Doppler inter-cell interference suppression method based on gaussian learning factor particle swarm optimization algorithm

Abstract

The application discloses a Doppler inter-cell interference suppression method based on a Gaussian learning factor particle swarm algorithm, and steps are as follows: pilot and data symbols are encoded to a resource grid in a two-dimensional time-delay Doppler domain through orthogonal time-frequency space modulation; at a transmitter, data on the time-delay Doppler domain grid is mapped to a time-frequency domain through inverse symplectic finite Fourier transform to obtain a time-frequency domain signal; a Heisenberg transform is performed on the time-frequency domain signal to obtain a time-domain sending signal; the time-domain sending signal is obtained after passing through a wireless channel to obtain a time-domain receiving signal; a Doppler compensation process is performed on the time-domain receiving signal by using a Doppler frequency shift to obtain a signal compensated by the Doppler; then the signal compensated by the Doppler is matched filtered with a receiving pulse, and sampling is performed to obtain a matched filter output signal; a symplectic Fourier transform is performed on the matched filter output signal to obtain a time-delay Doppler receiving signal, and the method can realize suppression of Doppler inter-cell interference to a certain extent.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, specifically to a method for suppressing inter-Doppler interference based on a Gaussian learning factor particle swarm optimization algorithm, and particularly to a method for suppressing inter-Doppler interference (IDI) generated by small data frames in orthogonal time-frequency space (OTFS) modulation. Background Technology

[0002] In vehicle-to-everything (V2X) scenarios, the relative motion between the transmitter, receiver, and scattering objects causes a severe Doppler effect. This causes the orthogonal subcarriers of Orthogonal Frequency Division Multiplexing (OFDM), widely used in 4G and 5G communication systems, to become non-orthogonal, leading to a direct performance collapse. In 2017, Hadani et al. proposed a new two-dimensional modulation technique—Orthogonal Time-Frequency Spatial Modulation (OTFS). OTFS introduces the concept of the Delay Doppler domain (DD). OTFS uses the Symptotic Finite Fourier Transform (SFFT) to transform data symbols from the time-frequency domain to the Delay Doppler domain. The channel is then transformed from a complex time-varying multipath channel in the time-frequency domain into a Delay Doppler channel characterized by a finite number of reflectors. The Delay Doppler domain channel is sparse and time-invariant, improving the system's resistance to the Doppler effect compared to OFDM systems.

[0003] However, in high-speed and low-latency vehicle-to-everything (V2X) scenarios, the Doppler resolution of OTFS becomes lower due to frame length limitations. The actual Doppler frequency shift is difficult to represent using integer taps in the time-delay Doppler domain, leading to fractional Doppler channels. In fractional Doppler channels, the peak value of the channel response for each path may not fall on the sampling point, and its sidelobes extend across the entire Doppler domain. This unavoidable diffusion is called inter-Doppler interference, which poses a challenge to system performance. Summary of the Invention

[0004] This invention addresses the performance degradation of the OTFS system in low-latency vehicle-to-everything (V2X) scenarios by proposing a Doppler interference suppression method based on the Gaussian learning factor particle swarm optimization algorithm. This method effectively suppresses Doppler interference caused by smaller data frames in the OTFS system during low-latency V2X scenarios, demonstrating significant practical application value.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A Doppler interference suppression method based on Gaussian learning factor particle swarm optimization algorithm includes the following steps:

[0007] Step 1: Encode pilot and data symbols onto the resource grid in the two-dimensional time-delay Doppler domain using orthogonal time-frequency space;

[0008] Step 2: On the transmitter, the data on the time-delay Doppler domain grid is mapped to the time-frequency domain through inverse symplectic finite Fourier transform to obtain the time-frequency domain signal;

[0009] Step 3: Perform Heisenberg transform on the time-frequency domain signal to obtain the time-domain transmitted signal;

[0010] Step 4: The time-domain transmitted signal is transmitted through the wireless channel to obtain the time-domain received signal;

[0011] Step 5: Perform Doppler compensation processing on the time-domain received signal using Doppler frequency shift to obtain the Doppler-compensated signal;

[0012] Step 6: First, generate the receiving pulse locally, then perform matched filtering on the Doppler-compensated signal and the receiving pulse, and sample to obtain the output signal of the matched filter;

[0013] Step 7: Perform a Sin-Fourier transform on the output signal of the matched filter to obtain the received signal in the time-delay Doppler domain.

[0014] Preferably, in step 5, the method for obtaining the Doppler frequency shift is as follows:

[0015]

[0016] Where v′ is the Doppler frequency shift, κ refers to fractional Doppler, N represents the total number of data symbols, and T represents the duration of a single symbol.

[0017] Preferably, the fractional Doppler κ is estimated using a Gaussian learning factor particle swarm optimization algorithm based on the criterion of minimizing Doppler leakage power of the pilot signal in the time-delay Doppler domain.

[0018] Preferably, in step 5, the Doppler leakage power of the pilot signal in the time-delay Doppler domain is used as the objective function to obtain the fractional Doppler κ.

[0019] Preferably, the objective function is established as follows:

[0020] First, the time-domain pilot signal transmitted through the wireless channel is converted to the time-delay Doppler domain. At this point, the input-output relationship of the time-domain pilot signal in the time-delay Doppler domain is expressed as follows:

[0021]

[0022] Where P represents the channel path count in the time-delay Doppler domain, k∈[1,M], l∈[1,N], l and k represent the time delay tap and Doppler tap of the received signal in the time-delay Doppler domain, respectively, M represents the number of subcarriers, N represents the number of symbols, and h i α is the channel amplitude corresponding to the tap. i Let represent the fractional Doppler attenuation coefficient of the signal amplitude for the i-th path. [·] M This represents the modulo M operation. and Let represent the time delay tap, integer, and fractional Doppler tap of the i-th path received in the time delay Doppler domain, respectively.

[0023] The total power of the time-domain pilot signal in the time-delay Doppler domain is:

[0024]

[0025] Then select the J signals y(k) with the largest receive pilot gain in the grid. m ,l m ), where J can be set to three times the number of channel multipaths, m∈[1J], then the power of the selected first J grid signals is:

[0026]

[0027] At this point, the leakage power of the time-domain pilot signal, which is also the objective function, can be obtained as follows:

[0028] Q k (κ)=Q s -Q m .

[0029] Preferably, when the channel has different fractional Doppler K values, the pilot signal will have different leakage power Q. k (κ), when κ=0, the leakage power of the pilot signal will also reach its minimum value. Specifically, a particle swarm optimization algorithm based on Gaussian learning factor is used to search for κ to make the objective function Q k (κ) is minimized, i.e.:

[0030]

[0031] As a preferred embodiment, the Gaussian learning factor particle swarm optimization algorithm is used to calculate the fractional Doppler κ as follows:

[0032] (1) Set the maximum number of iterations T = 40, the population size Y = 10, and the maximum speed v max =0.1, position vector upper and lower limits b h =0.5, b l=-0.5, inertia coefficient W=2, learning factor C1=C2=2;

[0033] (2) First, initialize the velocity of each particle to v. i =rand(0,1)v max The position is x i =rand(0,1)(b h -b l )+b l Set the current position as the historical best position P. i =x i Calculate x i The corresponding fitness function Q i , where position x i and fitness function Q i Corresponding to fractional Doppler κ and objective function Q respectively k (κ);

[0034] (3) For each iteration t∈[1,T], the learning factors C1 and C2 are updated using a Gaussian distribution:

[0035]

[0036] In the formula: C1 and C2 are the self-Gaussian learning factor and the global Gaussian learning factor, respectively.

[0037] (4) For each particle, update its velocity according to the following formula:

[0038]

[0039] when make

[0040] In the formula: t is the current iteration number, and r1 and r2 are random numbers uniformly distributed in (0,1).

[0041] (5) Update the position x of each particle i :

[0042]

[0043] (6) Then calculate the new position x i+1 The fitness function Q under t+1 ;

[0044] (7) Update the individual historical best position of each particle:

[0045] When Q t+1 <Q t hour, Otherwise P it+1 =P i t

[0046] (8) Update the historical best position of particles in the entire population:

[0047]

[0048] (9) Repeat steps (3) to (8) until t > T.

[0049] (10) Finally, the global optimal fitness and its corresponding position are obtained. The globally optimal position obtained at this point is also the magnitude of the channel fractional Doppler, i.e.

[0050] Preferably, in step 5, the Doppler compensation method for the time-domain data signal is as follows:

[0051] r′(t)=r(t)e j2π(-v′)(t-τ)

[0052] =∫∫h(τ,v)x(t-τ)e j2π(v+v′)(t-τ) e j2π(-v′)(t-τ) dvdτ

[0053] =∫∫h(τ,v)x(t-τ)e j2πv(t-τ) dvdτ

[0054] in, Representing integer and fractional Doppler frequency shifts respectively, the Doppler tap size of the signal after Doppler compensation is... When the channel environment is single-path, the received signal only has integer Doppler.

[0055] Preferably, in step 6, the time-domain data signal and the received pulse are matched and filtered. If the received pulse is g tx From (t), we can obtain its mutual ambiguity function with the received signal as:

[0056]

[0057] Then, sampling is performed at intervals t = mT and f = nΔf to obtain the output signal of the matched filter:

[0058]

[0059] Preferably, in step 7, performing a Sine Fourier transform on the matched filter output signal y[m,n] yields the received signal in the time-delay Doppler domain:

[0060]

[0061] This invention has the following characteristics and beneficial effects:

[0062] The proposed Doppler interference suppression method uses a Gaussian learning factor particle swarm optimization algorithm to estimate fractional Doppler based on the criterion of minimizing Doppler leakage power of pilot signals in the time-delay Doppler domain. Then, Doppler compensation is performed on the received data signal, thereby improving the sparsity of the channel in the time-delay Doppler domain. This effectively suppresses the Doppler interference problem caused by the small data frame size of the OTFS system, thereby reducing the complexity of channel estimation and achieving a better system bit error rate. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a system model diagram of the Doppler interference suppression method based on Gaussian learning factor particle swarm algorithm in this invention;

[0065] Figure 2 This is a schematic diagram of the pilot and data frame structure of orthogonal time-frequency spatial modulation in this invention;

[0066] Figure 3 This is a channel amplitude diagram in the time-delay Doppler domain of the pilot signal before and after Gaussian learning factor particle swarm Doppler compensation in a single-path scenario according to the present invention.

[0067] Figure 4 This is a performance comparison chart of the Gaussian learning factor particle swarm algorithm and the traditional particle swarm algorithm in the application scenario of this invention;

[0068] Figure 5 This is a comparison chart of bit error rates under different Doppler compensations when the size of the orthogonal time-frequency spatial modulation data frame is M=N=16 in this invention. Detailed Implementation

[0069] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0070] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0071] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0072] This invention provides a Doppler interference suppression method based on Gaussian learning factor particle swarm optimization algorithm. Firstly, it employs... Figure 1 The diagram shows the system model framework of orthogonal time-frequency spatial modulation, which uses pilot signals plus data frames for data transmission. The structures of the pilot signals and data frames are as follows: Figure 2 As shown, both experience the same channel. The specific steps include:

[0073] Step 1: OTFS encodes pilot and data symbols onto a resource grid in a two-dimensional time-delay Doppler domain, as shown below:

[0074]

[0075] Where M represents the number of subcarriers, N represents the number of symbols, Δf represents the subcarrier spacing, T represents the duration of a single symbol, and l and k represent the delay tap and Doppler tap of the received signal in the delay-Doppler domain, respectively.

[0076] Step 2: At the transmitter, the data on the time-delay Doppler domain grid is mapped to the time-frequency domain through an inverse symplectic finite Fourier transform (ISFFT). The time-frequency domain signal is:

[0077]

[0078] Step 3: Transform the obtained time-frequency domain signal to the time domain using the Heisenberg transform. The time-domain signal is:

[0079]

[0080] Among them, g tx (t) represents the transmitted pulse.

[0081] Step 4: The time-domain transmitted signal x(t) passes through the channel to obtain the time-domain received signal r(t), which includes the time-domain pilot signal and the time-domain data signal, and can be expressed as:

[0082] r(t)=∫ ν ∫ τ h(τ,ν)x(t-τ)e j2πν(t-τ) dτdν

[0083] Step 5: Use the Gaussian learning factor particle swarm algorithm to estimate the channel fractional Doppler κ based on the criterion of minimizing Doppler leakage power of the pilot signal in the time-delay Doppler domain.

[0084] First, let's introduce how to establish the objective function:

[0085] When the time-domain pilot signal transmitted through the wireless channel is converted to the time-delay Doppler domain, the Doppler resolution of the system decreases as the data frame size in the orthogonal time-frequency space decreases. After the transmitted signal is transmitted through the wireless channel, the received signal inevitably has a fractional Doppler frequency shift. At this time, the input-output relationship of the signal in the time-delay Doppler domain can be expressed as:

[0086]

[0087] Where P represents the channel path count in the time-delay Doppler domain, k∈[1,M], l∈[1,N], l and k represent the time delay tap and Doppler tap of the received signal in the time-delay Doppler domain, respectively, M represents the number of subcarriers, N represents the number of symbols, and h i α is the channel amplitude corresponding to the tap. i Let represent the fractional Doppler attenuation coefficient of the signal amplitude for the i-th path. [·] M This represents the modulo M operation. and Let these represent the delay taps, integer and fractional Doppler taps, and fractional Doppler taps received in the delay-Doppler domain for the i-th path, respectively. This represents the offset that occurs at an integer Doppler tap.

[0088] The total power of the pilot signal in the time-delay Doppler domain is:

[0089]

[0090] Then select the J signals y(k) with the largest receive pilot gain in the grid. m ,l m ), where J can be set to three times the number of channel multipaths, m∈[1J], then the power of the selected first J grid signals is:

[0091]

[0092] At this point, the leakage power of the pilot signal, which is also the objective function, can be obtained as follows:

[0093] Q k (κ)=Q s -Q m

[0094] When the channel has different fractional Doppler K values, the pilot signal will have different leakage power Q. k (κ), when κ=0, the leakage power of the pilot signal will also reach its minimum value. Specifically, a particle swarm optimization algorithm based on Gaussian learning factor is used to search for κ to make the objective function Q k (κ) is minimized, i.e.:

[0095]

[0096] Next, the specific details of the Gaussian learning factor particle swarm algorithm in this embodiment are as follows:

[0097] Step 5-1: Set the maximum number of iterations T = 40, the population size Y = 10, and the maximum speed v. max =0.1, position vector upper and lower limits b h =0.5, b l = -0.5, inertia coefficient W = 2, learning factor C1 = C2 = 2.

[0098] Step 5-2: First, initialize each particle x. i The speed is v i =rand(0,1)v max The position is x i =rand(0,1)(b h -b l )+b l Set the current position as the historical best position P. i =x i Calculate x i The corresponding fitness function Q i Where position x i and fitness function Q i Corresponding to fractional Doppler κ and objective function Q respectively k (κ).

[0099] Step 5-3: For each iteration t∈[1,T], update the learning factors C1 and C2 using a Gaussian distribution:

[0100]

[0101] In the formula: C1 and C2 are the self-Gaussian learning factor and the global Gaussian learning factor, respectively.

[0102] Step 5-4: For each particle, update its velocity according to the following formula:

[0103]

[0104] when make

[0105] In the formula: t is the current iteration number, and r1 and r2 are random numbers uniformly distributed in (0,1).

[0106] Step 5-5: Update the position x of each particle i :

[0107]

[0108] Steps 5-6: Then calculate the new position x i+1 The fitness function Q under t+1 .

[0109] Steps 5-7: Update the individual historical best position for each particle:

[0110] When Q t+1 <Q t hour, Otherwise P i t+1 =P i t

[0111] Steps 5-8: Update the historical best position of particles in the entire population:

[0112]

[0113] Step 5-9: Repeat steps 5-3 to 5-8 until t > T.

[0114] Steps 5-10: Finally, obtain the global optimal fitness and its corresponding position. The globally optimal position obtained at this point is also the magnitude of the channel fractional Doppler, i.e. Figure 3The diagram shows the channel amplitude in the time-delay Doppler domain of the pilot signal before and after Gaussian learning factor particle swarm Doppler compensation in a single-path scenario according to the present invention. The results show that the effect of inter-Doppler interference suppression is achieved. Figure 4 The graph shows a performance comparison between the Gaussian learning factor particle swarm algorithm and the traditional particle swarm algorithm in the scenario described in this invention. The results show that the Gaussian learning factor particle swarm algorithm has stronger global search capability and local exploitation capability.

[0115] Step 6: Obtain the fractional Doppler κ of the channel from Step 5, and convert it into a time-domain Doppler frequency shift v′:

[0116]

[0117] Step 7: Using the Doppler frequency shift v′, perform the following Doppler compensation processing on the received signal r(t) in the time domain:

[0118] r′(t)=r(t)e j2π(-v′)(t-τ)

[0119] =∫∫h(τ,v)x(t-τ)e j2π(v+v′)(t-τ) e j2π(-v′)(t-τ) dvdτ

[0120] =∫∫h(τ,v)x(t-τ)e j2πv(t-τ) dvdτ

[0121] in, Let represent the integer and fractional Doppler frequency shifts, respectively. The Doppler tap size of the signal after Doppler compensation is . When the channel environment is single-path, the received signal only has integer Doppler.

[0122] Step 8: Perform matched filtering on the received signal and the received pulse. If the received pulse is g tx From (t), we can obtain its mutual ambiguity function with the received signal as:

[0123]

[0124] Step 9: Then, sampling is performed at intervals t = mT and f = nΔf to obtain the output signal of the matched filter:

[0125]

[0126] Step 10: Performing a Sine Fourier transform on the matched filter output signal y[m,n] yields the received signal in the time-delay Doppler domain:

[0127]

[0128] Figure 5This is a comparison chart of bit error rates under different Doppler compensations when the data frame of orthogonal time-frequency spatial modulation is M=N=16 in this invention. Simulation results show that the invention has achieved the expected results.

[0129] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions, and variations can be made to these embodiments, including components, without departing from the principles and spirit of the present invention, and these variations still fall within the protection scope of the present invention.

Claims

1. A method for Doppler inter-cell interference mitigation based on Gaussain learning factor particle swarm optimization algorithm, characterized in that, The method comprises the following steps: Step 1, encoding pilot and data symbols into a resource grid in two-dimensional time-delay Doppler domain through orthogonal time-frequency space; Step 2, at the transmitter, data on the time-delay Doppler domain grid is mapped to a time-frequency domain through inverse symplectic finite Fourier transform to obtain a time-frequency domain signal; Step 3, performing a Heisenberg transform on the time-frequency domain signal to obtain a time-domain transmission signal; Step 4, obtaining a time-domain receiving signal after the time-domain transmission signal passes through a wireless channel, wherein the time-domain receiving signal comprises a time-domain pilot signal and a time-domain data signal; Step 5, performing Doppler compensation processing on the time-domain receiving signal by using a Doppler shift to obtain a Doppler-compensated signal; the Doppler shift is obtained according to a fractional Doppler, and a target function of the fractional Doppler is established in the following manner: First, convert the time-domain pilot signal passing through the wireless channel into the time-delay Doppler domain, wherein an input-output relationship of the time-domain pilot signal in the time-delay Doppler domain is represented as: where P denotes the count of channel paths in the delay-Doppler domain, k ∈ [1, M], l ∈ [1, N], l and k denote the delay tap and Doppler tap of the received signal in the delay-Doppler domain, respectively, M denotes the number of subcarriers, N denotes the number of symbols, h i denotes the channel amplitude for the corresponding tap, α i denotes the attenuation factor of the fractional Doppler on the signal amplitude for the i-th ray, [·] M denotes the mod M operation, and denote the delay tap, integer and fractional Doppler tap, respectively, of the i-th path received in the delay-Doppler domain. The total power of the time-domain pilot signal in the time-delay Doppler domain is: Then select the first J signals y(k m ,l m ) with the largest pilot gain in the grid, where J can be set as three times of the channel multipath number, m∈[1 J], the power of the first J selected grid signals is: At this time, the leakage power of the time-domain pilot signal, i.e., the target function, is: Q k (κ) = Q s - Q m ; the Gaussian learning factor particle swarm optimization algorithm search fractional Doppler κ method is as follows: (1) Set the maximum number of iterations T = 40, population size Y = 10, maximum speed v max = 0.1, position vector upper and lower limit b h = 0.5, b l = -0.5, inertia coefficient W = 2, learning factor C1 = C2 = 2; (2) First, initialize the velocity of each particle as v i = rand(0, 1)v max , the position as x i = rand(0, 1)(b h - b l ) + b l , set the current position as the historical optimal position P i = x i , calculate the corresponding fitness function Q i of x i , wherein the position x i and the fitness function Q i correspond to the fractional Doppler κ and the objective function Q k (κ), respectively; (3) for each iteration t∈[1,T], a Gaussian distribution is used to update learning factors C1 and C2: In the formula, C1 and C2 are respectively a self-Gaussian learning factor and a global Gaussian learning factor; (4) for each particle, update its speed according to the following formula: When Let In the formula, t is a current iteration number, r1 and r2 are random numbers uniformly distributed in (0, 1); (5) update the position x of each particle i : (6) Then the new position x is calculated i+1 under the fitness function Q t+1 ; (7) update the individual historical optimal position of each particle: When Q t+1 When Q t , Otherwise P i t+1 = P i t (8) update the historical optimal position of the particles in the entire population: (9) repeat steps (3) to (8) until t>T; (10) Finally, the global optimal fitness and its corresponding position are found The global optimal position obtained at this time is also the size of the channel score Doppler, i.e. Step 6, first locally generate a receiving pulse, then perform matched filtering on the Doppler-compensated signal and the receiving pulse, and sample to obtain a matched filter output signal; Step 7, performing symplectic Fourier transform on the matched filter output signal to obtain a time-delay Doppler domain receiving signal.

2. The method of claim 1, wherein the Gaussian learning factor particle swarm optimization algorithm is based on, In step 5, the method for obtaining the Doppler shift is: Wherein, v' is the Doppler shift, κ refers to the fractional Doppler, N represents the total number of data symbols, and T represents the duration of a single symbol.

3. The method of claim 2, wherein the Gaussian learning factor based particle swarm optimization algorithm is characterized by, The fractional Doppler κ is estimated based on the minimum criterion of the Doppler leakage power of the pilot signal in the time-delay Doppler domain by using a Gaussian learning factor particle swarm optimization algorithm.

4. The method of claim 3, wherein the Gaussian learning factor particle swarm optimization algorithm is based on, In step 5, the Doppler leakage power of the pilot signal in the time-delay Doppler domain is used as a target function for obtaining the fractional Doppler κ.

5. The method of claim 4, wherein the Gaussian learning factor particle swarm optimization algorithm is based on, When the channel has different fractional Doppler κ, the pilot signal will have different leakage power Q k (κ), when κ = 0, the pilot signal leakage power will also take the minimum value, the specific search based on Gaussian learning factor particle swarm optimization algorithm κ makes the target function Q k (κ) minimization, namely:

6. The method of claim 2, wherein the Gaussian learning factor based particle swarm optimization algorithm is characterized by, In step 5, the method for performing Doppler compensation on the time-domain receiving signal is: wherein, respectively represent integer, fractional Doppler shift, and the Doppler tap size of the Doppler-compensated signal is When the channel environment is single-path, there is only integer Doppler in the received signal.

7. The method of claim 1, wherein the Gauss-Learning Factor Particle Swarm Optimization algorithm is applied to suppress the inter-Doppler interference. In step 6, the time-domain Doppler-compensated signal is matched filtered with the received pulse, if the received pulse is g(t), the cross ambiguity function of g(t) and the received signal is given by: tx (t), the cross ambiguity function of g(t) and the received signal is given by: Then sample at intervals t=mT, f=nΔf to obtain a matched filter output signal:

8. The method of claim 1, wherein the Gauss-Learning Factor Particle Swarm Optimization algorithm is based on, In step 7, performing symplectic Fourier transform on the matched filter output signal y[m,n] can obtain a time-delay Doppler domain receiving signal:

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