Satellite communication-oriented differential time hopping estimation and multi-hop coherent combining method and device

Abstract

The application discloses a satellite communication-oriented differential time hopping estimation and multi-hop coherent combination method and device, and belongs to the field of digital signal processing. A sending end repeatedly sends information symbols in discontinuous time slots, and the time interval of each sending is randomly changed within a predetermined range according to an agreement between the two communication parties. After the time hopping signal is transmitted through an AWGN channel, a receiving end jointly estimates the time interval of adjacent two hops and the multi-hop carrier phase by using a cross-entropy iteration method. A mapping relationship between the time interval of the hop and the carrier phase and binary quantization bits is established, a binary quantization bit generation probability is iteratively updated, the estimated values of the time interval of the hop and the multi-hop carrier phase are regenerated, multi-hop coherent combination is performed, and the parameter estimated value with the minimum combination SNR loss is selected as a global optimal vector group. According to the global optimal vector group, multi-hop coherent combination is realized to obtain SNR gain. The application can reduce the complexity compared with a traditional traversal method.

Description

TECHNICAL FIELD

[0001] The present application relates to a cross-entropy iterative auxiliary differential time hopping estimation and coherent combining method and device, and belongs to the field of digital signal processing. BACKGROUND

[0002] In the satellite communication scenario, in order to avoid the interference of non-cooperative parties, time hopping technology is adopted. The time hopping communication system selects the signal transmission time discontinuously based on the time hopping pattern hopping, and can effectively avoid the interference signal through the division of time. For the jammer, the continuous transmission of the interference signal can produce a strong interference effect on the system, which increases the interference cost. The sending end disperses the signal in different time slots for repeated transmission, and in order to further improve the randomness of the signal in time, the time interval of each transmission is randomly changed within a predetermined range. The receiving end needs to estimate the transmission time interval in order to fully utilize all the signals dispersed in each time slot for combining. In addition, for the receiving end, the carrier phase of each hop signal is random, and in order to obtain higher signal-to-noise ratio gain, the receiving end performs coherent combining and demodulation on the multi-hop signals in different time slots, which requires accurate estimation and compensation of the carrier phase of each hop signal. Therefore, in order to improve the reliability of communication, the receiving end needs to jointly estimate the transmission time interval of each time and the carrier phase of each hop signal, and the higher parameter estimation dimension leads to increased complexity.

[0003] Cross-entropy, as a global random optimization algorithm, is widely used in multi-dimensional, large-scale, and nonlinear optimization problems. The optimization problem is converted into a probability estimation problem of a small probability event, that is, the occurrence of the optimal solution is a small probability event. An adaptive key sampling strategy is adopted, and samples with better selection effect are selected according to the target function for updating the probability distribution. New samples are generated using the updated probability distribution, and iteration is performed in turn to make the optimal solution more likely to occur. The application of cross-entropy to the joint estimation of multi-hop carrier phase and time hopping interval can reduce the complexity of parameter estimation. Based on this, a cross-entropy iterative auxiliary differential time hopping estimation and multi-hop coherent combining method is proposed. SUMMARY

[0004] In view of the problem that the estimation deviation of the time hopping interval and the carrier phase in the time hopping communication system causes the loss of the combined signal-to-noise ratio, the purpose of the present application is to provide a differential time hopping estimation and multi-hop coherent combining method and device for satellite communication. In the cross-entropy iteration process, the probability distribution of the parameters to be estimated is adaptively adjusted to obtain a time hopping interval and a multi-hop carrier phase estimation quantity that causes less loss of the combined signal-to-noise ratio, so as to realize the synchronization of the time hopping interval and the multi-hop carrier phase. On this basis, the signal-to-noise ratio gain is obtained through the coherent combining of the multi-hop signals, and the complexity is reduced compared with the traditional exhaustive method.

[0005] The purpose of the present application is achieved by the following technical solutions:

[0006] The application discloses a satellite communication-oriented differential time hopping estimation and multi-hop coherent combination method, wherein a sending end repeatedly transmits information symbols in discontinuous multiple time slots, and time intervals of each transmission are randomly changed within a predetermined range according to an agreement between the two communication parties.

[0007] The application discloses a satellite communication-oriented time hopping interval estimation and multi-hop coherent combination method, and comprises the following steps.

[0008] Step one: after BPSK modulation of information symbols to be transmitted, the information symbols are repeatedly transmitted in time discontinuous multiple time slots, each time slot is referred to as a hop, and time intervals between adjacent two-hop signals are randomly changed within a predetermined range to generate a time hopping interval random BPSK modulated time hopping signal.

[0009] The time hopping interval random BPSK modulated time hopping signal is shown in the following formula:

[0010]

[0011] The signal is repeatedly transmitted in M hops, and K symbols are transmitted in each hop, b k represents the kth BPSK modulated symbol, b k ∈{-1,+1}, w(t) is a single pulse waveform, T s is a single symbol duration, L m is the number of time slots before the mth hop, T p is a single time slot duration, f0 is a transmitting signal carrier frequency, and φ 0,m is a carrier initial phase of the mth hop transmitting signal.

[0012] Let L δ,m represent a time hopping interval, that is, the number of time slots between the mth hop and the m-1th hop, The two communication parties agree to randomly change within a predetermined range.

[0013] Step two: the BPSK modulated time hopping signal obtained in step one is sent into an AWGN channel, and a signal transmitted through the AWGN channel is received.

[0014] The received signal after AWGN channel is expressed as

[0015]

[0016] where A is the amplitude of the transmitted signal when it reaches the receiver, τ is the time delay of the receiver relative to the transmitter, f d is the frequency offset of the received signal relative to the transmitted signal, φ m is the carrier initial phase of the mth hop received signal, and n(t) represents an additive complex Gaussian white noise with a mean of 0 and a variance of The time delay τ and the frequency offset f d of the receiver have been ideally synchronized.

[0017] Step three: the carrier initial phase search range of each hop signal of the received signal is [0, 2π), and the search range of the time interval between adjacent two hop signals is agreed by the two communicating parties as {1, 2,..., L δ，max}, the carrier initial phase of the mth hop and the time interval search parameters are respectively where The search value is mapped to a binary code, D1 and D2 are the number of binary quantization bits, each carrier phase search value corresponds to a D1-bit binary number, and each hop time interval search value corresponds to a D2-bit binary number, that is, the parameter quantization of the hop time interval and the carrier phase of each hop signal is realized.

[0018] φ m,d represents the dth carrier initial phase estimation value of the mth hop, d ∈ {1, 2,..., N1}, and

[0019]

[0020] d-1 is expressed as a D1-bit binary number, and the mapping relationship between φ m,d and the D1-bit binary number is established according to the above formula.

[0021] L δ,m,u represents the uth hop time interval estimation value of the mth hop, u ∈ {1, 2,..., N2}, and N2 = L δ,max , and

[0022] L δ,m,u = u (4)

[0023] u-1 is expressed as a D2-bit binary number, and the mapping relationship between L δ,m,u and the D2-bit binary number is established according to the above formula.

[0024] Step four: generate N c groups of binary codes according to the preset initial probability of binary quantization bits, called candidate groups, from N cN groups of binary code mapping get M-hop carrier phase and hop interval estimation c Group initial solution, cross-entropy iterative process by updating the binary quantization bit generation probability to regenerate the parameter solution.

[0025] The binary quantization bit generation probability of the i-th cross-entropy iteration is The probability of taking value 1 is Randomly generate binary quantization bits Indicates the generation The probability of taking value 1 is Wherein Indicates the vthbit of the mthhop carrier phase quantization bit of the i-th iteration, A group of hop interval quantization bits of the mthhop Wherein Indicates the lthbit of the mthhop carrier phase quantization bit of the i-th iteration,

[0026] The carrier phase estimation value is obtained by

[0027] The hop interval estimation value is obtained by

[0028]

[0029] According to formula (5) (6), the M-hop carrier phase and hop interval estimation value is obtained by

[0030]

[0031] Step five: generate the local pulse template according to the hop interval estimation value obtained in step four, and perform correlation operation on the received signal, that is, match the local template with the time slot position of the received signal, and compensate the signal carrier phase of the selected time slot according to the carrier phase estimation value, and coherently combine the multiple-hop signals after compensation.

[0032] The hop interval estimation value of each hop is obtained by The number of time slots before the mthhop is obtained

[0033]

[0034] The receiving end generates a local pulse template according to Correlation operation and phase compensation are performed on the kthdata of all M hops

[0035] ​​​

[0036] The kth data combined signal of all M hops is further represented as

[0037]

[0038] Wherein P≤M, P is the number of time slot positions of useful signals in M-hop data matching the local pulse template.

[0039] Step six: SNR estimation is performed on the combined signal obtained in step five, the difference between the estimated actual SNR and the theoretical combined SNR of M hops is calculated, and the N c group carrier phase and hop time interval parameters are solved, and the combined SNR loss corresponding to the solution is taken as the objective function of cross-entropy iteration.

[0040] If the hop time interval estimation is accurate, i.e. then Thus, P=M in formula (9), and if the carrier phase estimation is accurate, i.e. then the kth data combined signal of all M hops is

[0041]

[0042] Let γ1 represent the single-hop SNR, and the theoretical SNR of the combined signal is

[0043] γ comb = γ1+10log 10 (M) (11)

[0044] The SNR of the actual combined signal is estimated using the method shown in formula (12)

[0045]

[0046] Wherein mean() represents taking the mean value, and var() represents taking the variance, and the combined SNR loss is

[0047]

[0048] According to formulas (11), (12), and (13), N c group parameters are used to obtain N c combined SNR loss estimation results, and the combined SNR loss is taken as the objective function of cross-entropy iteration.

[0049] Step seven: In order to minimize the combined SNR loss, samples with better objective function effects are selected from the candidate group to update the generation probability of binary quantization bits. N cSort the combined signal-to-noise ratio (SNR) losses from smallest to largest, and select the top N SNR losses with the smallest combined SNR losses. e The hop interval and carrier phase quantization parameters corresponding to the group are used as the preferred group. The probability of each bit in the binary quantization bits of the preferred group being 0 or 1 is calculated. Based on the probability obtained from the statistics of the preferred group, the binary code generation probability of the next cross-entropy iteration is obtained.

[0050] The multidimensional parameter estimation problem of carrier phase and hop interval is transformed into selecting the optimal carrier phase and hop interval quantization bits b that minimizes the signal-to-noise ratio loss during combining, i.e.

[0051]

[0052] in, This is the set of values ​​for the M-hop carrier phase and hop interval quantization parameters.

[0053] To obtain the carrier phase and hop interval quantization parameters that minimize the combined signal-to-noise ratio loss, the binary quantization bit generation probability is iterated to make the probability distribution of the parameters to be estimated approximate the optimal probability distribution. The probability iteration rule is as follows:

[0054]

[0055] Where, I{γ loss ≤T H} is a binary indicator function, T H For threshold

[0056]

[0057] The probability of generating binary quantized bits is iteratively updated according to the probability iteration rule shown in formula (15) in the candidate group N. c From the group of binary quantized bits, select N that minimizes the combined signal-to-noise ratio loss. e Group of binary quantized bits, threshold T in formula (15) H With N c and N e Related. The sorting result of the merged signal-to-noise ratio loss obtained from the i-th cross-entropy iteration is denoted as... The corresponding hop interval and carrier phase binary quantization bits are denoted as: Depend on The estimated value obtained by mapping is denoted as l∈{1, 2, ..., N} c The top N with the smallest combined signal-to-noise ratio loss e Group of binary quantized bits form the preferred group The corresponding time hop interval and carrier phase estimate are Calculate the probability p that each quantized bit of the preferred group is 1. i+1

[0058]

[0059] p i+1 Smooth processing, update the generation probability of next iteration

[0060]

[0061] Wherein, alpha is smoothing coefficient, alpha belongs to (0,1], alpha is bigger, convergence speed is faster.

[0062] Step eight: avoid falling into local optimal solution by setting global optimal vector group, select the hop time interval and carrier phase estimation value with minimum combined SNR loss in cross entropy iteration process as global optimal vector group, when Each element in the formula becomes 0 or 1, or the iteration number reaches the maximum iteration number set, cross entropy iteration stops, and the global optimal vector group is outputted.The global optimal vector group obtained by random search is used for time slot selection and carrier phase compensation to received signal, and the SNR is improved by multi-hop coherent combination.

[0063] Set For recording the minimum combined SNR loss from the first iteration to the i-th iteration, set As the minimum combined SNR loss from the first iteration to the i-th iteration, set Corresponding hop time interval and carrier phase estimation value, when the minimum value of N c Combined SNR loss obtained in the i-th iteration in step five meets , the value of Is updated to And the value of Is updated to Otherwise And Invariable. Calculate the number N of elements in the formula as 0 or 1, when N < M (D1+D2) and the current iteration number is less than the maximum iteration number set, the value of As the input condition of next cross entropy iteration, repeat steps four to eight. When N=M (D1+D2) or the current iteration number is equal to the maximum iteration number set, cross entropy iteration stops, and the global optimal vector group Is outputted.

[0064] The application also discloses a satellite communication-oriented differential hop time estimation and multi-hop coherent combination device for realizing the satellite communication-oriented differential hop time estimation and multi-hop coherent combination method.The satellite communication-oriented hop time interval estimation and multi-hop coherent combination device comprises a parameter quantization unit, a parameter compensation and multi-hop combination unit, an SNR estimation unit and a cross entropy iteration unit.

[0065] The parameter quantization unit is configured to establish a mapping relationship between the hop interval and the carrier phase search value and the binary quantization bit.

[0066] The parameter compensation and multi-hop combining unit is configured to perform parameter compensation and multi-hop combining on the estimation result obtained by the cross entropy iteration unit, to perform correlation operation on the local pulse template generated according to the hop interval and the received signal, to select a time slot where the signal is located, to compensate the carrier phase of the signal in the selected time slot according to the carrier phase estimation value, and to perform coherent combining on the multi-hop signal after compensation.

[0067] The signal-to-noise ratio estimation unit is configured to perform signal-to-noise ratio estimation on the combined signal obtained by the parameter compensation and combining unit, to calculate the difference between the estimated signal-to-noise ratio and the theoretical signal-to-noise ratio, and to obtain the combined signal-to-noise ratio loss.

[0068] The cross entropy iteration unit is configured to generate a probability generation binary code from the binary quantization bit, to generate the hop interval and the carrier phase estimation value according to the binary code and the mapping relationship obtained by the parameter quantization unit, to output the estimation value to the parameter compensation and multi-hop combining unit, to take the combined signal-to-noise ratio loss calculated by the signal-to-noise ratio estimation unit as a target function, to select a sample with smaller combined signal-to-noise ratio loss as an optimal group, to generate the probability of binary quantization bit generation again according to the optimal group for the next iteration, to record the hop interval and the carrier phase estimation value corresponding to the minimum combined signal-to-noise ratio loss in the iteration process as a global optimal vector group, and to output the global optimal vector group after the iteration is stopped.

[0069] Advantages:

[0070] 1. The cross entropy iteration assisted differential hop time estimation and multi-hop coherent combining method and device disclosed in the application can make the solution with smaller combined signal-to-noise ratio loss more likely to occur by adaptively updating the probability distribution in the random search process, thereby realizing joint estimation of the time interval of the signals of two adjacent hops and the multi-hop carrier phase. Compared with the traditional traversal method which needs to search all possible parameter values one by one and has high complexity, the cross entropy iteration method can significantly reduce the number of searches and reduce the complexity of parameter estimation.

[0071] 2. The cross entropy iteration assisted differential hop time estimation and multi-hop coherent combining method and device disclosed in the application can perform coherent combining on the signals of multiple time slots on the basis of joint estimation of the hop interval and the carrier phase, demodulate the combined signals, and improve the demodulation signal-to-noise ratio through multi-hop coherent combining under the condition that the single-hop signal-to-noise ratio is low.

[0072] 3. In satellite communication scenarios, wireless communication links are susceptible to interference from non-cooperative parties. The cross-entropy iterative-assisted differential time-hop estimation and multi-hop coherent combining method and apparatus disclosed in this invention transmits signals discontinuously in the time domain with random time intervals. The receiving end uses a cross-entropy iterative method to jointly estimate the time-hop interval and carrier phase, thereby achieving multi-hop coherent combining and demodulation. For non-cooperative parties, the jammer needs to continuously transmit interference to achieve a strong interference effect, increasing the difficulty of interference from non-cooperative parties and improving the anti-interference capability of communication.

[0073] 4. The method and apparatus for differential time-hopping estimation and multi-hop coherent merging assisted by cross-entropy iteration disclosed in this invention randomly generates estimated values ​​for the time-hopping interval and carrier phase during the cross-entropy iteration process. The randomness of parameter generation may lead to the appearance of a better solution than the parameter solution obtained in the last iteration during the intermediate process of the iteration. By setting a global optimal vector group to select the solution that achieves the best effect on the objective function during the iteration process, it is possible to avoid the cross-entropy iteration getting trapped in local optima and reduce the parameter estimation error of the cross-entropy iteration. Attached Figure Description

[0074] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 A flowchart for joint estimation of hop interval and carrier phase assisted by cross-entropy iteration.

[0076] Figure 2 A block diagram of differential jump time estimation and multi-hop coherent merging aided by cross-entropy iteration.

[0077] Figure 3 This is a schematic diagram of the differential jump time interval.

[0078] Figure 4 To merge the signal-to-noise ratio loss iterative curves.

[0079] Figure 5 The combined bit error rate is assisted by cross-entropy iteration. Detailed Implementation

[0080] The present invention will be further described and described in detail below with reference to the accompanying drawings and embodiments.

[0081] like Figure 1 As shown in the figure, the specific implementation steps of the cross-entropy iteration-assisted differential time-hopping estimation and multi-hop coherent merging method disclosed in this embodiment are as follows:

[0082] Step one: the time slot structure of the time-hopping signal based on differential hop interval is shown in Figure 2 The M = 8, L δ,max = 4, K = 1000 in this embodiment, and the BPSK modulated time-hopping signal is generated as shown in formula (1). The 1000 BPSK modulated symbols to be transmitted are repeatedly transmitted in 8 non-continuous hops. The time slot interval between the adjacent two time-hopping signals is agreed to be in the range of L δ,m ∈{1,2,3,4}.

[0083] Step two: the BPSK modulated time-hopping signal obtained in step one is sent into the AWGN channel, and the signal transmitted through the AWGN channel is received. The received signal after the AWGN channel is shown in formula (2). The time delay τ and the frequency offset f d of the receiving end are ideally synchronized.

[0084] Step three: the carrier initial phase search range of each hop signal of the received signal is [0, 2π), and the search range of L δ,m is agreed to be {1, 2, 3, 4}. In this embodiment, D1 = 6, D2 = 2, N1 = 64, N2 = 4, the carrier initial phase and the hop interval search parameters of the mth hop received signal are respectively The search values are mapped to binary codes. Each carrier phase search value corresponds to a 6-bit binary number, and each hop interval search value corresponds to a 2-bit binary number, that is, the parameter quantization of the hop interval and the carrier phase of each hop signal is realized.

[0085] The dth carrier initial phase estimation value of the mth hop is shown in formula (3). d-1 is expressed as a 6-bit binary number, for example, d-1 = 30 is expressed as binary number 011110, so as to establish the mapping relationship between φ m,d and the 6-bit binary number. The uth hop interval estimation value of the mth hop is shown in formula (4). u-1 is expressed as a 2-bit binary number, for example, u-1 = 2 is expressed as binary number 10, so as to establish the mapping relationship between and the 2-bit binary number.

[0086] Step four: the number of candidate groups N c = 1300 in this embodiment. According to the preset initial probability of binary quantization bits, 1300 groups of binary codes are generated, which are called candidate groups. The M hop carrier phase and hop interval estimation value are mapped to 1300 groups of initial solutions by N c groups of binary codes. The parameter solution is regenerated by updating the probability of binary quantization bits in the cross-entropy iteration process.

[0087] In this embodiment, the binary quantization bits of the carrier phase and hop interval estimation value of 8 hops are 64 bits. The probability of each bit is Randomly generate binary quantized bits The carrier phase quantization bits of the m-th hop are According to formula (5) The mapping yields a set of carrier phase estimates for the m-th hop, and the set of hop interval quantization bits for the m-th hop is... According to formula (6) The mapping yields a set of estimated hop intervals for the m-th hop. Therefore, from... The mapping yields the estimated values ​​of the M-hop carrier phase and hop interval.

[0088] Step 5: Generate a local pulse template based on the hop interval estimate obtained in Step 4, perform correlation calculation with the received signal, that is, match the local template with the time slot position of the received signal, compensate the signal carrier phase of the selected time slot according to the carrier phase estimate, and coherently combine the compensated multi-hop signals.

[0089] Step Six: Estimate the signal-to-noise ratio (SNR) of the merged signal obtained in Step Five. Calculate the difference between the estimated actual SNR and the theoretical SNR of the 8-hop merged signal. Obtain the merged SNR loss corresponding to the 1300 sets of carrier phase and hop interval parameter solutions generated in Step Four. Use the merged SNR loss as the objective function for cross-entropy iteration. According to formula (11), the theoretical SNR of the merged signal is γ1 + 10log 10 8≈γ1+9. The signal-to-noise ratio of the actual merged signal is estimated according to formula (12). The merged signal-to-noise ratio loss is calculated according to formula (13), and the merged signal-to-noise ratio loss estimation results of 1300 candidate groups are obtained.

[0090] Step 7: With the goal of minimizing the combined signal-to-noise ratio (SNR) loss, select samples from the candidate group that perform well according to the objective function to update the binary quantization bit generation probability. Sort the 1300 combined SNR losses obtained in Step 6 from smallest to largest. In this embodiment, N... e =130, select the top N with the smallest combined signal-to-noise ratio loss. e The hop interval and carrier phase quantization parameters corresponding to the group are used as the preferred group. The probability of each bit in the binary quantization bits of the preferred group being 0 or 1 is calculated. Based on the probability obtained from the statistics of the preferred group, the binary code generation probability of the next cross-entropy iteration is obtained.

[0091] Sort the 1300 combined signal-to-noise ratio loss estimates obtained from the i-th cross-entropy iteration in ascending order to obtain: The corresponding hop interval and carrier phase binary quantization bits are denoted as: Depend on The estimated value obtained by mapping is denoted as l∈{1, 2, ..., N}c The first N e The preferred group is composed of the first N The corresponding hop time interval and carrier phase estimation value is The probability p of each bit of the preferred group being quantized to 1 is calculated according to formula (17) i+1 The generation probability of the next iteration is updated according to formula (18) In this embodiment, a = 1, i.e.

[0092] Step eight: Avoiding falling into a local optimal solution by setting a global optimal vector group, selecting the hop time interval and carrier phase estimation value with the minimum combined SNR loss in the cross-entropy iteration process as the global optimal vector group, and when each element in the global optimal vector group becomes 0 or 1, or the number of iterations reaches the maximum number of iterations set, the cross-entropy iteration stops, and the global optimal vector group is output. The global optimal vector group obtained by random search is used for time slot selection and carrier phase compensation of the received signal, and the SNR is improved through multi-hop coherent combination.

[0093] The global optimal vector group is set to record the minimum combined SNR loss from the first iteration to the i-th iteration, and the global optimal vector group is set to record the corresponding hop time interval and carrier phase estimation value When the minimum value of the N c combined SNR losses obtained in the i-th iteration in step five satisfies , the value of is updated to , and the value of is updated to Otherwise, and remain unchanged. The number M of elements in that are 0 or 1 is calculated, and when M < 64 and the current number of iterations is less than the maximum number of iterations set, the global optimal vector group is taken as the input condition for the next cross-entropy iteration, and steps four to six are repeated. When M = 64 or the current number of iterations is equal to the maximum number of iterations set, the cross-entropy iteration stops, and the global optimal vector group

[0094] As shown in Figure 4 , when N c = 1300, the ratio of the number of preferred groups to the number of candidate groups N e / N c is changed, the iteration convergence curve is simulated, and the relationship between the average combined SNR loss and the number of iterations is obtained. When N e / N cWhen N = 0.01, convergence is achieved in 10 iterations, with a combined signal-to-noise ratio loss of approximately 1 dB. e / N c When N = 0.15, the combined signal-to-noise ratio loss decreases to approximately 0.4 dB, and convergence occurs after approximately 25 iterations. e / N c When N is small, the convergence speed is faster, but it is prone to converging to a local optimum. As N increases... e / N c Increasing the value makes it easier to obtain the global optimal solution, but it increases the number of iterations and the complexity.

[0095] Figure 5 This paper compares the bit error rate (BER) of the cross-entropy iterative auxiliary method with that of the ergodic method and the theoretical merging method. The ergodic method performs a point-by-point search on a two-dimensional grid of hop intervals and carrier phase. Ideally, this method can accurately estimate discrete hop intervals, but the performance loss comes from the increased grid search step size of the carrier phase. The cross-entropy iterative auxiliary method uses an adaptive key sampling strategy to randomly search on the two-dimensional grid of hop intervals and carrier phase, reducing the number of grid searches. In this embodiment, the ergodic method uses 2^32 grids in the hop interval dimension. 16 The number of grids in the carrier phase dimension is 2. 24 The number of searches using the traversal method is 2. 16 ×2 24 =2 40 In the cross-entropy iterative auxiliary method, N c =1300 and N e / N c When the cross-entropy ratio is 0.1, convergence occurs after approximately 20 iterations. Therefore, the number of cross-entropy iterations is approximately 1300 × 20 = 26000, significantly reduced compared to the ergodic method. The received signal carrier phase is set in the middle of the grid, and in a single hop E... b The simulated demodulation bit error rate under the condition that / N0 ranges from -6dB to 1dB, such as Figure 5 As shown, the cross-entropy iterative auxiliary method and the traversal method have basically the same bit error rate (@BER=1×10). -4 The performance loss compared to the theoretical merging is approximately 0.5 dB (@BER=1×10). -4 Compared to the traversal method, the cross-entropy iterative auxiliary method reduces the parameter estimation complexity.

[0096] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for differential time hopping estimation and multi-hop coherent combining for satellite communications, characterized by: The method comprises the following steps of: Step one: the information symbol to be sent is modulated by BPSK and then repeatedly transmitted in time discontinuous multiple time slots, each time slot is called a hop, the time interval between adjacent two hops is randomly changed within a predetermined range, and a BPSK modulated hop time interval random signal is generated; Step two: the BPSK modulated hop time interval signal obtained in step one is sent into an AWGN channel, and a signal transmitted through the AWGN channel is received; Step three: the carrier initial phase search range of each hop signal is [0, 2π), and the search range of the interval time of adjacent two-hop signals is {1, 2,..., L δ，max} agreed by the two communication parties, the search parameters of the carrier initial phase and the hop time interval of the mth hop are Wherein The search value is mapped with the binary code, D1 and D2 are the binary quantization bit numbers, each carrier phase search value corresponds to a D1 bit binary number, and each hop time interval search value corresponds to a D2 bit binary number, that is, the parameter quantization of the hop time interval and the carrier phase of each hop signal is realized. Step four: generate N c group binary codes, called candidate groups, from N c group binary codes to M-hop carrier phase and hop time interval estimates c group initial solution, the parameter solution is regenerated by updating the binary quantization bit generation probability in the cross-entropy iteration process; Step five: a local pulse template is generated according to the hop time interval estimation value obtained in step four, and a correlation operation is performed on the local pulse template and the received signal, that is, the local pulse template is matched with the time slot position of the received signal, the signal carrier phase of the selected time slot is compensated according to the carrier phase estimation value, and the multiple hop signals after compensation are coherently combined; Step six: SNR estimation is performed on the combined signal obtained in step five, the difference between the estimated actual SNR and the theoretical combined SNR of M hops is calculated to obtain the combined SNR loss corresponding to the group carrier phase and hop time interval parameter solution generated in step four, and the combined SNR loss is taken as the objective function of the cross-entropy iteration. c Step six: SNR estimation is performed on the combined signal obtained in step five, the difference between the estimated actual SNR and the theoretical combined SNR of M hops is calculated to obtain the combined SNR loss corresponding to the group carrier phase and hop time interval parameter solution generated in step four, and the combined SNR loss is taken as the objective function of the cross-entropy iteration. Step seven: in order to minimize the loss of the combined signal-to-noise ratio, a sample with better target function effect is selected from the candidate group, and is used to update the generation probability of the binary quantization bit; N c The N e groups corresponding to the smallest merging signal-to-noise ratio loss are selected as the preferred groups, and the hop time interval and carrier phase quantization parameters of the preferred groups are selected as the preferred parameters. The probability of each bit being 0 or 1 in the binary quantization bits of the preferred groups is calculated, and the probability obtained by the preferred groups is used to obtain the binary code generation probability of the next cross-entropy iteration. Step eight: the global optimal vector group is set to avoid falling into a local optimal solution, the hop time interval and the carrier phase estimation value with the minimum loss of the combined signal-to-noise ratio in the cross entropy iteration process are selected as the global optimal vector group, the cross entropy iteration is stopped when each element in the generation probability becomes 0 or 1 or the iteration number reaches the maximum iteration number, and the global optimal vector group is output; The global optimal vector group obtained through the random search is used for time slot selection and carrier phase compensation of the received signal, and the signal-to-noise ratio is improved through multiple hop coherent combination.

2. The satellite communication oriented differential time hopping estimation and multi-hop coherent combining method of claim 1, wherein: In step one, The BPSK modulated hop time interval signal is as follows: The signal is repeatedly transmitted over M hops, with K symbols transmitted in each hop. k b represents the k-th BPSK modulation symbol. k ∈{-1,+1}, w(t) is a single pulse waveform, T s For the duration of a single symbol, L m T is the number of time slots before the m-th hop. p The duration of a single time slot, f0 is the carrier frequency of the transmitted signal, and φ 0,m This is the initial phase of the carrier wave for the m-th hop of the transmitted signal.

3. The satellite communication oriented differential time hopping estimation and multi-hop coherent combining method of claim 2, wherein: In step two, The received signal after the AWGN channel is represented as where A is the amplitude of the transmitted signal when it reaches the receiver, τ is the time delay of the receiver relative to the transmitter, f d is the frequency offset of the received signal relative to the transmitted signal, φ m is the initial phase of the carrier of the mth hop received signal, and n(t) represents additive complex Gaussian white noise with mean 0 and variance 4. The satellite communication oriented differential time hopping estimation and multi-hop coherent combining method of claim 3, wherein: In step three, φ m,d denotes the initial phase estimate of the dth carrier of the mth hop, d e {1, 2,..., N1}, and Let d-1 be represented as a D1bit binary number, φ is established by the above formula m,d Mapping relationship with D1bit binary number; L δ,m,u denotes the u-th hop time interval estimate of the m-th hop, u e {1, 2,..., N2}, and N2= L δ,max , has L δ,m,u = u (4) Let u-1 be represented as a D2bit binary number, then the above equation becomes L δ,m,u The mapping relationship with the D2bit binary number.

5. The satellite communication oriented differential time hopping estimation and multi-hop coherent combining method of claim 4, wherein: In step four, The probability of generating a binary quantization bit for the i-th cross-entropy iteration is with probability Randomly generating a binary quantization bit representing generating with probability 1; a set of carrier phase quantization bits for the m-th hop is wherein representing the j-th carrier phase quantization bit of the m-th hop for the i-th iteration A set of hop time separation quantization bits for the m-th hop wherein representing the j-th carrier phase quantization bit of the m-th hop for the i-th iteration From The carrier phase estimate is obtained by mapping From The mapping results in a hop interval estimate of From equations (5) (6) we have The M-hop carrier phase and hop time interval estimates are obtained by mapping 6. The satellite communication oriented differential time hopping estimation and multi-hop coherent combining method of claim 5, wherein: The implementation method of step five is Estimation of the time gap per hop Obtaining the number of slots before the mth hop The receiving end according to Generate a local pulse template, and perform correlation calculations and phase compensation on the k-th data of all M-beats. The kth data combined signal of all M hops is further represented as Wherein, P≤M, P is the number of time slot positions of the useful signal in the M hop data matched with the local pulse template.

7. The satellite communication oriented differential time hopping estimation and multi-hop coherent combining method of claim 6, wherein: The implementation method of step six is If the time interval estimation is accurate, i.e. then so that P = M in equation (9), and if the carrier phase estimation is accurate, i.e. then the combined signal of the kth data of all M hops is Let γ1 represent the single hop signal-to-noise ratio, and the theoretical signal-to-noise ratio of the combined signal is γ comb = γ1+ 10 log 10 (M) (11) The signal-to-noise ratio of the actual combined signal is estimated by the method shown in formula (12) Wherein, mean() represents taking the mean value, and var() represents taking the variance, and the loss of the combined signal-to-noise ratio is According to the formula (11) (12) (13), N c group parameters obtain N c combined SNR loss estimation results, and the combined SNR loss is taken as the target function of cross-entropy iteration.

8. The satellite communication oriented differential time hopping estimation and multi-hop coherent combining method of claim 7, wherein: The implementation method of step seven is The multi-dimensional parameter estimation problem of the carrier phase and the hop time interval is converted into selecting the optimal carrier phase and hop time interval quantization bit b so that the loss of the combined signal-to-noise ratio is minimized, that is wherein, is a set of values for the M carrier phase and hop time interval quantization parameters; In order to obtain the carrier phase and hop time interval quantization parameters that minimize the loss of the combined signal-to-noise ratio, the generation probability of the binary quantization bit is iterated, so that the probability distribution of the to-be-estimated parameters is close to the optimal probability distribution, and the probability iteration rule is where I{γ loss ≤ T H} is a binary indicator function, T H is a threshold The probability of the binary quantization bits is iteratively updated according to the probability iteration rule shown in formula (15), and the N c Among the group binary quantization bits, the N e group binary quantization bits that minimize the merging SNR loss are selected, and the threshold T H in formula (15) is related to N c and N e ; the sorting result of the merging SNR loss obtained by the i th cross-entropy iteration is denoted as The corresponding hop time interval and carrier phase binary quantization bits are denoted as The estimated value obtained by mapping is denoted as The N group binary quantization bits with the minimum merging SNR loss form an optimal group e The corresponding hop time interval and carrier phase estimated values are The probability p of each bit quantization bit being 1 in the optimal group is calculated i+1 ​ p i+1 Smoothed, update the generating probabilities for the next iteration Wherein, α is a smoothing coefficient, and α∈(0,1], the larger α is, the faster the convergence speed is.

9. The satellite communication oriented differential time hopping estimation and multi-hop coherent combining method of claim 8, wherein: The implementation method of step eight is Setting For recording the minimum combined SNR loss from the first iteration to the i-th iteration, set As the input condition for recording The corresponding hop time interval and carrier phase estimation value, when the minimum value of the N c combined SNR loss obtained in the i-th iteration in step five satisfies , update the value of to and update the value of to Otherwise , remain unchanged; calculate the number N of elements in 0 or 1, when N < M(D1+D2) and the current iteration number is less than the set maximum iteration number, set as the input condition for the next cross-entropy iteration, repeat steps four to eight; when N = M(D1+D2) or the current iteration number is equal to the set maximum iteration number, the cross-entropy iteration stops, and the global optimal vector group is output.

10. A satellite communication oriented differential time hopping estimation and multi-hop coherent combining apparatus for implementing the satellite communication oriented differential time hopping estimation and multi-hop coherent combining as claimed in claim 1, 2, 3, 4, 5, 6 or 7, characterized by: The method comprises a parameter quantization unit, a parameter compensation and multiple hop combination unit, a signal-to-noise ratio estimation unit and a cross entropy iteration unit. The parameter quantization unit is used to establish a mapping relationship between the hop time interval and the carrier phase search value and the binary quantization bit. The parameter compensation and multi-hop combining unit performs parameter compensation and multi-hop combining on the estimation result obtained by the cross entropy iteration unit, performs correlation operation on the local pulse template generated according to the hop time interval and the received signal, selects the time slot where the signal is located, compensates the carrier phase of the selected time slot signal according to the carrier phase estimation value, and performs coherent combining on the compensated multi-hop signal; The signal-to-noise ratio estimation unit performs signal-to-noise ratio estimation on the combined signal obtained by the parameter compensation and combining unit, calculates the difference between the estimated signal-to-noise ratio and the theoretical signal-to-noise ratio, and obtains the combined signal-to-noise ratio loss; The cross entropy iteration unit generates a binary code according to the probability of binary quantization bit generation, generates the hop time interval and the carrier phase estimation value according to the mapping relationship obtained by the parameter quantization unit, outputs the estimation value to the parameter compensation and multi-hop combining unit, takes the combined signal-to-noise ratio loss calculated by the signal-to-noise ratio estimation unit as the objective function, selects the sample with smaller combined signal-to-noise ratio loss as the optimal group, regenerates the probability of binary quantization bit generation according to the optimal group for the next iteration, records the hop time interval and the carrier phase estimation value corresponding to the minimum combined signal-to-noise ratio loss as the global optimal vector group during the iteration process, and outputs the global optimal vector group after the iteration is stopped.

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