BPSK+DSSS differential recursive demodulation method and device based on multi-phase weighted signal synthesis

Abstract

The present invention discloses a BPSK+DSSS differential recursive demodulation method and device based on multi-phase weighted signal synthesis, the method comprising the following steps: S1: performing pre-processing including low-noise amplification, mixing and filtering on a radio frequency signal to obtain a filtered intermediate frequency signal; S2: sampling the filtered intermediate frequency signal obtained in step S1 to obtain an ADC sampling sequence; S3: converting the sampling sequence s obtained in step S2 into an ADC sampling sequence; n The DC bias component in the #imgabs0# sequence is removed and the #imgabs0# sequence is calculated. S4: Based on the #imgabs1# sequence obtained in step S3, cumulative smoothing despreading is performed to calculate the #imgabs2# sequence. S5: The #imgabs3# sequence is decimate-decoded to obtain the #imgabs4# sequence. S6: Based on the #imgabs5# sequence obtained in step S5, V is calculated to represent the equivalent signal amplitude of the corresponding #imgabs6# sequence. K sequence; S7: perform energy evaluation normalization; S8: perform cross-correlation calculation to find the best sampling decision point; S9: calculate the phase difference cosine value; S10: perform recursive decision.

Description

Technical Field

[0001] The present invention relates to the technical field of radio frequency signal processing, and in particular to a BPSK+DSSS differential recursive demodulation method and device based on multi-phase weighted signal synthesis. Background Art

[0002] BPSK, or binary phase-shift keying modulation, uses phase reversal to carry binary digital signals. When combined with direct sequence spread spectrum technology, or DSSS, it demonstrates its unique technical advantages. It is simple and practical, has high anti-interference and confidentiality capabilities, and has been applied in many systems.

[0003] Generally, there are two main methods for BPSK demodulation: coherent demodulation and incoherent demodulation. In the former, coherent demodulation is achieved through a carrier recovery circuit based on the spectral characteristics of the BPSK signal. In the latter, a carrier recovery circuit is not required, and demodulation is performed directly by utilizing the time domain characteristics of the BPSK signal. Examples include demodulation based on time measurement and demodulation based on phase comparison.

[0004] For discontinuous, bursty communications, the short signal duration does not allow the phase-locked loop (PLL) circuit to converge and track, making carrier recovery and tracking impossible. Consequently, coherent demodulation is impossible. In this case, non-coherent demodulation becomes the only option.

[0005] Figure 1 A traditional BPSK+DSSS signal demodulation scheme is shown. However, this demodulation scheme requires phase detection and despreading operations in the high-speed digital domain, so it has high computing power requirements and is not suitable for FPGAs. This leads to high cost, high power consumption, and poor integration. It is not suitable for portable or handheld devices, which limits its scope of use. Summary of the Invention

[0006] In response to the above technical problems, the present invention starts from the BPSK+DSSS signal expression, derives the BPSK+DSSS differential recursive demodulation method, and analyzes the problems existing in BPSK+DSSS differential recursive demodulation. On this basis, a corresponding solution is given, namely the BPSK+DSSS differential recursive demodulation method based on multi-phase weighted signal synthesis. This method has low computing power requirements, is insensitive to carrier frequency deviation, has high receiving sensitivity, and can effectively eliminate the error propagation phenomenon caused by recursive demodulation.

[0007] In order to achieve the above object, the technical solution of the present invention provides a BPSK+DSSS differential recursive demodulation method based on multi-phase weighted signal synthesis, which includes the following steps:

[0008] S1: Preprocess the RF signal including low-noise amplification, mixing and filtering to obtain a filtered intermediate frequency signal;

[0009] S2: Sampling the filtered intermediate frequency signal obtained in step S1 to obtain the following ADC sampling sequence:

[0010] s n =A n cos(ω c nT s )+ξ n (a)

[0011] Among them, A n represents the signal amplitude, ω c Indicates the RF or IF carrier phase, T s is the ADC sampling period, f s is the ADC sampling frequency, ξ n represents the noise component;

[0012] S3: The sampling sequence s obtained in step S2 n The DC bias component in is removed, and formula (b) is used to obtain sequence:

[0013]

[0014] Where M is the number of samples corresponding to each baseband symbol before spreading, and K is the baseband symbol spacing before spreading;

[0015] S4: Based on the results obtained in step S3 Sequence, using formula (c) for cumulative smooth despreading, calculate to obtain sequence:

[0016]

[0017] S5: Use formula (d) to The sequence is decelerated and extracted to obtain sequence:

[0018]

[0019] Among them, X is the speed reduction multiple, is an integer, m is the length of the spreading sequence, and k is the number of samples of each symbol after spreading;

[0020] S6: Based on the result obtained in step S5 Sequence, use formula (e) to calculate V K sequence:

[0021]

[0022] V K The sequence represents the corresponding The equivalent signal amplitude of the sequence, where V K The larger the value, the The larger the equivalent amplitude of the sequence signal, the higher the signal-to-noise ratio, where len represents the number of data samples used for signal amplitude evaluation;

[0023] S7: Perform energy evaluation normalization by K In the sequence, find the maximum value V MAX =V P , and then according to formula (f) Normalize the sequence:

[0024]

[0025] S8: After normalization sequence, performing cross-correlation calculation using formula (g) according to the P value determined in step S7;

[0026]

[0027] in, Indicates from The sequence is extracted by extracting one data every λ data, L is the original frame synchronization sequence SYN 0 The length of SYN P The original frame synchronization sequence SYN 0 The new sequence is obtained by multiplying every two elements with an interval of P. LP is the new sequence SYN P length,

[0028] In the cross-correlation sequence R n Find the maximum value R max , and use this R max The position j is the reference point, and according to formula (h) The sequence is extracted to obtain the sequence

[0029]

[0030] S9: After obtaining After the sequence, the phase difference cosine value corresponding to different K values ​​is calculated using formula (i):

[0031]

[0032] Among them, I l Corresponding to the original frame synchronization sequence SYN 0 Synl , I l+K Corresponding to the original frame synchronization sequence SYN 0 Syn l+K ,i l Obtained by formula (h);

[0033] S10: Use formula (j) to make a recursive decision:

[0034]

[0035] Among them, I n+1 is the symbol to be judged, I n+1-K For the previously determined symbol, L is the original frame synchronization sequence SYN 0 The sequence length, The equation (h) is used to obtain the symbols of the entire transmission sequence by performing recursive decision starting from the last few bits (eg, the sixth bit from the end) of the frame header frame synchronization sequence.

[0036] Furthermore, in step S1 , a ceramic filter or an LC filter is used for filtering.

[0037] Furthermore, in step S2, the ADC sampling frequency f is calculated based on the baseband rate after spread spectrum and the number of samples corresponding to each symbol. s .

[0038] Furthermore, in step S2, the ADC sampling frequency f s =Mf b =mkf b , where f b is the baseband symbol rate before spreading, m is the spreading sequence length, k is the number of samples of each symbol after spreading, and M is the number of samples corresponding to each baseband symbol before spreading.

[0039] Furthermore, in step S5, the value range of λ is: λ≥5.

[0040] The technical solution of the present invention also provides a BPSK+DSSS differential recursive demodulation device based on multi-phase weighted signal synthesis, which includes the following modules:

[0041] The signal preprocessing module is used to perform preprocessing on the radio frequency signal, including low-noise amplification, mixing and filtering, to obtain a filtered intermediate frequency signal;

[0042] The ADC sampling module is used to sample the filtered intermediate frequency signal obtained by the signal preprocessing module to obtain the following ADC sampling sequence:

[0043] s n =A n cos(ωc nT s )+ξ n (a)

[0044] Among them, A n represents the signal amplitude, ω c Indicates the RF or IF carrier phase, T s is the ADC sampling period, f s is the ADC sampling frequency, ξ n represents the noise component;

[0045] The signal sequence calculation module is used to calculate and obtain the signal sequence by executing the following steps S11-S14 respectively. sequence, sequence, Sequence and V K sequence:

[0046] S11: The sampling sequence s obtained by the ADC sampling module n The DC bias component in is removed, and formula (b) is used to obtain sequence:

[0047]

[0048] Where M is the number of samples corresponding to each baseband symbol before spreading, and K is the baseband symbol spacing before spreading;

[0049] S12: Based on the data obtained in step S11 Sequence, using formula (c) for cumulative smooth despreading, calculate to obtain sequence:

[0050]

[0051] S13: Use formula (d) to Sequence speed reduction extraction, obtain sequence:

[0052]

[0053] Among them, X is the speed reduction multiple, is an integer, m is the length of the spreading sequence, and k is the number of samples of each symbol after spreading;

[0054] S14: Based on the data obtained in step S5 Sequence, use formula (e) to calculate V K sequence:

[0055]

[0056] VK The sequence represents the corresponding The equivalent signal amplitude of the sequence, where V K The larger the value, the The larger the equivalent amplitude of the sequence signal, the higher the signal-to-noise ratio, where len represents the number of data samples used for signal amplitude evaluation;

[0057] Energy evaluation normalization module is used to calculate the energy consumption of K In the sequence, find the maximum value V MAX =V P , and then according to formula (f) Normalize the sequence:

[0058]

[0059] Cross-correlation calculation module, used to calculate the normalized Sequence, according to the P value determined by the energy evaluation normalization module, the cross-correlation calculation is performed using formula (g);

[0060]

[0061] in, Indicates from The sequence is extracted by extracting one data every λ data, L is the original frame synchronization sequence SYN 0 The length of SYN P The original frame synchronization sequence SYN 0 The new sequence is obtained by multiplying every two elements with an interval of P. LP is the new sequence SYN P length,

[0062] In the cross-correlation sequence R n Find the maximum value R max , and use this R max The position j is the reference point, and according to formula (h) The sequence is extracted to obtain the sequence

[0063]

[0064] Phase difference cosine value calculation module is used to obtain After the sequence, the phase difference cosine value corresponding to different K values ​​is calculated using formula (i):

[0065]

[0066] Among them, I l Corresponding to the original frame synchronization sequence SYN 0 Synl , I l+K Corresponding to the original frame synchronization sequence SYN 0 Syn l+K ,i l Obtained by formula (h);

[0067] The multi-channel signal synthesis decision module is used to make recursive decisions using formula (j):

[0068]

[0069] Among them, I n+1 is the symbol to be judged, I n+1-K For the previously determined symbol, L is the original frame synchronization sequence SYN 0 The sequence length, The equation (h) is used to obtain the symbols of the entire transmission sequence by performing recursive decision starting from the last few bits (eg, the sixth bit from the end) of the frame header frame synchronization sequence.

[0070] Furthermore, in the signal preprocessing module, a ceramic filter or an LC filter is used for filtering.

[0071] Furthermore, in the ADC sampling module, the ADC sampling frequency f is calculated based on the baseband rate after spread spectrum and the number of samples corresponding to each symbol. s .

[0072] Furthermore, in the ADC sampling module, the ADC sampling frequency f s =Mf b =mkf b , where f b is the baseband symbol rate before spreading, m is the spreading sequence length, k is the number of samples of each symbol after spreading, and M is the number of samples corresponding to each baseband symbol before spreading.

[0073] Furthermore, when the signal sequence calculation module executes step S13, the value range of λ is: λ≥5. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0075] Figure 1 This is a flowchart of the traditional BPSK+DSSS signal demodulation solution;

[0076] Figure 2 It is a schematic diagram of the burst data frame structure;

[0077] Figure 3 This is a block diagram of the BPSK+DSSS multipath signal synthesis differential demodulation of the present invention. DETAILED DESCRIPTION

[0078] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 any creative efforts shall fall within the scope of protection of the present invention.

[0079] like Figure 2 As shown in the figure, for burst data communication, the frame structure generally consists of two parts: the SYN part and the DATA part. The SYN part is used for frame synchronization and marks the beginning of the frame, while the DATA part is used to carry user information. To combat data errors, the DATA part generally also includes CRC bytes for verifying data integrity and FEC encoding information for error correction.

[0080] There are usually two ways to use the SYN field. One is to use a pseudo-random sequence with sufficient length and good autocorrelation. For example, in DMR or PDT, the length of the SYN field in a voice frame is 24 symbols, and the corresponding sequence is:

[0081] +3-3-3-3+3-3-3+3+3+3-3+3+3+3-3+3+3+3-3+3+3-3-3+3-3-3+3+3

[0082] At the receiving end, correlation calculation is used to search for the frame header and define the beginning of the frame.

[0083] Another type of SYN uses a fixed-length sequence of alternating 0s and 1s followed by a one-byte or multi-byte signature to mark the beginning of a frame. For example, in the Automatic Identification System (AIS), the SYN field consists of a 24-bit alternating 0-1 sequence followed by a one-byte signature (0x7E). Similarly, at the receiving end, correlation calculations are performed to find SYNs and thus delimit frames.

[0084] In modern communications, the former method is more commonly used, that is, the SYN field is represented by a pseudo-random sequence with good autocorrelation.

[0085] In either case, the SYN field is known in advance. Once a correlation peak or signature is found, it can be considered to be the SYN field and can be filled with the corresponding information without the need for calculation.

[0086] For convenience, we define SYN as follows:

[0087] SYN 0 =[syn1,syn2,syn3,syn4,syn5,syn6,syn7,syn8......syn L ](1)

[0088] Among them, syn n = ±1, its amplitude value is normalized to 1, SYN 0 The sequence length is L.

[0089] According to SYN 0 We can calculate a new set of sequences as follows:

[0090] SYN 1 =[syn1×syn2,syn2×syn3,syn3×syn4......,syn L-1 ×syn L ](2)

[0091] SYN 2 =[syn1×syn3,syn2×syn4,syn3×syn5......,syn L-2 ×syn L ](3)

[0092] That is, the new sequence is obtained by multiplying every two elements in the original sequence with an interval of K. That is, the elements of the new sequence are defined as follows:

[0093] SYN K =[syn1×syn 1+K ,syn2×syn 2+K ,syn3×syn 3+K ...,syn L-K ×syn L ] (4)

[0094] The length of the corresponding new sequence is LK, where K = 1, 2, 3, 4, ... .

[0095] We define the DSSS spreading sequence as:

[0096] c=[c0,c1,c2,......,c m-2 ,c m-1 ](5)

[0097] The length of the spreading sequence is m.

[0098] Expand it k times, that is, each spread spectrum sequence element is simply repeated k-1 times to obtain an expanded spread spectrum sequence, that is,

[0099] C=[C0,C1,C2,......,C M-2 ,C M-1 ] (6)

[0100] in,

[0101] C J =c j =c J / k 0≤j<m 0≤J<M=k×m(7)

[0102] “ / ” is the integer division operator.

[0103] The length of the extended spreading sequence is:

[0104] M=k×m(8)

[0105] For the baseband signal after spread spectrum, each symbol is sampled k times, which is equivalent to sampling each symbol M times for the baseband signal before spread spectrum, where k, m and M are all positive integers.

[0106] For the BPSK+DSSS modulation system, before BPSK modulation, spread spectrum processing is required in the baseband digital domain, that is,

[0107] A n =A NM+J =I N C J N=n / M, J=n%M(9)

[0108] " / " is the integer division operator, and "%" is the remainder operator. M represents the number of samples corresponding to a baseband symbol before spreading, that is, the system sampling frequency is M = k × m times the baseband symbol rate. N Indicates the Nth baseband symbol value before spreading, either +1 or -1.

[0109] Obviously, A n That is A NM+J Represents the J-th sampling value corresponding to the N-th baseband symbol before spreading.

[0110] For BPSK signals, it is generally expressed as follows:

[0111] s(t)=A(t)cos(ω c t+φ0)+ξ(t) (10)

[0112] Where A(t) represents the signal amplitude at time t (either +1 or -1), ω c represents the RF or IF carrier phase, and φ0 represents the initial carrier phase. Since the initial phase does not affect the subsequent analysis, we will ignore this item in the following analysis process.

[0113] In the discrete domain, the above formula can be expressed as:

[0114] s n =A n cos(ω c nT s )+ξ n (11)

[0115] Substituting (9) into equation (9) yields:

[0116] s n =s NM+J =A NM+J cos(ω c nT s )+ξ n =I N C J cos(ω c nT s )+ξ n (12)

[0117] Right now:

[0118] s n =I N C J cos(ω c nT s )+ξ n n=NM+J(13)

[0119] Likewise, we get:

[0120] s n+KM =I N+K C J cos(ω c nT s +ω c KMT s )+ξ n+KM n=NM+J(14)

[0121] We define:

[0122]

[0123] According to equations (13), (14) and (15), we can obtain:

[0124]

[0125] Expand the above formula to get three parts, as follows:

[0126]

[0127] PART 3: I N C J cos(ω c nT s )ξ n+KM +I N+K C J cos(ω c nT s +ω c KMT)ξ n (19)

[0128] Because C J C J ≡1, so C J C J Item can be ignored.

[0129] Among them, PART 2 and PART 3 are modulated by high-frequency signals, which can be filtered out or weakened by low-pass filtering or accumulation. Therefore, we do not consider the latter two items, thus obtaining:

[0130]

[0131] Obviously, when NM≤n<(N+1)M, I N I N+K cos(ω c KMT s ) is a constant value and does not change with n, so the effective signal can be enhanced by accumulation, and ξ n ξ n+KM are uncorrelated or weakly correlated random variables, which are weakened by accumulating and canceling each other out. Therefore, we define:

[0132]

[0133] Obviously, when n=NM, there will be extreme values ​​(maximum or minimum), as shown in the following formula:

[0134]

[0135] That is, through the sliding window accumulation, the useful signal is enhanced M times, while the noise component will not be enhanced through the accumulation operation due to its irrelevant or weak correlation.

[0136] According to statistical theory, when M→∞, the noise component approaches 0.

[0137] From formula (22), we can see that in order to obtain In order to obtain a larger statistical gain, the value of M is usually large, and multiple calculations need to be repeated. For this reason, we make appropriate transformations on the above formula to obtain

[0138]

[0139] Right now:

[0140]

[0141] By transformation, we get a Recursive calculation method, using this recursive formula, calculate a The value only requires one addition and one subtraction, that is, each value only requires two calculations, regardless of the value of M. Obviously, the use of recursive formulas can greatly reduce the amount of calculations, which is very important for low-computing systems such as MCUs.

[0142] As mentioned above, when M is large, can be ignored, so we get:

[0143]

[0144] In order to avoid calculations in the high-speed domain, we need to reduce the speed in time. To this end, we need to perform downsampling operations. To facilitate subsequent processing, the downsampling rate is generally defined as 3 to 15 times the baseband rate before spread spectrum. In theory, the higher the sampling rate, the higher the time domain resolution, which is more conducive to achieving optimal sampling judgment. When the MCU computing power and storage space allow, a higher downsampling rate should be used as much as possible.

[0145] For this we define:

[0146]

[0147] That is, every X data is extracted once, where is an integer, m is the degree of the spreading sequence, k is the number of samples of each symbol after spreading, and M corresponds to the number of samples corresponding to a baseband symbol before spreading.

[0148] When the downsampling rate is high enough, that is, the time resolution is large enough, the basic shape and amplitude value of the waveform remain unchanged.

[0149] From (25) we can get Learned that The size of depends on two factors, namely the M value and cos(ω c KMT s ), where M is determined by the system parameters and is a fixed value, and cos(ω cKMT s ) changes due to the drift of the carrier frequency and the local oscillator frequency and the change of the K value.

[0150] For a certain K value, cos(ω c KMT s ) is a semi-static random variable, that is, it can be seen as a constant in a short period of time, but this constant changes slowly over time and changes randomly between -1 and +1. Obviously, when cos(ω c KMT s )=0, When there is only noise information but no signal component information, when cos(ω c KMT s )=±1, the information component is the largest, which is most conducive to subsequent signal processing. In order to perform subsequent signal processing, we must first evaluate the signal amplitude. For this purpose, we define the signal equivalent amplitude value:

[0151]

[0152] Pick up Find the RMS value of a sufficiently long sequence and estimate its equivalent signal amplitude.

[0153] This results in a set of data, namely Compare their sizes and confirm the largest group.

[0154] That is:

[0155] V max =max(V 1 V 2 V 3 V 4 V 5 V 6 ......)(28)

[0156] And take its maximum value for all The signal is normalized, that is, all data are divided by V max Maximum value:

[0157]

[0158] Assume V P =V max Maximum (P is a certain value, or 1 or 2 or other values), which means the corresponding The signal component in the sequence is the largest and the noise component is the smallest. The sequence contains the product information of two adjacent baseband signals with a spacing of K. In order to obtain the best sampling decision point, we use SYN K(See formula (2)) and perform cross-correlation calculation to find the location of the correlation peak. If the correlation peak is greater than a certain set threshold, the signal can be considered valid, and the location corresponding to the correlation peak is the best sampling decision point for the sequence.

[0159] The cross-correlation calculation is as follows:

[0160]

[0161] Among them, L is the length of the original synchronization sequence SYN of the frame, LP is SYN P Sequence length.

[0162] Traverse R n Sequence, if the corresponding point j has the largest cross-correlation value, R j If the maximum value exceeds a certain threshold, it is considered that a data frame has been received, and this is used as a reference point. Perform λ-fold speed reduction to obtain a new set of sequences.

[0163]

[0164] Where K = 1, 2, 3, 4, 5, 6, 7, 8, and j is the position offset value corresponding to the correlation peak.

[0165] When the sampling density is sufficient,

[0166]

[0167] That is:

[0168]

[0169] In this new sequence, each sample value corresponds to the product of two adjacent baseband symbols with a distance of K and the cosine value of their phase difference.

[0170] Transforming formula (33) yields:

[0171]

[0172] That is:

[0173]

[0174] For the frame header, i.e. the SYN part, I n , I n+K are all known, among which is obtained by calculation, and for I n , I n+K Part, there are:

[0175] I n=syn n 0≤n<L(36)

[0176] I n+K =syn n+K K≤n+K<L(37)

[0177] Therefore, the corresponding cos(ω c KMT s ), in order to improve cos(ω c KMT s ) accuracy and reduce the influence of accidental errors, which can be processed by cumulative smoothing, that is:

[0178]

[0179] From formula (34), we can get:

[0180]

[0181] That is:

[0182]

[0183] Then we get:

[0184]

[0185] Using formula (41), it is easy to obtain:

[0186]

[0187] Using the recursive formula (42), as long as I is clear n value, Value and cos(ω c KMT s ) value, we recursively make a decision to obtain the entire baseband symbol sequence.

[0188] However, it must be noted that the use of the above recursive formula has a serious problem, namely error propagation. That is, when a judgment is wrong, it will cause subsequent judgments to be wrong. Therefore, this method of relying on one signal is only effective when the signal is good. Once a sudden error occurs, it will inevitably lead to error propagation, and ultimately make the entire data unavailable.

[0189] To this end, we must introduce multiple signals for in-phase superposition to prevent error propagation. The principle is as follows:

[0190] According to formula (41), we can easily obtain:

[0191]

[0192] For other K values, the same applies and will not be listed here.

[0193] Adding the above formula together we get:

[0194]

[0195] because

[0196]

[0197] Ignore fixed coefficients get:

[0198]

[0199] Different from equation (42), equation (51) shows that the value of the next symbol no longer depends only on the value of the previous symbol, but depends on the previous R values. In this way, by introducing multiple signals and adding them in phase (phase adjustment is achieved by multiplying cos(ω c KMT s ) achieves this goal, achieving the goal of mutually reinforcing signal components and canceling out noise components, significantly improving the signal-to-noise ratio. Furthermore, the introduction of multiple components prevents error propagation. Even if a symbol is misjudged, as long as its weight is kept below 50%, persistent misjudgment, a phenomenon known as error propagation, can be avoided.

[0200] Theoretically, the more signal branches R there are, the greater the statistical gain and the higher the signal-to-noise ratio.

[0201] The lower the bit error rate, the less error propagation there is. However, from a practical perspective, increasing R will lead to increased computational complexity and memory requirements. Therefore, from an engineering perspective, an R value of 5 or 6 is generally sufficient. When R = 6, it ensures that under any circumstances, the proportion of any component is far less than 50%. That is, for any value of θ, we have:

[0202]

[0203] It can effectively suppress the error propagation phenomenon. Therefore, considering the comprehensive computing power and performance issues, R=6 or R=5 is usually sufficient.

[0204] According to the above analysis, we get the following Figure 3 The demodulation process of the BPSK+DSSS burst communication system shown in FIG:

[0205] (1) Amplification, mixing, and filtering

[0206] To facilitate subsequent processing, the RF signal needs to be pre-processed, including signal low-noise amplification, mixing, and filtering. For example, through the above processing, the RF signal can be moved to a suitable intermediate frequency (such as 10.7MHz), and then filtered through a filter (such as a ceramic filter or LC filter) to remove unwanted out-of-band signals;

[0207] (2)ADC sampling

[0208] According to the baseband rate after spread spectrum and the number of samples corresponding to each symbol, the ADC sampling frequency is calculated, and the filtered intermediate frequency signal is sampled to obtain the ADC sampling sequence:

[0209] s n =A n cos(ω c nT s )+ξ n (11)

[0210] Where T s is the ADC sampling period, is the ADC sampling frequency f s The reciprocal of

[0211] And f s =Mf b =mkf b , where f b is the baseband symbol rate before spreading, m is the spreading sequence length, k is the number of sampling times corresponding to each symbol period after spreading, and M=mk is the number of sampling times corresponding to each baseband symbol before spreading.

[0212] (3) Calculation sequence

[0213] For ADC sampling data, after removing the DC bias component, the ADC sampling original sequence s without DC component is obtained. n , using formula (15), namely:

[0214]

[0215] Wherein, M is the number of samples corresponding to one symbol (before spreading) period, and K is the baseband symbol (before spreading) spacing. For example, calculations are performed for K=1, 2, 3, 4, 5, and 6 to obtain new sequences.

[0216] Based on the above formula (14), we can know that

[0217] s n+KM =A n+KM cos(ω c nT s +ω c KMTs )+ξ n+KM

[0218] For the cases where K = 1, 2, 3, 4, 5, and 6, 6 new sequences are calculated, namely

[0219]

[0220] (4) Accumulate smooth despreading and calculate sequence

[0221] For the above 6 groups of sequences Using formula (24),

[0222]

[0223] Calculate 6 new sequences respectively, namely

[0224] (5) Reduce the speed and extract, and obtain sequence

[0225] For the above 6 groups of sequences Using formula (26),

[0226]

[0227] Wherein, X is the decimation multiple, λ is the number of samples corresponding to each baseband symbol after decimation, and M=λX.

[0228] Up The sequence is decelerated and extracted to obtain 6 groups of signal sequences, namely,

[0229] The principle of decimation is that, within the constraints of computing power, λ should be as large as possible. A larger λ value corresponds to a higher temporal resolution, which is more conducive to subsequent decision making. Generally, λ is ≥ 5, ensuring that the number of samples corresponding to each baseband symbol period is an integer greater than or equal to 5.

[0230] (6) Calculate V K sequence

[0231] According to formula (27),

[0232]

[0233] For the above 6 groups Sequence, get a new set of sequences, namely V 1 、V 2 、V 3 、V 4 、V 5、V 6 , which represents the corresponding The equivalent signal amplitude of the sequence, V K The larger the value, the The larger the equivalent amplitude of the sequence signal, the higher the signal-to-noise ratio. Here, len represents the number of data samples used for signal amplitude evaluation. Theoretically, the larger the len, the more accurate the evaluation. Generally, len should be greater than 2M / X, which corresponds to sample data of more than two symbols.

[0234] (7) Normalization

[0235] In V K In the sequence, find the maximum value V MAX =V P , then according to formula (29), that is:

[0236]

[0237] For 6 groups Normalize the sequence.

[0238] (8) Cross-correlation calculation to find the best sampling decision point

[0239] After normalization Sequence, according to the P value determined in step (7), the cross-correlation calculation is performed using formula (30);

[0240]

[0241] in, Indicates from The sequence is extracted by extracting one data every λ data, L is the original frame synchronization sequence SYN 0 The length of SYN P The original frame synchronization sequence SYN 0 The new sequence is obtained by multiplying every two elements with an interval of P. LP is the synchronization sequence SYN P length.

[0242] In the cross-correlation sequence, find the maximum value R max， And with this R max The position j is the reference point, according to formula (31), that is:

[0243]

[0244] right The sequence is downsampled to obtain 6 new sequences, namely

[0245] (9) Calculate the cosine value of the phase difference

[0246] In obtaining After the sequence, use formula (38), that is:

[0247]

[0248] Calculate the cosine of the phase difference corresponding to different K values.

[0249] Its I l Corresponding to the frame synchronization original sequence SYN 0 The element syn in l , I l , I l+K Corresponding to the original frame synchronization sequence SYN 0 Syn l ,syn l+K , that is, I0 = syn0, I1 = syn1, and so on. l Based on step (8) sequence.

[0250] (10) Recursive judgment

[0251] Based on the above work and the relevant information obtained, formula (51) is used, namely:

[0252]

[0253] Among them I n+1 is the symbol to be judged, I n+1-K For the previously determined symbols, recursive determination can be performed starting from the last few bits of the frame header frame synchronization sequence (e.g., the sixth bit from the end) to obtain the symbols of the entire transmission sequence. Based on step (8) sequence.

[0254] In the present invention, through algorithm optimization design, phase detection and despreading operations in the high-speed digital domain are avoided. Through careful algorithm design, the product information between adjacent symbols is utilized and then accumulated, thereby completing the DSSS despreading operation that requires large computing power in disguise. Then, using downsampling technology, the data is quickly reduced from the high-speed domain to the low-speed domain (from MSps to tens of kSps), and then complex signal processing and logical judgment are performed, including signal amplitude evaluation, comparison, correlation calculation, phase difference cosine value calculation, multi-channel signal synthesis and other complex operations. The corresponding processing gain is obtained through the complexity of the algorithm, thereby improving the system receiving sensitivity.

[0255] The algorithm of the present invention has the following advantages:

[0256] (1) Avoiding direct despreading algorithm reduces computing power requirements, so that the algorithm can be implemented on a medium-performance MCU or FPGA chip without the need for a dedicated ASIC chip, FPGA chip, or high-performance DSP chip. It has the advantages of low cost, low power consumption, and small size, while reducing development difficulty and shortening development cycle.

[0257] (2) The algorithm is robust and insensitive to frequency offsets. It can be used in systems with large frequency offsets, reducing the requirements on the RF system.

[0258] (3) The algorithm improves the receiving sensitivity and increases the system stability by synthesizing multiple in-phase weighted signals. When the number of synthesis paths is greater than 5, its receiving sensitivity is better than that of the coherent receiving system. Simulation calculations show that this method has a statistical gain of about 3dB.

[0259] (4) The algorithm uses multi-channel in-phase weighted signal synthesis technology to ensure that the high signal-to-noise ratio branch signal occupies a larger component in the signal synthesis, while the low signal-to-noise ratio branch signal occupies a smaller component, thus avoiding the deterioration of the signal-to-noise ratio due to equal-weight synthesis;

[0260] (5) By synthesizing multiple branch signals, the error propagation caused by excessive weight of a branch signal is avoided, and the block or grid-like error phenomenon caused by misjudgment of a symbol is avoided, which in turn leads to FEC failure.

[0261] (6) This algorithm does not require carrier frequency recovery, is suitable for burst communication, and reduces the complexity of hardware circuits.

[0262] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A BPSK+DSSS differential recursive demodulation method based on multi-phase weighted signal synthesis, characterized in that: The steps include: S1: Preprocess the RF signal including low-noise amplification, mixing and filtering to obtain a filtered intermediate frequency signal; S2: Sampling the filtered intermediate frequency signal obtained in step S1 to obtain the following ADC sampling sequence: s n =A n cos(ω c nT s )+ξ n (a) Among them, A n represents the signal amplitude, ω c Indicates the RF or IF carrier frequency, T s is the ADC sampling period, f s is the ADC sampling frequency, ξ n represents the noise component; S3: The sampling sequence s obtained in step S2 n The DC bias component in is removed, and formula (b) is used to obtain sequence: Where M is the number of samples corresponding to each baseband symbol before spreading, and K is the baseband symbol spacing before spreading; S4: Based on the results obtained in step S3 Sequence, using formula (c) for cumulative smooth despreading, calculate to obtain sequence: S5: Use formula (d) to The sequence is decelerated and extracted to obtain sequence: Among them, X is the speed reduction multiple, is an integer, m is the length of the spreading sequence, and k is the number of samples of each symbol after spreading; S6: Based on the result obtained in step S5 Sequence, use formula (e) to calculate V K sequence: V K The sequence represents the corresponding The equivalent signal amplitude of the sequence, where V K The larger the value, the The larger the equivalent amplitude of the sequence signal, the higher the signal-to-noise ratio, where len represents the number of data samples used for signal amplitude evaluation; S7: Perform energy evaluation normalization by K In the sequence, find the maximum value V MAX =V P , and then according to formula (f) Normalize the sequence: S8: After normalization Sequence, according to the P value determined in step S7, the cross-correlation calculation is performed using formula (g); in, Indicates from The sequence is extracted by extracting one data every λ data, L is the original frame synchronization sequence SYN 0 The length of SYN P The original frame synchronization sequence SYN 0 The new sequence is obtained by multiplying every two elements with an interval of P. LP is the new sequence SYN P length, In the cross-correlation sequence R n Find the maximum value R max , and use this R max The position j is the reference point, and according to formula (h) The sequence is extracted to obtain the sequence S9: After obtaining After the sequence, the phase difference cosine value corresponding to different K values ​​is calculated using formula (i): Among them, I l Corresponding to the original frame synchronization sequence SYN 0 Syn l , I l+K Corresponding to the original frame synchronization sequence SYN 0 Syn l+K , Obtained by the above formula (h); S10: Use formula (j) to make a recursive decision: Among them, I n+1 is the symbol to be judged, I n+1-K For the previously determined symbol, L is the original frame synchronization sequence SYN 0 The sequence length, The above formula (h) is used to obtain the symbols of the entire transmission sequence by performing recursive judgment starting from the last few bits of the frame header frame synchronization sequence.

2. The method according to claim 1, characterized in that In step S1 , a ceramic filter or an LC filter is used for filtering.

3. The method according to claim 1, characterized in that In step S2, the ADC sampling frequency f is calculated based on the baseband rate after spread spectrum and the number of samples corresponding to each symbol. s .

4. The method according to claim 3, characterized in that In step S2, the ADC sampling frequency f s =Mf b =mkf b , where f b is the baseband symbol rate before spreading, m is the spreading sequence length, k is the number of samples of each symbol after spreading, and M is the number of samples corresponding to each baseband symbol before spreading.

5. The method according to claim 1, wherein In step S5, the value range of λ is: λ≥5.

6. A BPSK+DSSS differential recursive demodulation device based on multi-phase weighted signal synthesis, characterized in that: Includes the following modules: The signal preprocessing module is used to perform preprocessing on the radio frequency signal, including low-noise amplification, mixing and filtering, to obtain a filtered intermediate frequency signal; The ADC sampling module is used to sample the filtered intermediate frequency signal obtained by the signal preprocessing module to obtain the following ADC sampling sequence: s n =A n cos(ω c nT s )+ξ n (a) Among them, A n represents the signal amplitude, ω c Indicates the RF or IF carrier frequency, T s is the ADC sampling period, f s is the ADC sampling frequency, ξ n represents the noise component; The signal sequence calculation module is used to calculate and obtain the signal sequence by executing the following steps S11-S14 respectively. sequence, sequence, Sequence and V K sequence: S11: The sampling sequence s obtained by the ADC sampling module n The DC bias component in is removed, and formula (b) is used to obtain sequence: Where M is the number of samples corresponding to each baseband symbol before spreading, and K is the baseband symbol spacing before spreading; S12: Based on the data obtained in step S11 Sequence, using formula (c) for cumulative smooth despreading, calculate to obtain sequence: S13: Use formula (d) to Sequence speed reduction extraction, obtain sequence: Among them, X is the speed reduction multiple, is an integer, m is the length of the spreading sequence, and k is the number of samples of each symbol after spreading; S14: Based on the data obtained in step S5 Sequence, use formula (e) to calculate V K sequence: V K The sequence represents the corresponding The equivalent signal amplitude of the sequence, where V K The larger the value, the The larger the equivalent amplitude of the sequence signal, the higher the signal-to-noise ratio, where len represents the number of data samples used for signal amplitude evaluation; Energy evaluation normalization module is used to calculate the energy consumption of K In the sequence, find the maximum value V MAX =V P , and then according to formula (f) Normalize the sequence: Cross-correlation calculation module, used to calculate the normalized Sequence, according to the P value determined by the energy evaluation normalization module, the cross-correlation calculation is performed using formula (g); in, Indicates from The sequence is extracted by extracting one data every λ data, L is the original frame synchronization sequence SYN 0 The length of SYN P The original frame synchronization sequence SYN 0 The new sequence is obtained by multiplying every two elements with an interval of P. LP is the new sequence SYN P length, In the cross-correlation sequence R n Find the maximum value R max , and use this R max The position j is the reference point, and according to formula (h) The sequence is extracted to obtain the sequence Phase difference cosine value calculation module is used to obtain After the sequence, the phase difference cosine value corresponding to different K values ​​is calculated using formula (i): Among them, I l Corresponding to the original frame synchronization sequence SYN 0 Syn l , I l+K Corresponding to the original frame synchronization sequence SYN 0 Syn l+K , Obtained by the above formula (h); The multi-channel signal synthesis decision module is used to make recursive decisions using formula (j): Among them, I n+1 is the symbol to be judged, I n+1-K For the previously determined symbol, L is the original frame synchronization sequence SYN 0 The sequence length, The above formula (h) is used to obtain the symbols of the entire transmission sequence by performing recursive judgment starting from the last few bits of the frame header frame synchronization sequence.

7. The device according to claim 6, characterized in that In the signal preprocessing module, ceramic filters or LC filters are used for filtering.

8. The device according to claim 6, characterized in that In the ADC sampling module, the ADC sampling frequency f is calculated based on the baseband rate after spread spectrum and the number of samples corresponding to each symbol. s .

9. The device according to claim 8, characterized in that In the ADC sampling module, the ADC sampling frequency f s =Mf b =mkf b , where f b is the baseband symbol rate before spreading, m is the spreading sequence length, k is the number of samples of each symbol after spreading, and M is the number of samples corresponding to each baseband symbol before spreading.

10. The device according to claim 6, characterized in that When the signal sequence calculation module executes step S13, the value range of λ is: λ≥5.

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