Passive detection method and device in non-Gaussian noise environment and electronic equipment

Abstract

The invention discloses a passive detection method and device in a non-Gaussian noise environment and electronic equipment. The method comprises the following steps: constructing an M-dimensional probability density function of Rayleigh distribution of a received signal; acquiring a first probability density function of the received signal under a hypothesis H1 and a second probability density function of the received signal under a hypothesis H0; the hypothesis H1 represents the hypothesis that the received signal comprises the radiation source signal; the hypothesis H0 represents the hypothesis that the received signal does not include the radiation source signal; based on the first probability density function and the second probability density function, a generalized likelihood ratio check formula is obtained according to a generalized likelihood ratio basis; deriving the M-dimensional probability density function under the assumption of H1 to obtain maximum likelihood estimation of a product of a channel coefficient and Doppler frequency shift; substituting the maximum likelihood estimation into a generalized likelihood ratio test formula to obtain a test calculation value; and comparing the test calculation value with a threshold value to realize passive detection through a generalized likelihood ratio test algorithm based on test hypothesis under the condition of no reference signal.

Description

Technical Field

[0001] The present invention belongs to the technical field of signal processing, and in particular relates to a passive detection method, device and electronic equipment in a non-Gaussian noise environment. Background Art

[0002] In the complex and ever-changing modern electronic countermeasure environment, the external radiation source radar third-party signal transmitting equipment plays an important role, so the importance of radiation source detection is self-evident. And with the advancement of science and technology, the popularization of various electronic products, the electromagnetic environment in space is becoming increasingly complex. In the detection process, the complex electromagnetic environment will seriously interfere with the detection effect and affect the accuracy of radiation source detection.

[0003] At present, energy detection method and matched filtering method are the most widely used classical signal detection methods at home and abroad. Energy detection algorithm is a non-coherent signal detection method. It compares and judges the energy of the signal in a specific time with the preset threshold value to determine whether there is a signal; the cross-correlation detection method is based on the matched filtering theory method. It accumulates the target energy by using the fuzzy function of the reference signal and the target echo signal to obtain the target delay Doppler information to achieve signal detection. However, the reference signal in the matched filtering method needs to be obtained by direct wave purification. Therefore, the acquisition and purification of the direct wave signal is crucial to detection. If there is a scene where the direct wave cannot be received, the detection performance will drop sharply, which seriously limits the use environment of the external radiation source radar. Therefore, it is very important to study the detection of external radiation sources under zero direct waves. This research can not only overcome the limitations of direct wave purification technology, but also make the external radiation source radar detection system suitable for more application scenarios, thereby providing a theoretical basis and technical support for the development of a new generation of distributed external radiation source detection equipment in my country.

[0004] However, the above traditional detection technology cannot quickly detect low signal-to-noise ratio and blind signals in complex electromagnetic environments, and requires reference signals. Therefore, the detection technology of weak radiation sources under the background of Rayleigh noise still faces many difficulties. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a passive detection method, device and electronic equipment in a non-Gaussian noise environment.

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

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

[0008] Get the received signal;

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

[0010] Acquire a first probability density function of the received signal under hypothesis H1 and a second probability density function of the received signal under hypothesis H0; the hypothesis H1 represents the hypothesis that the received signal includes a radiation source signal; the hypothesis H0 represents the hypothesis 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, a generalized likelihood ratio test formula is obtained according to a generalized likelihood ratio basis;

[0012] Under the assumption H1, the M-dimensional probability density function is derived to obtain a maximum likelihood estimate of the product of the channel coefficient of the received signal and the Doppler frequency shift;

[0013] Substituting the maximum likelihood estimate into the generalized likelihood ratio test formula to obtain a test calculation value;

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

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

[0016]

[0017] Wherein, f(y) represents the M-dimensional probability density function; y n represents the received signal of 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 represents variance; R represents the distance difference between the direct wave and the echo; y represents the received signal; and the superscript H represents the conjugate transpose operation of the matrix.

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

[0019]

[0020] Wherein, f(y|H1) represents the first probability density function; y n represents the received signal of the nth dimension; N represents the total number of dimensions of the received signal; α represents the product of the channel coefficient of the received signal and the Doppler frequency shift; s n represents the radiation source signal of 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; and the superscript H represents the conjugate transpose operation of the matrix.

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

[0022]

[0023] Wherein, f(y|H0) represents the second probability density function; y n represents the received signal of 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; and the superscript H represents the conjugate transpose operation of the matrix.

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

[0025]

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

[0027] Optionally, comparing the verification calculated value with a preset threshold value to achieve passive detection includes:

[0028] When the verification calculation 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 verification calculation value is less than the preset threshold value, it is determined that the received signal does not contain a radiation source signal.

[0030] In a second aspect, the present invention provides a passive detection device in a non-Gaussian noise environment, the passive detection device comprising:

[0031] A first acquisition module, used to acquire a received signal;

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

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

[0034] A generalized likelihood ratio test formula construction module, used for obtaining a generalized likelihood ratio test formula according to a generalized likelihood ratio basis based on the first probability density function and the second probability density function;

[0035] A maximum likelihood estimation determination module, used to derive the M-dimensional probability density function under the H1 hypothesis to obtain a maximum likelihood estimate of the product of the channel coefficient of the received signal and the Doppler frequency shift;

[0036] A test calculation value determination module, used for substituting the maximum likelihood estimate into the generalized likelihood ratio test formula to obtain a test calculation value;

[0037] The passive detection implementation module is used to compare the verification calculation value with a preset threshold value to implement passive detection.

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

[0039]

[0040] Wherein, f(y) represents the M-dimensional probability density function; y n represents the received signal of 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 represents variance; R represents the distance difference between the direct wave and the echo; y represents the received signal; and the superscript H represents the conjugate transpose operation of the matrix.

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

[0042]

[0043] Wherein, f(y|H1) represents the first probability density function; y n represents the received signal of the nth dimension; N represents the total number of dimensions of the received signal; α represents the product of the channel coefficient of the received signal and the Doppler frequency shift; s n represents the radiation source signal of 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; and the superscript H represents the conjugate transpose operation of the matrix.

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

[0045]

[0046] Wherein, f(y|H0) represents the second probability density function; y n represents the received signal of 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; and the superscript H represents the conjugate transpose operation of the matrix.

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

[0048]

[0049] Among them, f(y|H1) represents the first probability density function; f(y|H0) represents the second probability density function; 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 verification calculation value is greater than or equal to the preset threshold value; when the verification calculation value is less than the preset threshold value, determine that the received signal does not contain a radiation source signal.

[0051] In a third aspect, the present invention provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0052] Memory, used to store computer programs;

[0053] The processor is used to implement the method steps described in any of the above-mentioned passive detection methods in a non-Gaussian noise environment when executing the program stored in the memory.

[0054] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in any of the above-mentioned passive detection methods in a non-Gaussian noise environment are implemented.

[0055] The present invention provides a passive detection method in a non-Gaussian noise environment. First, a first probability density function of a received signal under a hypothesis H1 and a second probability density function of the received signal under a hypothesis H0 are obtained. The hypothesis H1 represents the hypothesis that the received signal includes a radiation source signal. The hypothesis H0 represents the hypothesis that the received signal does not include a radiation source signal. On this basis, a generalized likelihood ratio test formula is obtained using a generalized likelihood ratio basis. A maximum likelihood estimate of the product of a channel coefficient and a Doppler frequency shift obtained by deriving an M-dimensional probability density function of a Rayleigh distribution of the received signal under the hypothesis H1 is substituted into the generalized likelihood ratio test formula to obtain a 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, detection of a weak radiation source signal is achieved by a generalized likelihood ratio test algorithm based on the test hypothesis.

[0056] The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0058] Figure 2Schematic diagram of an application scenario of a passive detection method in a non-Gaussian noise environment provided by an embodiment of the present invention;

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

[0060] Figure 4 is a simulation principle diagram of a passive detection method provided by an embodiment of the present invention;

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

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

[0063] Figure 7 is a schematic diagram of DTMB comparison experiment results provided by an embodiment of the present invention;

[0064] Figure 8 is a schematic diagram of a linear frequency modulation signal comparison experiment result provided by an embodiment of the present invention;

[0065] Fig. 9 is a schematic structural diagram of a passive detection device in a non-Gaussian noise environment provided by an embodiment of the present invention;

[0066] Fig.10 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

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

[0068] In order to solve the problem that the traditional detection technology cannot quickly detect low signal-to-noise ratio and blind signals in complex electromagnetic environments and requires reference signals, the embodiment of the present invention provides a passive detection method in a non-Gaussian noise environment. Figure 1 , Figure 1 1 is a flow chart of a passive detection method in a non-Gaussian noise environment provided by an embodiment of the present invention, which specifically includes the following steps:

[0069] Step S101, obtaining a received signal.

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

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

[0072] In an embodiment of the present invention, considering that Rayleigh fading is considered to be the most typical small-scale fading under non-Gaussian noise conditions, an embodiment of the present invention studies the passive detection problem under a Rayleigh noise environment and constructs an 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 is described below:

[0074] (1) Power spectrum simulation:

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

[0076]

[0077] Where W(f) represents the exponential power spectrum; a is a constant whose value should make W(f 3dB / 2)=0.5, so f represents frequency; f d Indicates the Doppler center frequency, that is, the average Doppler frequency; f 3dB represents the half-power point bandwidth.

[0078] According to the exponential power spectrum, the power spectrum density S is obtained. x (ω), S x The first complex stationary random sequence of (ω) is x(n), and x(n) can be obtained by the linear time-invariant system of the linear phase filter H(ω) to obtain the second complex stationary random sequence y(n), thereby obtaining the power spectral density S of y(n) y (ω), as follows:

[0079] Sy(ω)=|H(ω) 2 Sx(ω);

[0080] Among them, the noise power spectral density S y (ω) is a Gaussian spectrum; x(n) is a spectrum that obeys N(μ,σ 2 ) is a Gaussian white noise, that is, S x (ω)=σ 2; ω is the angular frequency, ω=2πf; μ represents the mathematical expectation; σ 2 Represents variance.

[0081] In the embodiment of the present invention, the complex stationary random sequence is a signal sequence with stationary properties, in which the value of the signal is in complex form. The stationary properties mainly include the following points: 1. The mean is constant, that is, the expected value of the sequence is equal at any time point; 2. The autocovariance of the sequence is only related to the time interval, but not to the time point; 3. The power spectrum density is only related to the frequency and is translation-invariant over time.

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

[0083] The random sequences v1 and v2 that obey N(0,1) are passed through the designed filters H(f) respectively, and the outputs u1 and u2 are correlated Gaussian sequences with Gaussian power spectrum density.

[0084] (2) Simulating environmental noise:

[0085] Generate random numbers λ1,λ2 and N(μ,σ) uniformly distributed 2 ) are mutually orthogonal Gaussian white noise sequences v3, v4:

[0086]

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

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

[0089] See also Figure 4 , Figure 4 is a simulation principle diagram of the passive detection method provided by an embodiment of the present invention, where v 1,i and v 2,i Output u through H(z) 1,i and u 2,i , v 1,i and v 2,i are two independent Gaussian noise sequences that conform to N(0,1) distribution; u 1,i and u 2,i represents the output after the linear phase filter; i represents the channel. s 2 with u 1,i and u 2,i Output w by dot product 1,i and w 2,i , and respectively w 1,i and w 2,i After the square operation, sum it up and take the square root of the sum result to output z i . represents variance; w 1,i and w 2,i Both represent uniformly distributed noise multiplied by variance; z i Indicates the final output.

[0090] Construct a signal model of 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] Among them, x k (n) represents the Gaussian white noise of the received signal of the nth dimension received by the kth signal monitoring station; k = 1, 2, ..., K, b k represents the loss coefficient of the signal echo received by the kth signal monitoring station, l k represents the time delay of the kth signal monitoring station, Ω kis the Doppler frequency difference. The noise signal is the Rayleigh noise w k (n); s represents the radiation source signal; n represents the dimension of the received signal is 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:

[0094]

[0095] Where f(y) represents the M-dimensional probability density function; y n represents the received signal of 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 represents variance; R represents the distance difference between the direct wave and the echo; y represents the received signal; and the superscript H represents the conjugate transpose operation of the matrix.

[0096] Step S103, obtaining a first probability density function of the received signal under hypothesis H1 and a second probability density function of the received signal under hypothesis H0; hypothesis H1 indicates that the received signal includes a radiation source signal; hypothesis H0 indicates that the received signal does not include a 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 the embodiment of the present invention, there may be two situations for the received signal, one is the received signal including the radiation source signal, and the other is the received signal not including the radiation source signal. Therefore, assumption H1 is the assumption that the received signal includes the radiation source signal; assumption H0 is the assumption that the received signal does not include the radiation source signal.

[0099] The radiation source signal refers to a signal emitted by a radiation source for transmitting information or communicating. The radiation source may be an electromagnetic wave transmitter, a radio station, a satellite or other equipment.

[0100] In the embodiment of the present invention, the radiation source signal can be expressed as the product of the complex envelope of the transmission signal and its phase change part. The specific expression is as follows:

[0101]

[0102] Where s(n) represents the radiation source signal of the nth dimension; u(n) represents the complex envelope of the radiation signal source of the nth dimension; f c represents the carrier frequency of the transmitted signal; j represents a complex number; t represents time; Indicates the initial phase of the transmitted signal.

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

[0104]

[0105] Where f(y|H1) represents the first probability density function; y n represents the received signal of the nth dimension; N represents the total number of dimensions of the received signal; α represents the product of the channel coefficient of the received signal and the Doppler frequency shift; s n represents the radiation source signal of 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; and the superscript H represents the conjugate transpose operation of the matrix.

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

[0107]

[0108] Where, f(y|H0) represents the second probability density function; y n represents the received signal of 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; and the superscript H represents the conjugate transpose operation of the matrix.

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

[0110] In an embodiment of the present invention, after obtaining the first probability density function and the second probability density function, a generalized likelihood ratio test formula can be obtained according to the generalized likelihood ratio basis. The specific generalized likelihood ratio test formula is constructed by comparing the probability density functions under two hypotheses.

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

[0112]

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

[0114] Step S105 , under the H1 assumption, the M-dimensional probability density function is differentiated to obtain a maximum likelihood estimate of the product of the channel coefficient of the received signal and the Doppler frequency shift.

[0115] In the embodiment of the present invention, the M-dimensional probability density function is derived under the H1 assumption to obtain the maximum likelihood estimate of the product α of the channel coefficient of the received signal and the Doppler frequency shift:

[0116]

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

[0118] Step S106, substituting the maximum likelihood estimate into the generalized likelihood ratio test formula to obtain a test calculation value.

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

[0120]

[0121] Step S107, comparing the verification calculated value with a preset threshold value to achieve passive detection.

[0122] Passive detection is a technology that does not emit any electromagnetic waves, but instead achieves target detection by receiving electromagnetic radiation already existing in the environment, that is, receiving signals.

[0123] In the embodiment of the present invention, passive detection can be performed by comparing the calculated value with the preset threshold value, and the specific method is as follows:

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

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

[0126] In the embodiment of the present invention, by checking the relationship between the calculated value and the threshold value, it is determined whether the received signal contains a radiation source, thereby providing a basis for subsequent signal processing and analysis.

[0127] Specifically, the preset threshold value may be set by a technician based on theory or practical experience, and is not limited here.

[0128] In an embodiment of the present invention, a first probability density function of a received signal under hypothesis H1 and a second probability density function of a received signal under hypothesis H0 are firstly obtained; wherein hypothesis H1 represents the hypothesis that a radiation source signal is included in the received signal; hypothesis H0 represents the hypothesis that a radiation source signal is not included in the received signal, and on this basis, a generalized likelihood ratio test formula is obtained using the generalized likelihood ratio basis, and a maximum likelihood estimate of the product of a channel coefficient and a Doppler frequency shift obtained by deriving the M-dimensional probability density function of the Rayleigh distribution of the received signal under hypothesis H1 is substituted into the generalized likelihood ratio test formula to obtain a test calculation value, so as to realize passive detection by comparing the test calculation value with a preset threshold value, and in the absence of a reference signal, detection of a weak radiation source signal is realized by a generalized likelihood ratio test algorithm based on the test hypothesis.

[0129] The simulation experiment of a passive detection method in a non-Gaussian noise environment provided by an embodiment 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, the interval of each signal-to-noise ratio change is 1dB, and 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 also Figure 5 and Figure 6 , Figure 5 is a schematic diagram of Rayleigh distribution noise simulation provided by an embodiment of the present invention, Figure 5 (a) is the noise time domain diagram, which shows the Rayleigh noise time domain waveform. Its horizontal axis represents the number of points and the vertical axis represents the amplitude. Figure 5 (b) in FIG. 5 is a noise amplitude distribution diagram, which reflects 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 of the relationship between detection performance, signal-to-noise ratio and signal length. Figure 6 The signal lengths in (a), (b), (c) and (d) are 1s, 0.1s, 0.01s and 0.001s respectively. The horizontal axis represents the signal-to-noise ratio in decibels, and the vertical axis represents the detection probability. With the improvement of the signal-to-noise ratio, the detection probability continues to increase. When the signal-to-noise ratio is above -50dB, a good detection effect can be achieved. At the same time, the longer the signal length, the more stable the detection, indicating that with the increase of signal length and the improvement of signal-to-noise ratio, the detection accuracy is significantly improved. In the absence of a reference signal, the detection of weak radiation source signals is achieved through the generalized likelihood ratio test algorithm based on the test hypothesis, and it is suitable for low signal-to-noise ratios.

[0133] See also Figure 7 and Figure 8 , Figure 7 is a schematic diagram of DTMB comparison experiment results provided by an embodiment of the present invention, Figure 8 is a schematic diagram of a linear frequency modulation signal comparison experiment result provided by an embodiment of the present invention, Figure 7 and Figure 8 The horizontal axis represents the signal-to-noise ratio in decibels, and the vertical axis represents the detection probability. Figure 7 and Figure 8 The passive detection method provided by the embodiment of the present invention is compared with the existing delay estimation algorithm and the traditional kurtosis algorithm. From the simulation results, it can be seen that the detection probability of the passive detection method provided by the embodiment of the present invention is significantly greater than that of the existing algorithm.

[0134] Based on the same inventive concept, the embodiment of the present invention also provides a passive detection device in a non-Gaussian noise environment, see Fig. 9 , Fig. 9 : is a schematic diagram of the structure of a passive detection device in a non-Gaussian noise environment provided by an embodiment of the present invention, the passive detection device comprising:

[0135] A first acquisition module 901, configured to acquire a received signal;

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

[0137] The second acquisition module 903 is used to acquire a first probability density function of the received signal under the assumption H1 and a second probability density function of the received signal under the 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] A generalized likelihood ratio test formula construction module 904 is used to obtain a generalized likelihood ratio test formula based on the first probability density function and the second probability density function according to a generalized likelihood ratio basis;

[0139] A maximum likelihood estimation determination module 905 is used to derive the M-dimensional probability density function under the H1 assumption to obtain a maximum likelihood estimation of the product of the channel coefficient of the received signal and the Doppler frequency shift;

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

[0141] The passive detection implementation module 907 is used to compare the verification calculation value with a preset threshold value to implement passive detection.

[0142] In an embodiment of the present invention, a first probability density function of a received signal under hypothesis H1 and a second probability density function of a received signal under hypothesis H0 are firstly obtained; wherein hypothesis H1 represents the hypothesis that a radiation source signal is included in the received signal; hypothesis H0 represents the hypothesis that a radiation source signal is not included in the received signal, and on this basis, a generalized likelihood ratio test formula is obtained using the generalized likelihood ratio basis, and a maximum likelihood estimate of the product of a channel coefficient and a Doppler frequency shift obtained by deriving the M-dimensional probability density function of the Rayleigh distribution of the received signal under hypothesis H1 is substituted into the generalized likelihood ratio test formula to obtain a test calculation value, so as to realize passive detection by comparing the test calculation value with a preset threshold value, and in the absence of a reference signal, detection of a weak radiation source signal is realized by a generalized likelihood ratio test algorithm based on the test hypothesis.

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

[0144]

[0145] Wherein, f(y) represents the M-dimensional probability density function; y n represents the received signal of 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 represents variance; R represents the distance difference between the direct wave and the echo; y represents the received signal; and the superscript H represents the conjugate transpose operation of the matrix.

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

[0147]

[0148] Wherein, f(y|H1) represents the first probability density function; y n represents the received signal of the nth dimension; N represents the total number of dimensions of the received signal; α represents the product of the channel coefficient of the received signal and the Doppler frequency shift; s n represents the radiation source signal of 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; and the superscript H represents the conjugate transpose operation of the matrix.

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

[0150]

[0151] Wherein, f(y|H0) represents the second probability density function; y nrepresents the received signal of 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; and the superscript H represents the conjugate transpose operation of the matrix.

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

[0153]

[0154] Among them, f(y|H1) represents the first probability density function; f(y|H0) represents the second probability density function; 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 verification calculation value is greater than or equal to the preset threshold value; when the verification calculation value is less than the preset threshold value, determine that the received signal does not contain a radiation source signal.

[0156] The embodiment of the present invention further provides an electronic device, such as Fig.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, used for storing computer programs;

[0158] The processor 1001 is used to implement the method steps described in any of the above passive detection methods in a non-Gaussian noise environment when executing the program stored in the memory 1003.

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

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

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

[0162] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, 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 the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention.

[0164] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0165] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other changes to the disclosed embodiments by viewing the drawings and the disclosed content. In the description of the present invention, the term "comprising" does not exclude other components or steps, "one" or "an" does not exclude multiple situations, and "multiple" means two or more, unless otherwise clearly and specifically limited. In addition, certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0166] The method provided in the embodiment of the present invention can be applied to electronic devices. Specifically, the electronic device can be: a desktop computer, a portable computer, an intelligent mobile terminal, a server, etc. This is not limited here, and any electronic device that can implement the present invention belongs to the protection scope of the present invention.

[0167] As for the device / electronic device / storage medium embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

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

[0169] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A passive detection method in a non-Gaussian noise environment, characterized in that: The passive detection method comprises: Get the received signal; Constructing an M-dimensional probability density function of the Rayleigh distribution of the received signal; Acquire a first probability density function of the received signal under hypothesis H1 and a second probability density function of the received signal under hypothesis H0; the hypothesis H1 represents the hypothesis that the received signal includes a radiation source signal; the hypothesis H0 represents the hypothesis that the received signal does not include a radiation source signal; Based on the first probability density function and the second probability density function, a generalized likelihood ratio test formula is obtained according to a generalized likelihood ratio basis; Under the assumption H1, the M-dimensional probability density function is derived to obtain a maximum likelihood estimate of the product of the channel coefficient of the received signal and the Doppler frequency shift; Substituting the maximum likelihood estimate into the generalized likelihood ratio test formula to obtain a test calculation value; The verification calculated value is compared with a preset threshold value to achieve passive detection.

2. The passive detection method according to claim 1, characterized in that: The M-dimensional probability density function is: Wherein, f(y) represents the M-dimensional probability density function; y n represents the received signal of 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 represents variance; R represents the distance difference between the direct wave and the echo; y represents the received signal; and the superscript H represents the conjugate transpose operation of the matrix.

3. The passive detection method according to claim 1, characterized in that: The first probability density function is: Wherein, f(y|H1) represents the first probability density function; y n represents the received signal of the nth dimension; N represents the total number of dimensions of the received signal; α represents the product of the channel coefficient of the received signal and the Doppler frequency shift; s n represents the radiation source signal of 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; and the superscript H represents the conjugate transpose operation of the matrix.

4. The passive detection method according to claim 1, characterized in that: The second probability density function is: Wherein, f(y|H0) represents the second probability density function; y n represents the received signal of 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; and the superscript H represents the conjugate transpose operation of the matrix.

5. The passive detection method according to claim 1, characterized in that: The generalized likelihood ratio test formula is: Among them, f(y|H1) represents the first probability density function; f(y|H0) represents the second probability density function; ζ represents the preset threshold value.

6. The passive detection method according to claim 1, characterized in that: Comparing the calculated value with a preset threshold value to achieve passive detection includes: When the verification calculation 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 verification calculation 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 in a non-Gaussian noise environment, characterized in that: The passive detection device comprises: A first acquisition module, used to acquire a received signal; A construction module, used to construct an M-dimensional probability density function of the Rayleigh distribution of the received signal; A second acquisition module is used to acquire a first probability density function of the received signal under a hypothesis H1 and a second probability density function of the received signal under a hypothesis H0; the hypothesis H1 represents a hypothesis that the received signal includes a radiation source signal; the hypothesis H0 represents a hypothesis that the received signal does not include a radiation source signal; A generalized likelihood ratio test formula construction module, used for obtaining a generalized likelihood ratio test formula according to a generalized likelihood ratio basis based on the first probability density function and the second probability density function; A maximum likelihood estimation determination module, used to derive the M-dimensional probability density function under the H1 hypothesis to obtain a maximum likelihood estimate of the product of the channel coefficient of the received signal and the Doppler frequency shift; A test calculation value determination module, used for substituting the maximum likelihood estimate into the generalized likelihood ratio test formula to obtain a test calculation value; The passive detection implementation module is used to compare the inspection calculated value with a preset threshold value to implement passive detection.

8. The passive detection device according to claim 7, characterized in that: The M-dimensional probability density function is: Wherein, f(y) represents the M-dimensional probability density function; y n represents the received signal of 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 represents variance; R represents the distance difference between the direct wave and the echo; y represents the received signal; and the superscript H represents the conjugate transpose operation of the 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, for implementing the steps of any method described in claims 1-6 when executing a program stored in a memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the methods of claims 1-6 are implemented.

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