A positioning method and system based on maximum correlation entropy in an impulse noise environment

Abstract

The present invention proposes a positioning method and system based on maximum correlation entropy in an impulse noise environment. The method comprises: step S1, multiple receiving stations with known speeds perform signal interception based on multiple time slots on their motion trajectories to receive time domain signals from a stationary radiation source target, and down-convert the received time domain signals according to the carrier frequency to obtain a baseband received signal; step S2, sampling the baseband received signal, and constructing a cost function based on correlation entropy for each sampling point based on the sampling data of each sampling point, thereby obtaining a global cost function; step S3, determining the estimated position of the stationary radiation source target by calculating the maximum value point of the global cost function, wherein the maximum value of the global cost function represents that the estimated position of the stationary radiation source target is an estimate of the true position of the stationary radiation source target.

Description

Technical Field

[0001] The present invention belongs to the technical field of signal processing, and in particular relates to a positioning method and system based on maximum correlation entropy in an impulse noise environment. Background Art

[0002] Passive positioning technology is a technology that does not need to emit electromagnetic signals itself, but uses receiving stations to intercept signals emitted or reflected by the target to determine the target's location. It has the advantages of low cost and strong anti-interference ability, and has important application value in the fields of surface ship positioning, sea area monitoring, and illegal ground intrusion.

[0003] Passive positioning is usually achieved through a two-step method, that is, first completing the estimation of parameters related to the target position, such as arrival time difference, arrival angle, arrival frequency difference, etc., and then obtaining the target position estimate through positioning solution. However, the two-step passive positioning algorithm ignores the premise that the received signals all come from the same target during the implementation process, so its positioning accuracy is limited.

[0004] In order to solve the shortcomings of the two-step method and improve positioning accuracy, the article "WEISS A J. Direct position determination of narrowband radio transmitters [J]. IEEE Signal Processing Letters, IEEE, 2004, 11 (5): 513–516" proposed a direct positioning (Direct Position Determination, DPD) method, which can directly use the observed signal without completing the estimation of the time difference / frequency difference to achieve the positioning of the target. Compared with the two-step positioning algorithm, DPD usually has better positioning performance because it does not need to estimate intermediate parameters.

[0005] Due to the actual influence of natural phenomena or human factors, noise is usually an impulse noise with obvious spike pulse characteristics, such as noise in power line communication systems, shallow sea water acoustic channel noise, etc. The rate at which the density function of this type of noise decays at the tail is less than that of Gaussian noise, and it can usually be modeled using an α-stable distribution with a thick tail characteristic. However, the DPD (ML-DPD) algorithm based on the maximum likelihood (ML) criterion usually considers establishing a likelihood function about the target position based on the distribution characteristics of the noise in a Gaussian noise environment, and estimates the target position by finding the maximum value of the likelihood function. The embodiments of the present invention will prove that the performance of the DPD algorithm based on the maximum likelihood criterion will be significantly deteriorated in an impulse noise environment.

[0006] In the pulse noise environment, the performance of various estimation algorithms originally established under Gaussian noise conditions is significantly reduced. In order to solve the parameter estimation problem in the pulse noise environment, the paper "Jin Yan, Ren Hang, Ji Hongbing. OFDM time domain parameter estimation based on correlation entropy under pulse noise [J]. Systems Engineering and Electronics, 2015, 37(12): 2701–2706." aims to solve the problem of performance degradation of traditional orthogonal frequency division multiplexing time domain parameter estimation methods under pulse noise environments, and proposes a new time domain parameter estimation method based on correlation entropy. This algorithm has good estimation performance under pulse noise conditions. In the paper "Cai Ruiyan, Yang Li, Qian Yang. A new method for DOA estimation of coherently distributed sources based on correlation entropy under pulse noise [J]. Journal of Electronics and Information Technology, 2020, 42(11): 2600–2606.", a DOA estimation algorithm based on coherently distributed sources is proposed based on the concept of correlation entropy to solve the problem of significant degradation of arrival direction estimation performance under pulse noise environments. It has high estimation accuracy and robustness under pulse noise environments. A time difference of arrival estimation algorithm based on expectation maximization algorithm has better performance in low signal-to-noise ratio conditions. In the paper "Tong Zhijian. Research on delay estimation algorithm based on correlation entropy in alpha stable distribution noise environment [D]. Dalian University of Technology, 2010." Aiming at the shortcomings of the classic delay estimation algorithm in dealing with the problem of delay estimation in impulse noise, a variety of new delay estimation algorithms based on correlation entropy are proposed. The new algorithms have excellent estimation accuracy and noise resistance in impulse noise environments. Summary of the invention

[0007] In order to solve the above technical problems, the present application proposes a positioning solution based on maximum correlation entropy in an impulse noise environment, which realizes effective target direct positioning in an impulse noise environment, and derives the corresponding direct positioning algorithm based on the maximum correlation entropy criterion, thereby solving the problem of decreased positioning performance of traditional target direct algorithms in the case of impulse noise.

[0008] The first aspect of the present invention discloses a positioning method based on maximum correlation entropy in an impulse noise environment. The method comprises:

[0009] Step S1, multiple receiving stations with known speeds perform signal interception based on multiple time slots on their motion trajectories to receive time domain signals from a stationary radiation source target, and perform down-conversion processing on the received time domain signals according to the carrier frequency, thereby obtaining baseband received signals;

[0010] Step S2, sampling the baseband received signal, and constructing a cost function based on correlation entropy of each sampling point based on sampling data of each sampling point, so as to obtain a global cost function;

[0011] Step S3: Determine the estimated position of the stationary radiation source target by calculating the maximum point of the global cost function, where the maximum value of the global cost function represents that the estimated position of the stationary radiation source target is the estimate of the true position of the stationary radiation source target.

[0012] According to the method of the first aspect of the present invention, it is characterized in that in the step S1, the velocities and positions of the multiple receiving stations with known velocities remain unchanged when performing the signal interception, and the baseband received signal after the down-conversion processing includes a transmission delay, a Doppler frequency shift, and channel additive noise, where the channel additive noise follows a symmetric α-stable distribution, and the channel additive noise is independent of the time-domain signal.

[0013] According to the method of the first aspect of the present invention, in the step S1, the baseband received signal intercepted by the l-th receiving station among the L receiving stations with known velocities in the k-th time slot during the signal interception of K time slots is:

[0014]

[0015] where 0 < t ≤ T, l = 1, 2, …, L; k = 1, 2, …, K, t is a continuous time variable, T is the signal interception duration of each time slot, τ l，k is the transmission delay of the time-domain signal received in the k-th time slot, is the time-domain signal received in the k-th time slot, f c is the carrier frequency, f l，k is the Doppler frequency shift of the time-domain signal received in the k-th time slot, z l,k (t) is the additive impulse noise at the l-th receiving station in the k-th time slot.

[0016] According to the method of the first aspect of the present invention, in the step S2:

[0017] Sampling the baseband received signal with T s as the sampling period, and the sampling form of the baseband received signal of the l-th receiving station in the k-th time slot is:

[0018]

[0019] where t is a continuous time variable, T is the signal interception duration of each time slot, l = 1, 2, …, L; k = 1, 2, …, K, the n-th sampling point 0 ≤ n ≤ N - 1, N = T / T s is the total number of sampling points;

[0020] Express the sampling data of the 1-Nth sampling points in vector form as:

[0021] r l，k =A l，k F l，k s k +z l，k

[0022] Where l = 1, 2, ..., L; k = 1, 2, ..., K, and:

[0023]

[0024]

[0025]

[0026]

[0027] Where diag{} represents a diagonal matrix, F l，k is the downward shift operator, with F l，k s k Implementations k displacement.

[0028] According to the method of the first aspect of the present invention, in step S2, sampling based on multiple sampling points is performed on the baseband received signal of each time slot in the multiple time slots, and the same core length parameter is selected for the cost function based on correlation entropy constructed for each sampling point, then the cost function based on correlation entropy of the i-th sampling point in the k-th time slot is:

[0029]

[0030] Where, k = 1, 2, ..., K, i = 1, 2, ..., N, l = 1, 2, ..., L, e l，k，i (p)=(r l，k -A l，k F l，k s k ) i Represents the sampling data of the lth receiver at each sampling point in each time slot.

[0031] According to the method of the first aspect of the present invention, in step S2, the global cost function is:

[0032]

[0033] in, is the cost function based on correlation entropy of the lth receiver.

[0034] According to the method of the first aspect of the present invention, in step S3, the estimated position of the stationary radiation source target is:

[0035]

[0036] Among them, characterizes the true position of the stationary radiation source target.

[0037] The second aspect of the present invention discloses a positioning system based on maximum correlation entropy in an impulse noise environment. The system includes:

[0038] A first processing unit, configured to call a plurality of receiving stations with known speeds to perform signal interception based on a plurality of time slots on their movement trajectories, so as to receive the time-domain signal from the stationary radiation source target, and perform down-conversion processing on the received time-domain signal according to the carrier frequency, thereby obtaining a baseband received signal;

[0039] A second processing unit, configured to sample the baseband received signal, and construct a cost function based on correlation entropy for each sampling point based on the sampling data of each sampling point, thereby obtaining a global cost function;

[0040] A third processing unit, configured to determine the estimated position of the stationary radiation source target by calculating the maximum value point of the global cost function, wherein the maximum value of the global cost function represents that the estimated position of the stationary radiation source target is an estimate of the true position of the stationary radiation source target.

[0041] According to the system of the second aspect of the present invention, the speeds and positions of the plurality of receiving stations with known speeds remain unchanged when performing the signal interception, and the baseband received signal after the down-conversion processing includes a transmission delay, a Doppler frequency shift, and channel additive noise, wherein the channel additive noise follows a symmetric α-stable distribution, and the channel additive noise is independent of the time-domain signal.

[0042] According to the system of the second aspect of the present invention, the baseband received signal intercepted by the l-th receiving station in the k-th time slot among the signal interceptions of the L receiving stations with known speeds in K time slots is:

[0043]

[0044] where 0 < t ≤ T, l = 1, 2,..., L; k = 1, 2,..., K, t is a continuous time variable, T is the signal interception duration of each time slot, τ l,k is the transmission delay of the time-domain signal received in the k-th time slot, is the time-domain signal received in the k-th time slot, f c is the carrier frequency, f l,k is the Doppler frequency shift of the time-domain signal received in the k-th time slot, z l,k(t) is the additive impulse noise at the lth receiving station in the kth time slot.

[0045] According to the system of the second aspect of the present invention, the second processing unit is specifically configured to: s The baseband received signal is sampled at a sampling period, and the sampling form of the baseband received signal of the lth receiving station in the kth time slot is:

[0046]

[0047] Where, l = 1, 2, ..., L; k = 1, 2, ..., K, the nth sampling point 0≤n≤N-1, N=T / T s is the total number of sampling points;

[0048] The sampling data of the 1st to Nth sampling points are expressed in vector form as follows:

[0049] r l,k =A l,k F l,k s k +z l,k

[0050] Where l = 1, 2, ..., L; k = 1, 2, ..., K, and:

[0051]

[0052]

[0053]

[0054]

[0055] Where diag{} represents a diagonal matrix, F l,k is the downward shift operator, with F l,k s k Implementations k displacement.

[0056] According to the system of the second aspect of the present invention, the second processing unit is specifically configured to perform sampling based on multiple sampling points on the baseband received signal of each time slot in the multiple time slots, and select the same core length parameter for the cost function based on correlation entropy constructed for each sampling point, then the cost function based on correlation entropy of the i-th sampling point in the k-th time slot is:

[0057]

[0058] Where k = 1, 2, ..., K, i = 1, 2, ..., N, l = 1, 2, ..., L, e l,k,i (p)=(r l,k -A l,k F l,k s k ) i Represents the sampling data of the lth receiver at each sampling point in each time slot.

[0059] According to the system of the second aspect of the present invention, the global cost function is:

[0060]

[0061] in, is the cost function based on correlation entropy of the lth receiver.

[0062] According to the system of the second aspect of the present invention, the estimated position of the stationary radiation source target is:

[0063]

[0064] in, Characterize the true position of the stationary radiation source target.

[0065] The third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the positioning method based on maximum correlation entropy in an impulse noise environment described in any one of the first aspects of the present disclosure are implemented.

[0066] The fourth aspect of the present invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the positioning method based on maximum correlation entropy in an impulse noise environment described in any one of the first aspects of the present disclosure are implemented.

[0067] The technical solution provided by the present invention uses samples of received signal sampling to approximately estimate the correlation entropy in an impulse noise environment, and constructs a local cost function based on the correlation entropy; adds the local cost functions from different receivers and different time slots to obtain a global cost function; uses a grid search method to find the maximum value point of the global cost function as an estimate of the true position of the target; fully considers more practical impulse noise, and constructs a new cost function based on the maximum correlation entropy criterion, which can significantly improve the positioning effect in an impulse noise environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0069] Figure 1 is a flowchart of a positioning method based on maximum correlation entropy in an impulse noise environment according to an embodiment of the present invention;

[0070] Figure 2 A schematic diagram of a positioning algorithm based on maximum correlation entropy in an impulse noise environment according to an embodiment of the present invention;

[0071] Figure 3 A schematic diagram of comparing the positioning performance of various algorithms under different generalized signal-to-noise ratio conditions according to an embodiment of the present invention;

[0072] Figure 4 A schematic diagram showing a comparison of positioning performances of various algorithms under different characteristic parameter conditions according to an embodiment of the present invention;

[0073] Figure 5 is a structural diagram of a positioning system based on maximum correlation entropy in an impulse noise environment according to an embodiment of the present invention;

[0074] Figure 6 The figure is a structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0076] The first aspect of the present invention discloses a positioning method based on maximum correlation entropy in an impulse noise environment. Figure 1 FIG. 4 is a flow chart of a positioning method based on maximum correlation entropy in an impulse noise environment according to an embodiment of the present invention; Figure 1 As shown, the method includes:

[0077] ( Figure 2 Schematic diagram of a positioning algorithm based on maximum correlation entropy in an impulse noise environment according to an embodiment of the present invention; Figure 2 (shown)

[0078] Step S1: Multiple receiving stations with known velocities perform signal interception based on multiple time slots on their movement trajectories to receive the time-domain signals from a stationary radiation source target, and perform down-conversion processing on the received time-domain signals according to the carrier frequency, so as to obtain baseband received signals;

[0079] Step S2: Sample the baseband received signals, and construct a cost function based on relative entropy for each sampling point based on the sampling data of each sampling point, so as to obtain a global cost function;

[0080] Step S3: Determine the estimated position of the stationary radiation source target by calculating the maximum point of the global cost function, where the maximum value of the global cost function represents that the estimated position of the stationary radiation source target is an estimate of the true position of the stationary radiation source target.

[0081] In step S1, multiple receiving stations with known velocities perform signal interception based on multiple time slots on their movement trajectories to receive the time-domain signals from a stationary radiation source target, and perform down-conversion processing on the received time-domain signals according to the carrier frequency, so as to obtain baseband received signals.

[0082] In some embodiments, in step S1, when performing the signal interception, the velocities and positions of the multiple receiving stations with known velocities remain unchanged. The baseband received signals after the down-conversion processing include transmission delay, Doppler frequency shift, and channel additive noise, where the channel additive noise follows a symmetric α-stable distribution, and the channel additive noise is independent of the time-domain signals.

[0083] In some embodiments, in step S1, for the l-th receiving station among L receiving stations with known velocities, the baseband received signal for the k-th time slot interception in the signal interception of K time slots is:

[0084]

[0085] where 0 < t ≤ T, l = 1, 2, …, L; k = 1, 2, …, K, t is a continuous time variable, T is the signal interception duration of each time slot, τ l,k is the transmission delay of the time-domain signal received in the k-th time slot, is the time-domain signal received in the k-th time slot, f c is the carrier frequency, f l,k is the Doppler frequency shift of the time-domain signal received in the k-th time slot, z l,k (t) is the additive impulse noise at the l-th receiving station in the k-th time slot.

[0086] Specifically, consider that there is a stationary radiation source target and L receiving stations with known speeds in space. Each receiving station intercepts signals for K time slots on its motion trajectory. Assume that the speed and position of the receiving station remain unchanged during each signal interception. Assume that the position and speed of the lth receiving station at the kth signal interception are p l,k and v l,k ,l=1,2,…,L;k=1,2,…,K, the target position is p. The emission signal of the radiation source in the kth time slot is Among them, s k (t) is the envelope of the signal transmitted by the radiation source in the kth time slot, and the bandwidth is W (W<<f c ), f c is the carrier frequency.

[0087] Considering the influence of time delay, Doppler frequency shift and channel additive noise, the carrier frequency f c After down-conversion of the received signal, the baseband observation signal intercepted by the lth receiving station for the kth time is

[0088]

[0089] Among them, τ l,k is the transmission delay of the transmitted signal in the kth time slot from the target to the lth receiving station, c is the speed of light; f l,k is the Doppler frequency shift generated when the transmitted signal of the kth time slot propagates from the target to the lth receiving station, z l,k (t) is the additive impulse noise at the lth receiving station in the kth time slot, which obeys the symmetric α-stable distribution, that is, SαS distribution, z l,k (t)~S α (γ l ,0,0), assuming that the noise and the transmitted signal are independent of each other.

[0090] In step S2, the baseband received signal is sampled, and based on the sampling data of each sampling point, a cost function based on correlation entropy of each sampling point is constructed to obtain a global cost function.

[0091] In some embodiments, in step S2:

[0092] T s The baseband received signal is sampled at a sampling period, and the sampling form of the baseband received signal of the lth receiving station in the kth time slot is:

[0093]

[0094] Where, l = 1, 2, ..., L; k = 1, 2, ..., K, the nth sampling point 0≤n≤N-1,N=T / T s is the total number of sampling points;

[0095] The sampling data of the 1st to Nth sampling points are expressed in vector form as follows:

[0096] r l,k =A l,k F l,k s k +z l,k

[0097] Where l = 1, 2, ..., L; l = 1, 2, ..., K, and:

[0098]

[0099]

[0100]

[0101]

[0102] Where diag{} represents a diagonal matrix, F l,k is the downward shift operator, with F l,k s k Implementations k displacement.

[0103] In some embodiments, in step S2, sampling based on multiple sampling points is performed on the baseband received signal of each time slot in the multiple time slots, and the same core length parameter is selected for the cost function based on correlation entropy constructed for each sampling point, then the cost function based on correlation entropy of the i-th sampling point in the k-th time slot is:

[0104]

[0105] Where k = 1, 2, ..., K, i = 1, 2, ..., N, k = 1, 2, ..., L, e l,k,i (p)=(r l,k -A l,k F l,k s k ) i Represents the sampling data of the lth receiver at each sampling point in each time slot.

[0106] In some embodiments, in step S2, the global cost function is:

[0107]

[0108] in, is the cost function based on correlation entropy of the lth receiver.

[0109] Specifically, T s The baseband received signal is sampled for the sampling period, then the sampling form of the received signal of the lth receiving station in the kth time slot is:

[0110]

[0111] Among them, the nth sampling point 0≤n≤N-1,N=T / T s is the total number of sampling points.

[0112] Write the received data of the 1st to Nth sampling points into the following vector form:

[0113] r l,k =A l,k F l,k s k +z l,k ,l=1,2,…,L;k=1,2,…,K

[0114] in,

[0115]

[0116]

[0117]

[0118]

[0119] Among them, diag{a1,…,a N} represents {a1,…,a N} is a diagonal matrix with diagonal elements. l,k is the down shift operator, through Circularly shifting the rows of the identity matrix yields, Indicates rounding down, use F l,k s k To achieve k displacement.

[0120] Specifically, based on the concept of correlation entropy and using sample values ​​instead of expectations, the cost function of the i-th sampling point in the k-th time slot is obtained as:

[0121]

[0122] Among them, e l,k,i (p)=(r l,k -Al,k F l,k s k ) i ,l=1,2,…,L represents the sampling data of the lth receiver at each sampling point in each time slot, (a) i Represents the i-th element of vector a.

[0123] In order to simplify the derivation, the present invention selects the same kernel length parameter for the cost function of each sampling point in each time slot. In fact, selecting different kernel length parameters for the cost function of each sampling point in each time slot may result in higher positioning performance. Add the cost functions of K different time slots and N different sampling points to obtain the global cost function:

[0124]

[0125] in, It is called the cost function of the lth receiver.

[0126] Note that when the estimated target position is closer to the actual target position, The larger the value, the global cost function reaches its maximum value at the actual position.

[0127] In step S3, the estimated position of the stationary radiation source target is determined by calculating the maximum point of the global cost function, wherein the estimated position of the stationary radiation source target represented by the maximum point of the global cost function is an estimate of the true position of the stationary radiation source target.

[0128] In some embodiments, the global cost function J is found MCC The maximum value of (p) is obtained to obtain the estimated position of the radiation source target

[0129]

[0130] Specific examples

[0131] Simulation conditions:

[0132] Assume that the target is located at [10000,10000]m. Consider that the number of receiving stations L = 5, and the initial positions of the five receiving stations are [0,0]m, [282.84,282.84]m, [-282.84,282.84]m, [0,-400]m, [0,-800]m, and the moving speed is [100,0]m. Tm / s, the number of interception time slots K = 5, the interception interval is 2s, and the interception time is 3.9ms each time. The carrier frequency of the transmitted signal is 2GHz, and the signal bandwidth is 200kHz. The maximum point of the cost function is found by the grid search method to obtain the estimated position of the target. Since impulse noise does not have a finite variance, the generalized signal-to-noise ratio (GSNR) is defined here:

[0133]

[0134] in, is the variance of the signal, and γ is the dispersion parameter of the noise that satisfies the α-stable distribution. The simulation experiment uses the root mean square error (RMSE) to measure the positioning performance of the algorithm, which is defined as follows:

[0135]

[0136] Where Q is the number of Monte Carlo experiments, and in this experiment, Q = 100; is the estimated position of the target in the qth Monte Carlo experiment.

[0137] Parameter setting: given the pulse noise characteristic parameter α = 0.8, the core length parameter σ of the MCC criterion = 4.

[0138] Figure 3 FIG. 4 is a schematic diagram showing a comparison of positioning performances of various algorithms under different generalized signal-to-noise ratio conditions according to an embodiment of the present invention; Figure 3 As shown in Figure 1, the curves of RMSE of MCC-DPD algorithm and ML-DPD algorithm changing with generalized signal-to-noise ratio GSNR are given. It can be clearly seen that compared with ML-DPD algorithm, MCC-DPD algorithm can achieve higher positioning accuracy in the environment of impulse noise, especially under high signal-to-noise ratio conditions.

[0139] Parameter setting: Given the core length parameter σ of the MCC criterion = 4, the generalized signal-to-noise ratio GSNR of the received signal at the receiving station = 5dB.

[0140] Figure 4 FIG. 1 is a schematic diagram comparing the positioning performance of various algorithms under different characteristic parameter conditions according to an embodiment of the present invention; Figure 4As shown in the figure, the curves of RMSE of MCC-DPD algorithm and ML-DPD algorithm as the characteristic parameter α changes are given. It can be clearly seen that compared with the ML-DPD algorithm, the MCC-DPD algorithm can effectively improve the positioning accuracy in a strong pulse noise environment of 0.6≤α≤1; as α increases, the impulse of the pulse noise gradually weakens, and the positioning accuracy of the ML-DPD algorithm gradually improves. Note that when α=2, the pulse noise degenerates into Gaussian noise. From Figure 4 It can be observed that when α ≥ 1.7, the positioning accuracy of the two algorithms tends to be consistent.

[0141] The second aspect of the present invention discloses a positioning system based on maximum correlation entropy in an impulse noise environment. Figure 5 FIG. 4 is a structural diagram of a positioning system based on maximum correlation entropy in an impulse noise environment according to an embodiment of the present invention; Figure 5 As shown, the system 500 includes:

[0142] The first processing unit 501 is configured to call multiple receiving stations with known speeds to perform signal interception based on multiple time slots on their motion trajectories to receive time domain signals from a stationary radiation source target, and perform down-conversion processing on the received time domain signals according to a carrier frequency, thereby obtaining a baseband received signal;

[0143] The second processing unit 502 is configured to sample the baseband received signal and construct a cost function based on correlation entropy of each sampling point based on sampling data of each sampling point, thereby obtaining a global cost function;

[0144] The third processing unit 503 is configured to determine the estimated position of the stationary radiation source target by calculating the maximum point of the global cost function, wherein the estimated position of the stationary radiation source target represented by the maximum point of the global cost function is an estimate of the true position of the stationary radiation source target.

[0145] According to the system of the second aspect of the present invention, the speed and position of the multiple receiving stations with known speeds remain unchanged when performing the signal interception, and the baseband received signal after the down-conversion processing contains transmission delay, Doppler frequency shift, and channel additive noise, wherein the channel additive noise obeys the symmetrical α- The channel additive noise is stably distributed and the time domain signal is independent of each other.

[0146] According to the system of the second aspect of the present invention, the baseband received signal intercepted in the kth time slot by the lth receiving station among the L receiving stations with known speeds in the signal interception of K time slots is:

[0147]

[0148] where \(0 < t\leq T\), \(l = 1, 2, \ldots, L\); \(k = 1, 2, \ldots, K\), \(t\) is a continuous time variable, \(T\) is the signal truncation duration of each time slot, \(\tau\) l,k is the transmission delay of the time-domain signal received in the \(k\)th time slot, is the time-domain signal received in the \(k\)th time slot, \(f\) c is the carrier frequency, \(f\) l,k is the Doppler frequency shift of the time-domain signal received in the \(k\)th time slot, \(z\) l,k (t) is the additive impulse noise at the \(l\)th receiving station in the \(k\)th time slot.

[0149] For the system according to the second aspect of the present invention, the second processing unit 502 is specifically configured to sample the baseband received signal with \(T\) s as the sampling period. The sampling form of the baseband received signal of the \(l\)th receiving station in the \(k\)th time slot is:

[0150]

[0151] where \(l = 1, 2, \ldots, L\); \(k = 1, 2, \ldots, K\), the \(n\)th sampling point \(0\leq n\leq N - 1\), \(N = T / T\) s is the total number of sampling points;

[0152] The sampling data of the 1 - Nth sampling points is represented in vector form as:

[0153] \(r\) l,k \(=\mathbf{A}\) l,k \(\mathbf{F}\) l,k \(\mathbf{s}\) k +\mathbf{z}\) l,k

[0154] where \(l = 1, 2, \ldots, L\); \(k = 1, 2, \ldots, K\), and:

[0155]

[0156]

[0157]

[0158]

[0159] where \(diag\{\}\) represents a diagonal matrix, \(\mathbf{F}\) l,k is a downward shift operator, and \(\mathbf{F}\) l,k \(\mathbf{s}\) k is used to implement the shift of \(\mathbf{s}\) k .

[0160] According to the system of the second aspect of the present invention, the second processing unit 502 is specifically configured to perform sampling based on multiple sampling points on the baseband received signal of each time slot in the multiple time slots, and select the same core length parameter for the cost function based on correlation entropy constructed for each sampling point, then the cost function based on correlation entropy of the i-th sampling point in the k-th time slot is:

[0161]

[0162] Where k = 1, 2, ..., K, i = 1, 2, ..., N, l = 1, 2, ..., L, e l,k,i (p)=(r l,k -A l,k F l,k s k ) i Represents the sampling data of the lth receiver at each sampling point in each time slot.

[0163] According to the system of the second aspect of the present invention, the global cost function is:

[0164]

[0165] in, is the cost function based on correlation entropy of the lth receiver.

[0166] According to the system of the second aspect of the present invention, the estimated position of the stationary radiation source target is:

[0167]

[0168] in, Characterize the true position of the stationary radiation source target.

[0169] The third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the positioning method based on maximum correlation entropy in an impulse noise environment described in any one of the first aspects of the present disclosure are implemented.

[0170] Figure 6 is a structural diagram of an electronic device according to an embodiment of the present invention, such as Figure 6As shown, the electronic device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, near field communication (NFC) or other technologies. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covered on the display screen, or a button, a trackball or a touch pad set on the housing of the electronic device, or an external keyboard, touch pad or mouse, etc.

[0171] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a structural diagram of the part related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic device to which the technical solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0172] The fourth aspect of the present invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the positioning method based on maximum correlation entropy in an impulse noise environment described in any one of the first aspects of the present disclosure are implemented.

[0173] The technical solution provided by the present invention uses samples of received signal sampling to approximately estimate the correlation entropy in an impulse noise environment, and constructs a local cost function based on the correlation entropy; adds the local cost functions from different receivers and different time slots to obtain a global cost function; uses a grid search method to find the maximum value point of the global cost function as an estimate of the true position of the target; fully considers more practical impulse noise, and constructs a new cost function based on the maximum correlation entropy criterion, which can significantly improve the positioning effect in an impulse noise environment.

[0174] Please note that the technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. The above-mentioned embodiments only express several implementation methods of the present application, and their descriptions are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present application, several variations and improvements can be made, which all belong to the scope of protection of the present application. Therefore, the scope of protection of the patent in this application shall be based on the attached claims.

Claims

1. A positioning method based on maximum correlation entropy in an impulse noise environment, characterized in that: The method comprises: Step S1, multiple receiving stations with known speeds perform signal interception based on multiple time slots on their motion trajectories to receive time domain signals from a stationary radiation source target, and perform down-conversion processing on the received time domain signals according to the carrier frequency, thereby obtaining baseband received signals; Step S2, sampling the baseband received signal, and constructing a cost function based on correlation entropy of each sampling point based on sampling data of each sampling point, so as to obtain a global cost function; Step S3, determining the estimated position of the stationary radiation source target by calculating the maximum point of the global cost function, wherein the estimated position of the stationary radiation source target represented by the maximum point of the global cost function is an estimate of the true position of the stationary radiation source target.

2. The positioning method based on maximum correlation entropy in an impulse noise environment according to claim 1, characterized in that: In step S1, the speeds and positions of the multiple receiving stations with known speeds remain unchanged when performing the signal interception, and the baseband received signal after the down-conversion processing includes transmission delay, Doppler frequency shift, and channel additive noise, wherein the channel additive noise obeys the symmetric α - The channel additive noise is stably distributed and the time domain signal is independent of each other.

3. The positioning method based on maximum correlation entropy in an impulse noise environment according to claim 2, characterized in that: In step S1, the baseband received signal intercepted in the kth time slot by the lth receiving station among the L receiving stations with known speeds in the signal interception of K time slots is: where \(0 < t\leq T\), \(l = 1, 2,\cdots,L\); \(k = 1, 2,\cdots,K\), \(t\) is a continuous time variable, \(T\) is the signal interception duration of each time slot, \(\tau\) l，k is the transmission delay of the time-domain signal received in the \(k\)-th time slot, is the time-domain signal received in the \(k\)-th time slot, \(f\) c is the carrier frequency, \(f\) l，k is the Doppler frequency shift of the time-domain signal received in the \(k\)-th time slot, \(z\) l，k (t) is the additive impulse noise at the \(l\)-th receiving station in the \(k\)-th time slot.

4. The positioning method based on maximum correlation entropy in an impulse noise environment according to claim 3, characterized in that: In step S2: T s The baseband received signal is sampled at a sampling period, and the sampling form of the baseband received signal of the lth receiving station in the kth time slot is: Where l = 1, 2, ..., L, k = 1, 2, ..., K, the nth sampling point is the total number of sampling points; The sampling data of the 1st to Nth sampling points are expressed in vector form as follows: r l,k =A l,k F l,k s k +z l,k Where l = 1, 2, ..., L, k = 1, 2, ..., K, and: Where diag{} represents a diagonal matrix, F l，k is the downward shift operator, with F l，k s k Implementations k displacement.

5. The positioning method based on maximum correlation entropy in an impulse noise environment according to claim 4, characterized in that: In step S2, sampling based on multiple sampling points is performed on the baseband received signal of each time slot in the multiple time slots, and the same core length parameter is selected for the cost function based on correlation entropy constructed for each sampling point, then the cost function based on correlation entropy of the i-th sampling point in the k-th time slot is: Where k = 1, 2, ..., K, i = 1, 2, ..., N, l = 1, 2, ..., L, e l,k,i (p)=(r l,k -A l,k F l,k s k ) i Represents the sampling data of the lth receiver at each sampling point in each time slot.

6. The positioning method based on maximum correlation entropy in an impulse noise environment according to claim 5, characterized in that: In step S2, the global cost function is: in, is the cost function based on correlation entropy of the lth receiver.

7. The positioning method based on maximum correlation entropy in an impulse noise environment according to claim 6, characterized in that: In step S3, the estimated position of the stationary radiation source target is: in, Characterize the true position of the stationary radiation source target.

8. A positioning system based on maximum correlation entropy in an impulse noise environment, characterized in that: The system comprises: The first processing unit is configured to call a plurality of receiving stations with known speeds to perform signal interception based on a plurality of time slots on their motion trajectories to receive time domain signals from a stationary radiation source target, and perform down-conversion processing on the received time domain signals according to a carrier frequency, thereby obtaining baseband reception signals; A second processing unit is configured to sample the baseband received signal and construct a cost function based on correlation entropy of each sampling point based on sampling data of each sampling point, thereby obtaining a global cost function; The third processing unit is configured to determine the estimated position of the stationary radiation source target by calculating the maximum point of the global cost function, wherein the estimated position of the stationary radiation source target represented by the maximum point of the global cost function is an estimate of the true position of the stationary radiation source target.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the positioning method based on maximum correlation entropy in an impulse noise environment described in any one of claims 1 to 7 are implemented.

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 a positioning method based on maximum correlation entropy in an impulse noise environment according to any one of claims 1 to 7 are implemented.

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