents the total number of dimensions of the received signal; This indicates the total number of variables in the received signal; Indicates variance; This indicates the distance difference between the direct wave and the echo; Indicates the received signal; superscript This represents the conjugate transpose operation of a matrix.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the steps of the method according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

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