Mobile target detection method, device, equipment and product based on cellular-free network

Abstract

The invention discloses a mobile target detection method, device, equipment and product based on a cellular-free network, and relates to the technical field of wireless network communication, and the method comprises the steps: determining n target detection periods based on the target change degree, and obtaining the predicted target position in the n target detection periods; evaluating the channel state from each node to the predicted target position and the fusion center according to the predicted target position and the channel knowledge map in the n target detection periods; based on the fusion covariance and the channel knowledge map, obtaining an optimal node combination and a corresponding optimal period by calculating a normalized trace; target detection is carried out based on the optimal node combination and the corresponding optimal period, the target position is estimated by fusing node detection information obtained through target detection, and the node detection information comprises information obtained through detection of all nodes participating in detection. According to the method, continuous and accurate tracking of the target position is realized, and the communication efficiency is ensured while the detection precision is improved.

Description

Technical Field

[0001] The present application relates to the field of wireless network communication technology, and in particular to a method, device, equipment and product for detecting a mobile target based on a non-cellular network. Background Art

[0002] The research on communication-awareness integrated systems mainly focuses on centralized communication-awareness integrated systems. This system deploys a macro base station in each cell, but since the communication link between the base station and the user may have significant signal attenuation, the signal quality at the user end is affected, resulting in a decrease in communication performance. In the process of target tracking and positioning, since some targets may be far away from the base station, it is difficult to achieve reliable target detection and tracking. Therefore, a cell-free massive multiple-input multiple-output (MIMO) system is proposed. Unlike a centralized massive multiple-input multiple-output system, a cell-free MIMO system deploys a large number of spatially distributed single-antenna or multi-antenna access points, which are connected to the central processing unit via high-speed links. With the help of spatial multiplexing technology, all access points can provide services to all users and targets in the space on the same time-frequency resources.

[0003] At present, in target detection and positioning, nodes generally measure distance or distance and angle, and each access point can be used as a node for target detection. However, facing the detection needs of moving targets, the accuracy of existing methods often cannot meet actual requirements.

[0004] Therefore, how to improve the detection accuracy of moving targets is a problem that needs to be solved urgently. Summary of the invention

[0005] The main purpose of this application is to provide a mobile target detection method, device, equipment and product based on a non-cellular network, aiming to solve the technical problem of low detection accuracy of mobile targets.

[0006] To achieve the above objectives, the present application proposes a mobile target detection method based on a non-cellular network, the method comprising:

[0007] Determine n target detection cycles based on the target change degree, and obtain the predicted target position within the n target detection cycles;

[0008] Evaluate the channel status from each node to the predicted target position and the fusion center according to the predicted target position and the channel knowledge map within the n target detection cycles;

[0009] Based on the fused covariance and the channel knowledge map, an optimal node combination and a corresponding optimal period are obtained by calculating a normalized trace;

[0010] Target detection is performed based on the optimal node combination and the corresponding optimal period, and the target position is estimated by fusing node detection information obtained by the target detection, wherein the node detection information includes information detected by all nodes participating in the detection.

[0011] In one embodiment, the step of determining n target detection cycles based on the target change degree and obtaining the predicted target position within the n target detection cycles includes:

[0012] Calculate the target change degree according to the target direction change degree and the target speed change degree;

[0013] Based on the target change degree, determining the maximum value of the detection cycle through the value of the pheromone, the value of the pheromone is used to represent the change degree of the detection demand;

[0014] Dividing the maximum value of the detection cycle to obtain n target detection cycles;

[0015] Based on the n target detection cycles, a predicted target position within the n target detection cycles is obtained.

[0016] In one embodiment, the step of determining the maximum value of the detection period by the value of the pheromone based on the target change degree includes:

[0017] Acquiring an initial value of the pheromone, wherein the initial value of the pheromone is used to reflect a change in the detection demand state of the target;

[0018] Update the pheromone value in a decreasing manner according to the volatility coefficient, and record and update the number of iterations;

[0019] When the pheromone value decays to the set minimum threshold, the maximum value of the detection cycle is determined according to the current number of iterations.

[0020] In one embodiment, the step of acquiring the predicted target position within the n target detection cycles based on the n target detection cycles includes:

[0021] During the target detection cycle, the current position and current motion state of the target are obtained;

[0022] Based on the current position and current motion state of the target, the corresponding predicted target position of the target in each detection cycle is predicted by the state transfer matrix to obtain the predicted target position in n target detection cycles.

[0023] In one embodiment, the step of obtaining the optimal node combination and the corresponding optimal period by calculating the normalized trace based on the fused covariance and the channel knowledge map includes:

[0024] Based on the channel knowledge map, the estimated error covariance matrix of each node combination is corrected by channel state data obtained from the channel knowledge map to obtain normalized traces of different node combinations;

[0025] By comparing the normalized traces of the different node combinations, the node combination corresponding to the minimum normalized trace is regarded as the optimal node combination, and the optimal period is the period corresponding to the optimal node combination.

[0026] In one embodiment, the step of evaluating the channel state from each node to the predicted target position and the fusion center according to the predicted target position and the channel knowledge map within the n target detection cycles includes:

[0027] Collecting actual channel data, inputting the actual channel data into an initial channel knowledge map for model training, and obtaining a target channel knowledge map;

[0028] The predicted target position information, fusion center information and each node information within n target detection cycles are input into the target channel knowledge map to obtain the channel status of each node to the predicted target position and the fusion center.

[0029] In one embodiment, the step of performing target detection based on the optimal node combination and the corresponding optimal period, and estimating the target position by fusing the node detection information obtained by the target detection, includes:

[0030] Sending a detection instruction issued by the fusion center to the participating nodes, wherein the detection instruction is used to specify the detection time and the participating nodes;

[0031] Based on the optimal node combination and the corresponding optimal period, target detection is performed according to the detection instruction to obtain node detection information, wherein the node detection information includes distance data and angle data between each participating node and the target;

[0032] The node detection information is transmitted back to the fusion center, and data fusion is performed on the node detection information to obtain fusion information;

[0033] The position information and speed information of the target are calculated according to the fusion information, and the target position is estimated according to the position information and speed information of the target.

[0034] In addition, to achieve the above purpose, the present application also proposes a mobile target detection device based on a non-cellular network, and the mobile target detection device based on a non-cellular network includes:

[0035] A position prediction module, used to determine n target detection cycles based on the target change degree, and obtain the predicted target position within the n target detection cycles;

[0036] A channel evaluation module, used to evaluate the channel status from each node to the predicted target position and the fusion center according to the predicted target position and the channel knowledge map within the n target detection cycles;

[0037] A node optimization module, used for obtaining an optimal node combination and a corresponding optimal period by calculating a normalized trace based on the fused covariance and the channel knowledge map;

[0038] The target detection module is used to perform target detection based on the optimal node combination and the corresponding optimal period, and estimate the target position by fusing the node detection information obtained by the target detection, wherein the node detection information includes the information detected by all nodes participating in the detection.

[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the mobile target detection method based on a non-cellular network are implemented as described above.

[0040] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the mobile target detection method based on a non-cellular network as described above.

[0041] One or more technical solutions proposed in this application have at least the following technical effects:

[0042] Based on the target change degree, n target detection cycles are determined, and the predicted target position within the n target detection cycles is obtained; the channel state from each node to the predicted target position and the fusion center is evaluated according to the predicted target position within the n target detection cycles and the channel knowledge map; based on the fusion covariance and the channel knowledge map, the optimal node combination and the corresponding optimal cycle are obtained by calculating the normalized trace; the target is detected based on the optimal node combination and the corresponding optimal cycle, and the target position is estimated by fusing the node detection information obtained by the target detection. The node detection information includes the information detected by all nodes participating in the detection. By determining the detection cycle based on the target change degree, the system can adaptively adjust the detection frequency according to the motion state of the target. In each detection cycle, by obtaining the predicted target position of the target, the system can locate the target more accurately. Combined with the prediction method of the state transfer matrix, the predicted target position is made more continuous and accurate, thereby improving the overall detection accuracy. The channel state from each node to the target is evaluated using the channel knowledge map to ensure that the node combination with the best channel state is selected during the detection process. The optimal node combination and the optimal detection cycle are selected by calculating the normalized trace to ensure the detection and positioning accuracy while achieving the best resource configuration. After each detection cycle, the target change degree is re-evaluated based on the updated target position. The feedback mechanism ensures continuous and accurate tracking of the target position, ensures the accuracy of detection, and further significantly improves communication efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0045] Figure 1 This is the first schematic diagram of the centralized communication perception integrated system scenario of this application;

[0046] Figure 2 This is the second schematic diagram of the centralized communication perception integrated system scenario of this application;

[0047] Figure 3 This is a flow chart of a first embodiment of a mobile target detection method based on a non-cellular network of the present application;

[0048] Figure 4 This is a flow chart of a second embodiment of a mobile target detection method based on a non-cellular network according to the present application;

[0049] Figure 5 This is a flow chart of a third embodiment of a mobile target detection method based on a non-cellular network of the present application;

[0050] Figure 6 A schematic diagram of the signaling flow of the mobile target detection method without a cellular network according to an embodiment of the present application;

[0051] Figure 7 This is a schematic diagram of the module structure of a mobile target detection device based on a non-cellular network according to an embodiment of the present application.

[0052] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0054] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0055] At present, the research on communication perception integrated system mainly focuses on centralized communication perception integrated system. Figure 1 , Figure 2 As shown in the figure, this system deploys a macro base station in each cell. However, since the communication link between the base station and the user may have significant signal attenuation, the signal quality at the user end is affected, resulting in a decrease in communication performance. In the process of target tracking and positioning, since some targets may be far away from the base station, it is difficult to achieve reliable target detection and tracking. In order to effectively solve the above problems, a cell-free massive multiple-input multiple-output (MIMO) system is proposed. Unlike a centralized massive multiple-input multiple-output system, a cell-free MIMO system deploys a large number of spatially distributed single-antenna or multi-antenna access points. The access points are connected to the central processing unit through high-speed links. With the help of spatial multiplexing technology, all access points can provide services to all users and targets in the space on the same time-frequency resources.

[0056] Since the distance or distance and angle are generally measured by nodes during target detection and positioning, the nodes may be fixed or mobile. The system knows the location coordinates of the node, and after detecting the distance or distance and angle to the target, it sends the information to the fusion center, and obtains the location coordinates and speed of the target using methods such as Kalman filtering or particle filtering. Each access point can be used as a node for target detection. Since multi-node collaboration is required, the detection environment of different detection nodes during target detection, or the communication environment of different detection nodes and the fusion center during communication, is very different. How to select the best node with good detection and communication environment and high positioning accuracy for detection, and realize adaptive adjustment of tracking detection parameters, faces huge challenges.

[0057] The present application provides a solution, firstly constructing a channel knowledge map of the detection area, determining n target detection cycles based on the target change degree, and obtaining the predicted target position within the n target detection cycles; evaluating the channel state from each node to the predicted target position and the fusion center according to the predicted target position within the n target detection cycles and the channel knowledge map; obtaining the optimal node combination and the corresponding optimal cycle by calculating the normalized trace based on the fusion covariance and the channel knowledge map; performing target detection based on the optimal node combination and the corresponding optimal cycle, and estimating the target position by fusing the node detection information obtained by the target detection, the node detection information includes the information detected by all the nodes participating in the detection. By determining the detection cycle based on the target change degree, the system can adaptively adjust the detection frequency according to the motion state of the target. In each detection cycle, by obtaining the predicted target position of the target, the system can locate the target more accurately. Combined with the prediction method of the state transfer matrix, the predicted target position is made more continuous and accurate, thereby improving the overall detection accuracy. The channel state from each node to the target is evaluated by using the channel knowledge map to ensure that the node combination with the best channel state is selected during the detection process, and the optimal node combination and the optimal detection cycle are selected by calculating the normalized trace to ensure the detection and positioning accuracy while achieving the optimal configuration of resources. After each detection cycle, the target change degree is re-evaluated based on the updated target position. The feedback mechanism ensures continuous and accurate tracking of the target position, ensures the accuracy of detection, and further significantly improves communication efficiency.

[0058] Based on this, the embodiment of the present application provides a mobile target detection method based on a non-cellular network, referring to Figure 3 , Figure 3 This is a flow chart of the first embodiment of the mobile target detection method based on non-cellular network of the present application.

[0059] In this embodiment, the mobile target detection method based on non-cellular network includes steps S10 to S40:

[0060] Step S10, determining n target detection cycles based on the target change degree, and obtaining the predicted target position within the n target detection cycles.

[0061] It should be noted that the target change degree can be used to measure the degree of change in the position, speed or motion mode of the target. The target detection cycle can be understood as the time interval for a detection during the target detection process. Multiple cycles can be used to continuously monitor the motion trajectory of the target. Predicting the target position can be estimating the possible position of the target at a certain moment in the future based on the current state and motion model of the target.

[0062] Step S20, evaluating the channel status from each node to the predicted target position and the fusion center according to the predicted target position and the channel knowledge map within the n target detection cycles.

[0063] It should be noted that the channel knowledge map can reflect the information map of the channel status between each node and the predicted target position and the fusion center, which is used to evaluate the signal transmission quality and related parameters, such as signal attenuation. The fusion center can fuse the detection data of each node and finally obtain the position and movement vector of the target. A node can be understood as a device that acts as a signal receiver or transmitter in the system, usually an access point or sensor, which is used to detect the position and status of the target. The channel status can be understood as the transmission quality from each node to the current position of the target, which can include signal-to-noise ratio, attenuation, delay, channel capacity, etc.

[0064] Step S30, based on the fused covariance and the channel knowledge map, the optimal node combination and the corresponding optimal period are obtained by calculating the normalized trace.

[0065] It should be noted that the normalized trace can be understood as the value obtained by fusion calculation using the channel data obtained in the channel knowledge map and the fusion covariance, which is used to evaluate the effectiveness of different node combinations in target detection, and usually represents an indicator of error or signal quality. The optimal node combination can be a set of nodes (or access points) selected when performing target detection, which can provide the best detection performance under the current channel conditions and target dynamic characteristics. The corresponding optimal period can be understood as the most suitable time period for detection in a specific detection task, based on the target change degree and channel conditions.

[0066] Step S40, performing target detection based on the optimal node combination and the corresponding optimal period, and estimating the target position by fusing the node detection information obtained by the target detection.

[0067] It should be noted that the node detection information includes information detected by all nodes participating in the detection. The detection information may be data on the state and position of the target obtained through the node detection, and is used to update the motion state of the target.

[0068] In this embodiment, by determining the detection cycle based on the target change degree, the system can adaptively adjust the detection frequency according to the motion state of the target. In each detection cycle, by obtaining the predicted target position of the target, the system can locate the target more accurately. Combined with the prediction method of the state transfer matrix, the predicted target position is more continuous and accurate, thereby improving the overall detection accuracy. The channel state from each node to the target is evaluated using the channel knowledge map to ensure that the node combination with the best channel state is selected during the detection process. The optimal node combination and the optimal detection cycle are selected by calculating the normalized trace to ensure the detection and positioning accuracy while achieving the best resource allocation. After each detection cycle, the target change degree is re-evaluated according to the updated target position. The feedback mechanism ensures continuous and accurate tracking of the target position, ensures the accuracy of detection, and further significantly improves the communication efficiency.

[0069] Reference Figure 4 , Figure 4 This is a flow chart of the second embodiment of the mobile target detection method based on non-cellular network of the present application. Figure 3 The first embodiment shown here proposes a second embodiment of the mobile target detection method based on a non-cellular network of the present application.

[0070] In the second embodiment, the step S10 includes:

[0071] Step S101, calculating the target change degree according to the target direction change degree and the target speed change degree.

[0072] It should be noted that the target direction change degree can be the change in the direction of the target within a specific time period, usually expressed in angles or radians. The target rate change degree can represent the change in the target speed, usually calculated by the speed difference of the target at different time points. The target change degree can be used as a comprehensive indicator, taking into account the target direction change degree and rate change degree.

[0073] Exemplarily, in each detection cycle, the detection node detects the target and obtains the target's position and movement vector. The data fusion center fuses the detection data of each node and finally obtains the target's position and movement vector. Based on this, the target direction change degree and target speed change degree can be obtained, which are expressed as formula (1) and formula (2) respectively.

[0074]

[0075]

[0076] In formula (1) and formula (2), and are the target movement vectors detected at time t and time tT respectively, and The target movement rate detected by the target at time t and time tT, and It is the speed that the system has detected at time t and time tT. The target is detected to obtain the position and speed information of the next cycle. The initial speed is also detected by the system. o (t, tT) is the target direction change, which represents the ratio of the velocity direction angle between time t and time tT to 2π. o (t, tT) is the target rate change, which represents the ratio of the difference between the target speed at time t and time tT to the speed at time t. The target change can be expressed as formula (3):

[0077] S o (t,tT)=θ o (t,tT)+v o (t,tT) (3)

[0078] S o (t, tT) represents the target change degree, which is the sum of the target direction change degree and the target rate change degree. The larger the target change degree, the greater the target change, and the more frequent the network detection needs to be.

[0079] Step S102, based on the target change degree, the maximum value of the detection cycle is determined by the value of the pheromone.

[0080] It should be noted that the value of pheromone is used to indicate the degree of change in detection demand, and the maximum value of the detection cycle is determined by the value of pheromone, wherein the maximum value of the detection cycle can be understood as the maximum detection time interval that can be sustained in a certain stage. As the target moves, the detection cycle needs to change adaptively. As the degree of change in detection demand increases, the detection cycle needs to become smaller so that the target information can be detected in time; conversely, the detection cycle needs to become larger in order to save energy. Exemplarily, the process of obtaining the degree of change in detection demand is equivalent to the process of obtaining pheromones in the ant colony algorithm, and the value of the degree of change in detection demand is equivalent to the value of the pheromone.

[0081] Step S103, dividing the maximum value of the detection cycle to obtain n target detection cycles.

[0082] Exemplarily, the maximum value of the detection period is represented by T max seconds, 0-T max The second is divided into n parts, and the obtained T j It can be expressed as formula (4):

[0083]

[0084] In formula (4), T jIt is represented as the jth target detection cycle. It can be understood that there are n target detection cycles in total.

[0085] Step S104, based on n target detection cycles, obtaining the predicted target position within n target detection cycles.

[0086] It should be noted that the predicted target position can be calculated based on the dynamic characteristics and motion model of the target in each detection cycle to obtain the expected position of the target in these cycles.

[0087] In this embodiment, by calculating the target change degree, the system can evaluate the motion state of the target in real time, thereby dynamically adjusting the detection cycle to ensure that the target can be effectively tracked under different circumstances. The maximum value of the detection cycle is divided into n target detection cycles, making the detection process more refined and accurate, which helps to obtain multiple detection data in a short time. The pheromone value is used to determine the maximum value of the detection cycle, and the detection frequency can be flexibly adjusted according to changes in detection needs, thereby improving resource utilization and detection accuracy.

[0088] In one embodiment, step S102 includes: obtaining an initial value of the pheromone, the initial value of the pheromone is used to reflect the change state of the detection demand of the target; updating the value of the pheromone in a decreasing manner according to the volatility coefficient, recording and updating the number of iterations; when the pheromone value decays to a set minimum threshold, determining the maximum value of the detection cycle according to the current number of iterations.

[0089] It should be noted that the initial value of the pheromone is used to represent the value of the current detection demand of the target, reflecting the severity or urgency of the target change. The higher the pheromone value, the greater the demand for target detection. The volatility coefficient can be used as a parameter to control the attenuation speed of the pheromone, and the value range is usually between 0 and 1. The value of the pheromone can be the value of the currently recorded detection demand change state. Over time, the value of the pheromone will be updated according to the changes in the environment and the target. The number of iterations can be the number of iterative operations performed in the process of updating the pheromone. Each time the pheromone is updated, the number of iterations increases. The set minimum threshold can be a predefined value. When the pheromone value drops below this value, it means that the detection demand has been reduced to the extent that the current detection cycle can be stopped.

[0090] For example, the initial value Q of the pheromone can be o (t) is set to the most recently perceived change S o (t,tT), expressed as formula (5):

[0091] Q o (t) = S o (t,tT) (5)

[0092] From formula (5), we can see that the last pheromone value Q o (tT) is equal to the last detection demand change S o (tT). Each time the pheromone is updated, its value will become smaller, and this process will be repeated until all pheromones are volatilized. The speed of pheromone volatilization depends on the volatilization coefficient ρ. o The value of (t) is the same as the value of the last pheromone Q o (tT) is determined by comparing the results. Pheromone update is carried out according to formula (6).

[0093] Q k (t) = (1-ρ) k Q o (t) (6)

[0094] In formula (6), k represents the number of pheromone updates, and ρ represents the volatility coefficient, which is expressed as formula (7):

[0095]

[0096] In formula (7), when the detection demand changes little and is relatively stable, the volatilization time can be slightly longer, ρ μ The value of can be slightly smaller; if the detection demand changes moderately, the value of ρ2 can be set to be smaller than ρ μ Slightly larger; detection demand changes greatly, ρ3 can be set to be larger than ρ2. The value range of α is (0,1).

[0097] When Q k ≈0.0001*Q o When , it can be considered that the pheromone has evaporated. At this time, the number of iterations is the new detection cycle T max , the new detection period T max It can be expressed as formula (8):

[0098]

[0099] It can be understood that the target detection update period T max Set the time for pheromone to evaporate. The faster the detection demand changes, the faster the pheromone evaporates. max The smaller the value, the shorter the detection interval; the slower the detection demand changes, the slower the pheromone evaporates, T max When the pheromone evaporates to a small enough size, it is considered that the pheromone has evaporated and the next target detection can be carried out.

[0100] In this embodiment, by obtaining the initial value of the pheromone and dynamically updating the pheromone according to the change of detection demand, the system can adapt to the change of the target state in real time, and by setting the minimum threshold and the number of iterations to calculate the maximum value of the detection cycle, the detection cycle can be adjusted based on actual needs, and the attenuation speed of the pheromone is controlled by the volatility coefficient, so that the system can effectively manage the detection resources. The value of the pheromone is combined with the number of iterations to provide data support for decision-making, improving the flexibility and response speed of detection.

[0101] In one embodiment, step S104 includes: obtaining the current position and current motion state of the target within the target detection cycle; based on the current position and current motion state of the target, predicting the corresponding predicted target position of the target within each detection cycle through a state transfer matrix to obtain the predicted target position within n target detection cycles.

[0102] It should be noted that, usually at time k, the node combination for target detection at time k+1 is determined. For example, target prediction is performed at time k to predict the position of the target at time k+1. Since the effective detection distance of the node is within a certain range, the node that can detect the target can be determined after predicting the position of k+1. The maximum detection period is calculated as T max Seconds and corresponding T j , the position at the next moment can be predicted by formula (9). Assume that the target is a slowly moving maneuvering point. For some complex motion models, a multi-model interaction method can be used. For simplicity, an approximate uniform speed model can be used, and the target state equation can be expressed as formula (10). Formula (9) and formula (10) are expressed as follows:

[0103]

[0104]

[0105] In formula (9) and formula (10), x t represents the target state at time t. t =(x,v x ,y,v y ,z,v z ), where (x, y, z) is the position of the target; (v x ,v y ,v z ) is the velocity in the corresponding x, y, and z directions. is the state transfer matrix of the j-th prediction. is the j-th predicted t+T j The target state at the moment is based on the predicted The target location information and node detection range are used to determine which nodes can detect the target for the jth prediction.

[0106] In this implementation, by obtaining the current position and motion state of the target and combining it with the state transfer matrix for prediction, the position of the target in each future detection cycle can be estimated more accurately, thereby improving the effectiveness of detection. By updating the current position and motion state of the target in real time, the system can quickly adapt to the dynamic changes of the target, thereby achieving continuous tracking of the target and ensuring that the detection strategy is always consistent with the target behavior.

[0107] Reference Figure 5 , Figure 5 This is a flow chart of the third embodiment of the mobile target detection method based on non-cellular network of the present application. Figure 4 The second embodiment shown proposes a third embodiment of the mobile target detection method based on a non-cellular network of the present application.

[0108] In the third embodiment, the step S30 includes:

[0109] Step S301, based on the fusion covariance and channel knowledge map, the estimation error covariance matrix of each node combination is corrected by the channel state data obtained from the channel knowledge map to obtain the normalized traces of different node combinations.

[0110] It should be noted that the node selection can be based on the estimated error covariance matrix and the channel knowledge map. Exemplarily, the target state in the system can be expressed as formula (11) and formula (12):

[0111] x k+1 =Φ k x k +v k , k = 0, 1, ..., (11)

[0112] y k =H k x k +W k ,i=0,1,...,N (12)

[0113] Among them, Φ k is the state transfer matrix, x k is the target state at time k, v k is the process noise, y k is the observation vector, H k is the state observation matrix.

[0114]

[0115]

[0116] In formula (13), is represented by the observation value corresponding to a single target, formula (14) is represented by the state observation matrix, and formula (15) is represented by the measurement noise matrix. Observation noise W k The covariance of can be expressed as formula (16):

[0117]

[0118] Formula (17) is expressed as the observation noise covariance corresponding to a single target, where R k is expressed as the noise covariance matrix, so the standard result of Kalman filtering is expressed as formula (18), The covariance of the tracking error can be expressed as formula (19).

[0119]

[0120]

[0121] In formulas (18) and (19), x k / k It is expressed as a posterior estimate, and the adaptive expression is formula (20), x k / k-1 It is expressed as the predicted state, specifically expressed as formula (21), P k / k It is expressed as the posterior covariance, specifically expressed as formula (22), It is expressed as the inverse matrix of the prediction error covariance matrix, and the corresponding expression is formula (23).

[0122]

[0123] P k / k =E[(x k / k -x k )(x k / k -x k )′|Y k ] (twenty two)

[0124] P k / k-1 =E[(x k / k-1 -x k )(x k / k-1 -x k )′|Y k-1 ] (twenty three)

[0125] Combining formula (20) to formula (23) yields formula (24):

[0126]

[0127] Based on the above formula, a prediction can be made to obtain formula (25) to formula (28), which are expressed as follows:

[0128]

[0129]

[0130] The time axis is expressed as formula (29):

[0131]

[0132] In order to further select the optimal combination from the candidate cycles and nodes, and predict the tracking effects of different cycles and node combinations. Since the above derivation does not require measurement information to calculate the estimated error covariance P of the extended Kalman filter, this means that the estimated error covariance matrix P of each node at time k+1 can be obtained at time k. k+1 / k+1 , the node selection can be performed based on the estimated error covariance matrix. Considering the impact of the channel environment, the communication channel and detection channel of each detection node can be obtained from the channel knowledge map, so we introduce the modified fusion covariance, which can be expressed as formula (30) or formula (31).

[0133]

[0134] In formulas (30) and (31), Represents the predicted t+T j The fusion covariance matrix of any N nodes at any time, where i is the i-th node, C i represents the communication path loss from the i-th node to the fusion center, M i Represents the detection path loss from the i-th node to the predicted target.

[0135] Considering that the fusion covariance in this embodiment has both position error and speed error, and the dimensions are not uniform, we introduce the normalized trace S to define the pros and cons of the nodes, calculate the S of different node combinations in different periods, and select the node combination and period with the smallest S to participate in the detection and positioning of the next period. The normalized trace S can be expressed as formula (32):

[0136]

[0137] In formula (32), p 11 Represents the fusion covariance matrix The element in the first row and first column of .

[0138] Step S302 , by comparing the normalized traces of the different node combinations, the node combination corresponding to the minimum normalized trace is regarded as the optimal node combination, and the optimal period is the period corresponding to the optimal node combination.

[0139] Exemplarily, the next detection cycle and which nodes will perform detection in the next cycle are calculated, and the calculation is performed in the fusion center. By comparing the normalized traces of different node combinations, the node combination corresponding to the minimum normalized trace is regarded as the optimal node combination. Based on the optimal node combination, the fusion center sends instructions to the nodes participating in the detection in the next cycle, instructing the nodes to perform detection in the next cycle. The detection results will be sent to the fusion center, and the coordinate position of the target will be located by calculation in the fusion center.

[0140] In this embodiment, by fusion calculation based on the channel knowledge map and the estimated error covariance matrix, the normalized traces of different node combinations are obtained, and the performance of each node combination in the target detection process can be effectively evaluated. By comparing these normalized traces, the node combination corresponding to the minimum normalized trace is identified as the optimal node combination, and the corresponding cycle is identified as the optimal cycle, ensuring that the node combination with the best performance is selected in target detection, thereby improving the accuracy and efficiency of detection, while optimizing the allocation of resources, reducing redundant detection, and enhancing the overall performance of the system.

[0141] In one implementation, based on the above-mentioned embodiments and implementation methods, the step of evaluating the channel state from each node to the predicted target position and the fusion center according to the predicted target position and the channel knowledge map within n target detection cycles includes: collecting actual channel data, inputting the actual channel data into the initial channel knowledge map for model training, and obtaining the target channel knowledge map; inputting the predicted target position information, the fusion center information, and the information of each node within n target detection cycles into the target channel knowledge map to obtain the channel state from each node to the predicted target position and the fusion center.

[0142] Exemplarily, the channel knowledge map can be used to obtain the signal-to-noise ratio of the probe channel, and the channel knowledge map construction method based on the expectation maximization algorithm can be used to obtain the channel knowledge data of the local communication environment through offline ray tracing simulation, offline field measurement or online real-time measurement. The relevant channel knowledge is statistically modeled according to the knowledge base, and the K-type parameters of the hybrid statistical model are estimated using an iterative algorithm to construct a channel knowledge map reflecting the local signal propagation environment; when communication or positioning is required, real-time location information is obtained through GPS, Beidou, cellular positioning, laser radar and self-sensor positioning, and the channel knowledge of the target location is obtained using the previously constructed channel knowledge map and the inverse distance weighted method, so as to be used for environment-aware adaptive communication. Exemplarily, based on the user-side geographic location information, the channel knowledge data reflecting the actual propagation environment of the local signal is obtained through offline ray tracing simulation, offline field measurement or online real-time measurement. According to the statistical channel model containing expert knowledge, the degree of freedom of the statistical channel model parameter set is increased from 1 to K, and the obtained channel knowledge data is combined with the iterative algorithm to estimate the K-type parameters of the hybrid model to construct the corresponding channel knowledge map.

[0143] In this implementation, by acquiring channel data from different nodes to the predicted target and fusion center, the system can deeply understand the characteristics of the communication environment. The channel knowledge map provides a comprehensive view of the channel status between the node and the predicted target and fusion center, helping the system to more effectively allocate communication and detection resources when performing target detection. By constructing a channel knowledge map, the system can select the optimal node during target detection, improve the accuracy of target positioning and tracking, and reduce the impact of signal attenuation. The construction of the channel knowledge map enables the system to perform adaptive communication and optimize the working parameters of the node, thereby improving the communication quality and the reliability of target detection.

[0144] In one embodiment, the step of performing target detection based on the optimal node combination and the corresponding optimal period, and estimating the target position by fusing the node detection information obtained by the target detection, includes: sending a detection instruction issued by a fusion center to participating nodes, the detection instruction is used to specify the detection time and participating nodes; based on the optimal node combination and the corresponding optimal period, performing target detection according to the detection instruction to obtain node detection information, the node detection information includes distance data and angle data between each participating node and the target; transmitting the node detection information back to the fusion center, and performing data fusion on the node detection information to obtain fusion information; calculating the target's position information and speed information based on the fusion information, and estimating the target position based on the target's position information and speed information.

[0145] It should be noted that after the fusion center calculates the position information and speed information based on the node detection information, it can predict the target position in different periods, determine the nodes within the effective detection distance of each period, and determine the next network detection period and the nodes of the detection target based on the estimation error covariance matrix corrected by the signal-to-noise ratio in the channel knowledge map. Based on the updated detection period and the nodes of the detection target, the fusion center once again sends the detection time to the nodes that need to participate in the detection and conducts a new round of detection. Exemplary, the signaling process of the mobile target detection method without a cellular network is as follows: Figure 6 shown.

[0146] In this embodiment,

[0147] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the mobile target detection method based on a non-cellular network of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0148] This application also provides a mobile target detection device based on a non-cellular network, please refer to Figure 7 , the mobile target detection device based on non-cellular network includes:

[0149] A position prediction module 10, for determining n target detection cycles based on the target change degree, and obtaining the predicted target position within the n target detection cycles;

[0150] A channel evaluation module 20, configured to evaluate the channel status from each node to the predicted target position and the fusion center according to the predicted target position and the channel knowledge map within the n target detection cycles;

[0151] A node optimization module 30, configured to obtain an optimal node combination and a corresponding optimal period by calculating a normalized trace based on the fused covariance and the channel knowledge map;

[0152] The target detection module 40 is used to perform target detection based on the optimal node combination and the corresponding optimal period, and estimate the target position by fusing the node detection information obtained by the target detection, wherein the node detection information includes the information detected by all nodes participating in the detection.

[0153] The mobile target detection device based on a non-cellular network provided by the present application adopts the mobile target detection method based on a non-cellular network in the above-mentioned embodiment, which can solve the technical problem of low detection accuracy of mobile targets. Compared with the prior art, the beneficial effects of the mobile target detection device based on a non-cellular network provided by the present application are the same as the beneficial effects of the mobile target detection method based on a non-cellular network provided by the above-mentioned embodiment, and the other technical features of the mobile target detection device based on a non-cellular network are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0154] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the mobile target detection method based on a non-cellular network in the above-mentioned embodiment.

[0155] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0156] The computer-readable storage medium may be included in the mobile target detection device based on no cellular network; or may exist independently without being assembled into the mobile target detection device based on no cellular network.

[0157] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by a mobile target detection device based on a non-cellular network, the mobile target detection device based on the non-cellular network: determines n target detection cycles based on the target change degree, and obtains the predicted target position within the n target detection cycles; evaluates the channel state from each node to the predicted target position and the fusion center according to the predicted target position and the channel knowledge map within the n target detection cycles; obtains the optimal node combination and the corresponding optimal cycle by calculating the normalized trace based on the fusion covariance and the channel knowledge map; performs target detection based on the optimal node combination and the corresponding optimal cycle, and estimates the target position by fusing the node detection information obtained by the target detection, wherein the node detection information includes the information detected by all nodes participating in the detection.

[0158] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAn) or a wide area network (WAn), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0159] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0160] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0161] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned mobile target detection method based on a non-cellular network, and can solve the technical problem of low detection accuracy of mobile targets. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the mobile target detection method based on a non-cellular network provided in the above-mentioned embodiment, and will not be repeated here.

[0162] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned mobile target detection method based on non-cellular network when executed by a processor.

[0163] The computer program product provided by the present application can solve the technical problem of low detection accuracy of mobile targets. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the mobile target detection method based on non-cellular network provided by the above embodiment, which will not be repeated here.

[0164] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A mobile target detection method based on non-cellular network, characterized in that: The method includes: Determine n target detection cycles based on the target change degree, and obtain the predicted target position within the n target detection cycles; Evaluate the channel status from each node to the predicted target position and the fusion center according to the predicted target position and the channel knowledge map within the n target detection cycles; Based on the fused covariance and the channel knowledge map, an optimal node combination and a corresponding optimal period are obtained by calculating a normalized trace; Target detection is performed based on the optimal node combination and the corresponding optimal period, and the target position is estimated by fusing node detection information obtained by the target detection, wherein the node detection information includes information detected by all nodes participating in the detection.

2. The method according to claim 1, characterized in that The step of determining n target detection cycles based on the target change degree and obtaining the predicted target position within the n target detection cycles includes: Calculate the target change degree according to the target direction change degree and the target speed change degree; Based on the target change degree, determining the maximum value of the detection cycle through the value of the pheromone, the value of the pheromone is used to represent the change degree of the detection demand; Dividing the maximum value of the detection cycle to obtain n target detection cycles; Based on the n target detection cycles, a predicted target position within the n target detection cycles is obtained.

3. The method according to claim 2, characterized in that The step of determining the maximum value of the detection period by the value of the pheromone based on the target change degree comprises: Acquiring an initial value of the pheromone, wherein the initial value of the pheromone is used to reflect a change in the detection demand state of the target; Update the pheromone value in a decreasing manner according to the volatility coefficient, and record and update the number of iterations; When the pheromone value decays to the set minimum threshold, the maximum value of the detection cycle is determined according to the current number of iterations.

4. The method according to claim 2, characterized in that The step of acquiring the predicted target position within the n target detection cycles based on the n target detection cycles comprises: During the target detection cycle, the current position and current motion state of the target are obtained; Based on the current position and current motion state of the target, the corresponding predicted target position of the target in each detection cycle is predicted by the state transfer matrix to obtain the predicted target position in n target detection cycles.

5. The method according to claim 1, characterized in that The step of obtaining the optimal node combination and the corresponding optimal period by calculating the normalized trace based on the fused covariance and the channel knowledge map includes: Based on the channel knowledge map, the estimated error covariance matrix of each node combination is corrected by channel state data obtained from the channel knowledge map to obtain normalized traces of different node combinations; By comparing the normalized traces of the different node combinations, the node combination corresponding to the minimum normalized trace is regarded as the optimal node combination, and the optimal period is the period corresponding to the optimal node combination.

6. The method according to any one of claims 1 to 5, characterized in that The step of evaluating the channel state from each node to the predicted target position and the fusion center according to the predicted target position and the channel knowledge map within the n target detection cycles includes: Collecting actual channel data, inputting the actual channel data into an initial channel knowledge map for model training, and obtaining a target channel knowledge map; The predicted target position information, fusion center information and each node information within n target detection cycles are input into the target channel knowledge map to obtain the channel status of each node to the predicted target position and the fusion center.

7. The method according to claim 1, characterized in that The step of performing target detection based on the optimal node combination and the corresponding optimal period, and estimating the target position by fusing the node detection information obtained by the target detection, comprises: Sending a detection instruction issued by the fusion center to the participating nodes, wherein the detection instruction is used to specify the detection time and the participating nodes; Based on the optimal node combination and the corresponding optimal period, target detection is performed according to the detection instruction to obtain node detection information, wherein the node detection information includes distance data and angle data between each participating node and the target; The node detection information is transmitted back to the fusion center, and data fusion is performed on the node detection information to obtain fusion information; The position information and speed information of the target are calculated according to the fusion information, and the target position is estimated according to the position information and speed information of the target.

8. A mobile target detection device based on a non-cellular network, characterized in that: The device comprises: A position prediction module, used to determine n target detection cycles based on the target change degree, and obtain the predicted target position within the n target detection cycles; A channel evaluation module, used to evaluate the channel status from each node to the predicted target position and the fusion center according to the predicted target position and the channel knowledge map within the n target detection cycles; A node optimization module, used for obtaining an optimal node combination and a corresponding optimal period by calculating a normalized trace based on the fused covariance and the channel knowledge map; The target detection module is used to perform target detection based on the optimal node combination and the corresponding optimal period, and estimate the target position by fusing the node detection information obtained by the target detection, wherein the node detection information includes the information detected by all nodes participating in the detection.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the mobile target detection method based on a non-cellular network as described in any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the mobile target detection method based on non-cellular network as claimed in any one of claims 1 to 7 are implemented.

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