Filtering and decoding joint iterative detection method and device for network control system

Abstract

The invention discloses a filtering and decoding joint iterative detection method and device for a network control system, and the method comprises the steps: calculating the prior probability of transmission data according to a control model and a filtering algorithm, and decoding a received signal; updating the prior probability of transmission data according to a decoding result and a filtering algorithm, and performing iterative detection on a received signal; and updating system output and control input based on an iterative detection result and a filtering algorithm. According to the filtering and decoding joint iterative detection method and device for the network control system, the fusion of communication and control is realized in the aspect of a bottom layer, the transmission error probability can be effectively reduced by utilizing the prior probability of a control model, and the transmission reliability is improved. Moreover, when a transmission error occurs, prediction of system output and control can be carried out, retransmission can be avoided, and the transmission time delay is effectively reduced. According to the method, the reliability and time delay requirements in an industrial scene can be effectively met.

Description

Technical Field

[0001] The present invention relates to the field of control and communication technologies, and in particular to a joint iterative detection method and device for filtering and decoding for a networked control system. Background Art

[0002] In traditional control systems, system components are connected in a point-to-point manner. When system components are geographically distributed, it is difficult to construct a complete control system in this connection manner, or the cost of constructing the system is relatively high. One of the effective means to solve this problem is to adopt networked communication, that is, data exchange between core components in the system, such as controlled objects, sensors, controllers, etc., relies on network transmission to achieve the purpose of remote control. A system based on this connection manner is called a networked control system.

[0003] Existing networked control systems mainly study the control system under conditions such as network communication resources, network transmission delay, quantization error, network attacks, etc. They neither use communication algorithms to improve control performance nor use control systems to improve communication performance. Existing networked control systems still adopt a separate modular design concept and cannot achieve the integration of communication and control. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to propose a joint iterative detection method and device for filtering and decoding for a networked control system, which realizes the integration of communication and control at the underlying level, can effectively reduce the transmission error probability by using the prior probability of the control model, and improves the transmission reliability. And when a transmission error occurs, the system output and control can be predicted, avoiding retransmission and effectively reducing the transmission delay. The present invention can effectively meet the reliability and delay requirements in industrial scenarios.

[0005] The technical solution adopted by the present invention to achieve the above purpose is as follows:

[0006] A joint iterative detection method for filtering and decoding for a networked control system includes the following steps:

[0007] 1) Calculate the prior probability of the transmission data according to the control model and the filtering algorithm, and decode the received signal;

[0008] 2) Update the prior probability of the transmission data according to the decoding result and the filtering algorithm, and perform iterative detection on the received signal;

[0009] 3) Update the system output and control input based on the iterative detection result and the filtering algorithm.

[0010] The step 1) includes the following steps:

[0011] 1.1) Determine the state equation of the networked control system;

[0012] 1.2) Determine the wireless transmission system and the channel model based on the state equation;

[0013] 1.3) Calculate the prior probability of the transmitted data, i.e., the maximum likelihood ratio LLR, according to the control system state equation and the filtering algorithm;

[0014] 1.4) Decode the received signal according to the LLR.

[0015] The said step 1.2) includes the following steps:

[0016] 1.2.1) Determine the quantization method and the transmission bits of the control system:

[0017] Perform q-bit uniform quantization on each element in the system output y k =[y k,1 ,…,y k,d T to obtain the quantization result

[0018]

[0019] where the function v i is the quantization boundary;

[0020] Define the quantization index function

[0021]

[0022] Define the binary representation of i as

[0023]

[0024] The transmission bit b of the control system is

[0025]

[0026] 1.2.2) Perform encoding modulation on the transmission bits:

[0027] Encode b with a CRC code of (K1, K = dq) to obtain a K1-length sequence s = bG1, where G1 represents the CRC generation matrix and the number of CRC bits is K1 - K;

[0028] Encode s with a systematic LDPC code of (N, K1) to obtain an N-length codeword c = sG2, where G2 represents the generation matrix of the systematic LDPC code and the overall generation matrix of the encoding scheme is G = G1G2;

[0029] Perform BPSK modulation on c to obtain the transmitted signal t = 1 - 2c.

[0030] ​Step 1.3) includes the following steps:

[0031] 1.3.1) Use Kalman filtering to predict the output of the control system and then obtain the covariance matrix of the control system output

[0032] 1.3.2) Calculate the prior probability of the transmitted data based on the predicted control output:

[0033] Let Y k,i represent the random variable of the predicted output of the control system, where represents the i-th element on the diagonal, and the probability density function of Y k,i is p(Y k,i =y). The LLR of the bit k,i in estimated according to p(Y =y) is:

[0034]

[0035] Calculate the LLR of the bits in the codeword c according to the generator matrix G:

[0036]

[0037] where g i,j represents the element in the i-th row and j-th column of the generator matrix G.

[0038] Step 1.4) is specifically as follows:

[0039] The received signal is r = t + n, n i , i = 1, 2,..., N, and n is independent and identically distributed Gaussian white noise, following distribution. Calculate the log-likelihood ratio based on the received signal to obtain

[0040]

[0041] Perform BP decoding of the LDPC code according to the log-likelihood ratio L(r i ), i = 1, 2,..., N, to obtain the decoding result and the corresponding log-likelihood ratio If can pass the CRC check, execute Step 3), otherwise, execute Step 2).

[0042] Step 2) includes the following steps:

[0043] 2.1) Initialize the current iteration count i cur = 1;

[0044] 2.2) According to the decoding result Update the prior probability, i.e., the maximum likelihood ratio LLR;

[0045] 2.3) Perform iterative detection on the received signal based on the updated LLR.

[0046] The step 2.2) includes the following steps:

[0047] 2.2.1) Calculate the output probability of the control system recovered by the communication system according to the decoding result:

[0048] According to the decoding result The decoding result of the transmitted bit of the recovered control system Divide into d groups The length of each group is q, representing the control system output y k,i The corresponding decoded bit;

[0049] According to the log-likelihood ratio Calculate the probability of the transmitted bit That is And

[0050] Assume that the control system output recovered by the communication system is Its probability is And

[0051] 2.2.2) Use Kalman filtering to estimate the system state and output of the control system;

[0052] 2.2.3) Calculate the prior probability based on the output probability and output estimate:

[0053] According to the covariance matrix estimation And the control system output Use To obtain the probability And And traverse all 2 qd Kinds To obtain the prior probability

[0054]

[0055] Then, the prior log-likelihood ratio L(c i ) is

[0056]

[0057] The step 2.3) is specifically:

[0058] Update the log-likelihood ratio of the received signal according to the received signal \(r = t + n\) and the prior log-likelihood ratio \(L(c i ), i = 1, 2, …, N\), that is

[0059]

[0060] Perform BP decoding of the LDPC code according to the log-likelihood ratio \(L(r i ), i = 1, 2, …, N\) to obtain the decoding result and the corresponding log-likelihood ratio If can pass the CRC check, then execute step 3); otherwise, judge whether i cur is less than or equal to the preset iterative detection times \(N ite , if i cur ≤N ite , update i cur = i cur +1, execute 2.2), if i cur > N ite , execute step 3).

[0061] The said step 3) includes the following steps:

[0062] 3.1) If the decoding result can pass the CRC check, then restore the system output according to and perform system state and output estimation using Kalman filtering; otherwise, predict the system state and system output using system state recursion;

[0063] 3.2) Update the control input u according to the system output k .

[0064] A joint iterative detection system for filtering and decoding for a networked control system, comprising a memory and a processor; the memory is used to store a computer program; the processor is used to implement the joint iterative detection method for filtering and decoding for the networked control system when executing the computer program.

[0065] The present invention has the following beneficial effects and advantages:

[0066] The present invention realizes the integration of communication and control at the underlying level, can effectively reduce the error probability of transmission by using the prior probability of the control model, and improves the transmission reliability. And when transmission errors occur, it can perform prediction of system output and control, can avoid retransmission, and effectively reduces the transmission delay. The present invention can effectively meet the reliability and delay requirements in industrial scenarios.

[0067] The method of the present invention is particularly suitable for being applied to a control system in an actual industrial scenario and has good practical application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a flowchart of a joint iterative detection method of filtering and decoding for a network control system provided by the present invention;

[0069] Figure 2 It is a device for joint iterative detection of filtering and decoding for a network control system provided by this application;

[0070] Figure 3 It is a comparison diagram of the bit error rate between the method of the present invention and the traditional communication decoding method;

[0071] Figure 4 It is a comparison diagram of the influence of the method of the present invention and the traditional communication decoding method on the control trajectory tracking. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0073] To make the objectives, technical solutions, and advantages of the present disclosure clearer and more understandable, the present disclosure will be further described in detail below with reference to specific embodiments and the accompanying drawings.

[0074] Unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of this specification should have the ordinary meanings understood by those of ordinary skill in the art to which the present disclosure pertains.

[0075] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0076] As Figure 1 shown, the joint iterative detection method of filtering and decoding for a network control system includes the following basic steps:

[0077] Step 1: Calculate the prior probability of the transmitted data according to the control model and the filtering algorithm, and decode the received signal:

[0078] 1.1) Determine the state equation of the network control system. For a network control system, it is described by the following discrete state equation

[0079]

[0080] where, x k is the n-dimensional system state, u k is the m-dimensional control input, y k is the d-dimensional system output, and is an n-dimensional Gaussian white noise with independent and identical distribution, where W and Z are covariance matrices. A is an n×n system matrix, B is an n×m control input matrix, and C is a d×n output matrix.

[0081] 1.2) Determine the wireless transmission system and channel model.

[0082] 1.2.1) Determine the quantization method and the transmission bits of the control system. First, perform q-bit uniform quantization on each element in the system output y k =[y k,1 ,…,y k,d T The quantization range is (-V, V), the step size is The quantization boundaries are v0 = -V, v i =v i-1 +Δ, i = 1, 2, …, 2 q . The quantization levels are located at the midpoints of the quantization intervals, that is The quantization result

[0083]

[0084] where the function

[0085] defines the quantization index function

[0086]

[0087] Define the binary representation of i as

[0088]

[0089] Then, the transmission bits of the control system are

[0090]

[0091] 1.2.2) Encode and modulate the transmission bits.

[0092] First, encode b with a (K1, K = dq) CRC code to obtain a K1-length sequence s = bG1, where G1 represents the CRC generation matrix and the number of CRC bits is K1 - K. Then, encode s with an (N, K1) systematic LDPC code to obtain an N-length codeword c = sG2, where G2 represents the generation matrix of the systematic LDPC code. The overall generation matrix of the encoding scheme is G = G1G2. Finally, perform BPSK modulation on c to obtain the transmission signal t = 1 - 2c.

[0093] 1.3) Calculate the prior probability of the transmission data according to the control model and filtering algorithm.

[0094] ​1.3.1) Predict the output of the control system using Kalman filtering.

[0095] Let the initial state of the Kalman filter be Initial covariance matrix The Kalman filter estimates the system state at time k-1 as The covariance matrix estimate of the system state is The Kalman filter predicts the control system state at time k as

[0096]

[0097] The predicted output of the control system is

[0098]

[0099] The covariance matrix of the control system output is

[0100]

[0101] 1.3.2) Calculate the prior probability of the transmitted data based on the predicted control output.

[0102] Let Y k,i be the random variable representing the predicted output of the control system, where represents the i-th element on the diagonal, and the probability density function of Y k,i is p(Y k,i =y). The LLR of the bit k,i in estimated according to p(Y =y) is

[0103]

[0104] Next, calculate the LLR of the bits in the codeword c according to the generator matrix G, that is,

[0105]

[0106] where represents g i,j the element in the i-th row and j-th column of the generator matrix G.

[0107] 1.4) Decode the received signal.

[0108] The received signal is r = t + n, where n i , i = 1, 2,..., N are independent and identically distributed Gaussian white noises, following distribution. Calculate the log-likelihood ratio according to the received signal, that is,

[0109]

[0110] According to the logarithmic likelihood ratio L(r i ), i = 1, 2, …, N, perform BP decoding of the LDPC code to obtain the decoding result and the corresponding logarithmic likelihood ratio If can pass the CRC check, execute Step 3; if it cannot pass the CRC check, execute Step 2.

[0111] Step 2: Update the prior probability of the transmitted data according to the decoding result and the filtering algorithm, and perform iterative detection on the received signal:

[0112] 2.1) Initialize the current iteration number i cur = 1.

[0113] 2.2) Update the prior probability according to the decoding result.

[0114] 2.2.1) Calculate the output probability of the control system restored by the communication system according to the decoding result. According to the decoding result Restore the decoding result of the bits transmitted by the control system Divide into d groups The length of each group is q, representing the decoded bits corresponding to the control system output y k,i .

[0115] According to the logarithmic likelihood ratio Calculate the probability of the transmitted bit , that is and Assume that the output of the control system restored by the communication system is Its probability is And

[0116] 2.2.2) Use Kalman filtering for system state and output estimation. The Kalman filter estimates the system state at time k - 1 as The covariance matrix of the system state is estimated as The system state recursion based on the control input is

[0117]

[0118] The covariance matrix recursion is

[0119]

[0120] The Kalman gain matrix is

[0121]

[0122] The system state estimation based on the system output is

[0123]

[0124] The covariance matrix estimation of

[0125]

[0126] The control system output obtained by Kalman filtering is

[0127]

[0128] 2.2.3) Calculate the prior probability according to the output probability and output estimation.

[0129] According to and Use the method in 1.3.2) to obtain Furthermore, and And traverse all 2 qd kinds to obtain the prior probability

[0130]

[0131] Then, the prior log-likelihood ratio is

[0132]

[0133] 2.3) Perform iterative detection on the received signal

[0134] According to the received signal r = t + n and the prior log-likelihood ratio L(c i ), i = 1, 2,..., N, update the received signal log-likelihood ratio, that is

[0135]

[0136] According to the log-likelihood ratio L(r i ), i = 1, 2,..., N, perform BP decoding of the LDPC code to obtain the decoding result and the corresponding log-likelihood ratio If can pass the CRC check, execute step 3; if it cannot pass the CRC check, determine whether i cur is less than or equal to the preset iterative detection times N ite , if i cur ≤N ite , update i cur = i cur +1, execute 2.2), if i cur>N ite Execute Step 3.

[0137] Step 3: Update the system output and control input based on the iterative detection result and the filtering algorithm:

[0138] 3.1) If the decoding result can pass the CRC check, according to restore the system output and use the Kalman filter to estimate the system state and output.

[0139] The system state estimate at time k-1 by the Kalman filter is For the system state the covariance matrix estimate is The system state recursion based on the control input is

[0140]

[0141] The covariance matrix recursion is

[0142]

[0143] The Kalman gain matrix is

[0144]

[0145] The system state estimate based on the system output is

[0146]

[0147] the covariance matrix estimate of

[0148]

[0149] The control system output obtained by the Kalman filter is

[0150]

[0151] 3.2) If the decoding result cannot pass the CRC check, use the system state recursion to predict the system state and system output, that is

[0152]

[0153] 3.3) Update the control input u according to the system output k

[0154] ​In the above embodiments provided by the present application, the joint iterative detection method of filtering and decoding for a network control system provided by the present application is introduced from the perspective of communication and control integration. It can be understood that the above method can be implemented in each network element. Each network element, such as a terminal, a base station, a control node, etc., includes corresponding hardware structures and / or software modules for performing various functions. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0155] Figure 2 The joint iterative detection device of filtering and decoding for a network control system provided by the present application includes:

[0156] Transceiver S201: a transmitter and a receiver, used for sending and receiving signals;

[0157] Controller / Processor S202: used for filtering, decoding, and calculating prior probabilities;

[0158] Memory S203: used for storing calculation results;

[0159] Communication interface S204: used to support communication between the joint iterative detection device of filtering and decoding and other network entities.

[0160] The joint iterative detection device of filtering and decoding for a network control system provided by the present application can execute the method steps of the previous embodiments, which will not be elaborated here.

[0161] The function of the above Controller / Processor S202 can be implemented by a circuit or by a general-purpose hardware executing software code. When the latter is adopted, the Memory S203 is further used to store program code executable by the Controller / Processor S202. When the Controller / Processor S202 runs the program code stored in the Memory S203, the foregoing functions are executed.

[0162] It can be understood that Figure 2 only a simplified design of the joint iterative detection device of filtering and decoding is shown. In practical applications, the device can include any number of transceivers, controllers / processors, memories, and / or communication interfaces, etc.

[0163] The present invention has been experimented and simulated in multiple simulation embodiments. Below, based on the test results of the simulation embodiments, the implementation process and performance analysis of the present invention will be introduced in detail:

[0164] Figure 3 This is a comparison chart of the bit error rate between the method of the present invention and the traditional communication decoding method. The communication configuration is code length 64, information bit length 16, CRC length 16, quantization range from -10 to 10, and quantization bit number 16.

[0165] The control system model is

[0166]

[0167] The PID control rate is [K P K I K D = [201.5 0.5]. The present invention has a performance gain of approximately 1.2 dB at a bit error rate of 10 -3 .

[0168] Figure 4 This is a comparison chart of the influence of the method of the present invention and the traditional communication decoding method on the control trajectory tracking. The method of the present invention can better track the control trajectory.

[0169] In specific implementation, the above device can be a terminal or a network-side device. The network-side device can be a base station or a control node.

[0170] The controller / processor of the above base station, terminal, or control node in this application can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in combination with the disclosure of this application. The processor can also be a combination that realizes computing functions, such as a combination of one or more microprocessors, a combination of DSP and microprocessors, and so on.

[0171] The steps of the methods or algorithms described in connection with the disclosure of the present application may be implemented in hardware, or may be implemented by a processor executing software instructions (e.g., program code). The software instructions may be composed of corresponding software modules, and the software modules may be stored in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable hard disk, a CD-ROM, or any other form of storage medium well-known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium may also be a part of the processor. The processor and the storage medium may be located in an ASIC. Additionally, the ASIC may be located in a terminal. Of course, the processor and the storage medium may also exist as discrete components in a terminal.

[0172] Those skilled in the art should be able to realize that, in one or more of the above examples, the functions described in the present application can be implemented in hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. The computer-readable medium includes computer storage media and communication media, where the communication media includes any medium that facilitates the transfer of a computer program from one place to another. The storage media can be any available medium accessible by a general-purpose or special-purpose computer.

[0173] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present application. It should be understood that the above description is only the specific embodiments of the present application and is not used to limit the protection scope of the present application. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present application should be included within the protection scope of the present application.

Claims

1. A joint iterative detection method of filtering and decoding for network control systems, characterized in that It includes the following steps: 1) Calculate the prior probability of the transmitted data according to the control model and filtering algorithm, and decode the received signal; 2) Update the prior probability of the transmitted data according to the decoding result and filtering algorithm, and perform iterative detection on the received signal; 3) Update the system output and control input based on the iterative detection result and filtering algorithm.

2. The joint iterative detection method for filtering and decoding according to claim 1, which is directed to a network control system, is characterized in that, The step 1) includes the following steps: 1.1) Determine the state equation of the network control system; 1.2) Based on the state equation, determine the wireless transmission system and channel model; 1.3) Calculate the prior probability of the transmitted data, i.e., the maximum likelihood ratio LLR, according to the control system state equation and filtering algorithm; 1.4) Decode the received signal according to the LLR.

3. The joint iterative detection method for filtering and decoding for a network control system according to claim 2, wherein The step 1.2) includes the following steps: 1.2.1) Determine the quantization method and the transmitted bits of the control system: For the system output y k = [y k,1 , …, y k,d T perform q-bit uniform quantization on each element to obtain the quantization result ​ Among them, Function y ∈ [v i , v i+1 ), i = 0, 1, …, 2 q -1, v i are quantization boundaries; Define the quantization index function Define the binary representation of i as The transmitted bit b of the control system is 1.2.2) Perform encoding and modulation on the transmitted bits: Encode b with the CRC code of (K1, K=dq) to obtain a K1-length sequence s = bG1, where, G1 represents the CRC generation matrix, and the number of CRC bits is K1 - K; Encode s with the systematic LDPC code of (N, K1) to obtain an N-length codeword c = sG2, where G2 represents the generation matrix of the systematic LDPC code, and the overall generation matrix of the encoding scheme is G = G1G2; Perform BPSK modulation on c to obtain the transmitted signal t = 1 - 2c.

4. The joint iterative detection method of filtering and decoding for a network control system according to claim 2, characterized in that, The step 1.3) includes the following steps: 1.3.1) Use Kalman filtering to predict the output of the control system and then obtain the covariance matrix of the control system output 1.3.2) Calculate the prior probability of the transmitted data according to the predicted control output: Let Y k,i be the random variable of the predicted output of the control system, where \(i = 1,2,\cdots,d\), denotes the \(i\)-th element on the diagonal, and the probability density function of Y k,i is \(p(Y k,i = y)\). The LLR of the bit k,i estimated according to \(p(Y in where \(i = 1,2,\cdots,d, l=q - 1,\cdots,0\) is: Calculate the LLR of the bits in the codeword c according to the generation matrix G; where, g i,j represents the element in the i-th row and j-th column of the generating matrix G.

5. The joint iterative detection method of filtering and decoding for a network control system according to claim 2, characterized in that The step 1.4) is specifically: The received signal is r = t + n, where n i , i = 1, 2, …, N, and n is independent and identically distributed Gaussian white noise, following a distribution. The log-likelihood ratio is calculated based on the received signal to obtain According to the log-likelihood ratio L(r i ), i = 1, 2, …, N, perform BP decoding of the LDPC code to obtain the decoding result and the corresponding log-likelihood ratio i = 1, 2, …, K. If can pass the CRC check, execute step 3); otherwise, execute step 2).

6. The joint iterative detection method of filtering and decoding for a network control system according to claim 1, wherein The step 2) includes the following steps: 2.1) Initialize the current iteration number i cur = 1; 2.2) According to the decoding result Update the prior probability, i.e., the maximum likelihood ratio LLR; 2.3) Perform iterative detection on the received signal based on the updated LLR.

7. The joint iterative detection method of filtering and decoding for network control systems according to claim 6, characterized in that The step 2.2) includes the following steps: 2.2.1) Calculate the probability of the control system output recovered by the communication system according to the decoding result; According to the decoding result Restore the decoding result of the bits sent by the control system Divide into d groups For i = 1, 2, …, d, the length of each group is q, representing the control system output y k,i The corresponding decoded bits; According to the log-likelihood ratio Calculate the probability of the transmitted bit for j = 1, 2, …, K, that is ​ Let the output of the control system for communication system restoration be The probability thereof is And 2.2.2) Use the Kalman filter to estimate the system state and output of the control system; 2.2.3) Calculate the prior probability based on the output probability and output estimation; Covariance matrix estimation and the control system output Using to obtain the probability and And for all 2 qd kinds traverse to obtain the prior probability Then, the prior log-likelihood ratio L(c i ) is 8. The joint iterative detection method for filtering and decoding oriented to a network control system according to claim 6, wherein The step 2.3) is specifically: According to the received signal r = t + n and the prior log-likelihood ratio L(c i ), i = 1, 2, …, N, update the log-likelihood ratio of the received signal, that is According to the logarithmic likelihood ratio L(r i ), i = 1, 2, …, N, perform BP decoding of the LDPC code to obtain the decoding result and the corresponding logarithmic likelihood ratio i = 1, 2, …, K. If can pass the CRC check, then execute step 3); otherwise, determine whether i cur is less than or equal to the preset iterative detection times N ite . If i cur ≤N ite , update i cur = i cur + 1, execute 2.2). If i cur > N ite , execute step 3).

9. The joint iterative detection method for filtering and decoding for a network control system according to claim 1, wherein, The step 3) includes the following steps: 3.1) If the decoding result can pass the CRC check, then according to restore the system output and use Kalman filtering to estimate the system state and output; otherwise, use the system state recursion to predict the system state and system output; 3.2) According to the system output Update the control input u k thereby 10. A joint iterative detection system for filtering and decoding in a network control system, characterized in that, It includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the joint iterative detection method of filtering and decoding for the network control system as described in claims 1 - 9 when executing the computer program.

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