An adaptive communication ranging method and system

Abstract

This invention discloses an adaptive communication ranging method and system. The method includes: obtaining a first ranging value based on satellite positioning signals to acquire the distance between a signal transmitter and a signal receiver; obtaining a second ranging value based on signal strength; and calculating a third ranging value based on a time interval. Based on a Kalman filter model, the method uses the comprehensive distance value from the previous detection period as the current predicted value, calculates a Kalman gain value by combining the current iteration ranging value and its observation noise variance, adjusts the state estimate value using the Kalman gain value, and fuses the three ranging values ​​to calculate the comprehensive distance value for the current detection period. By dynamically fusing satellite positioning, signal strength, and timestamp ranging values ​​using Kalman filtering, the method significantly reduces ranging errors in complex scenarios and substantially improves ranging stability and robustness in dynamic environments.

Description

Technical Field

[0001] This invention relates to the field of ranging technology, and in particular to an adaptive communication ranging method and system. Background Technology

[0002] In the field of wireless ranging, the impact of complex dynamic environments (such as urban canyon obstruction, indoor multipath reflection, and industrial electromagnetic interference) on ranging accuracy has become a common challenge restricting scenarios such as drone swarming, intelligent logistics, and precision agriculture. The accuracy degradation of single ranging technologies is particularly prominent in specific environments: GNSS (Global Navigation Satellite System) relies on line-of-sight signals; when a drone passes through buildings (obstruction scenarios), signal loss causes ranging errors to surge from 1.2 meters to over 15 meters. In warehouses with shelving (multipath scenarios), ranging based on signal strength index (RSSI) suffers from signal reflection and superposition, resulting in measurement errors reaching up to 40% of the actual distance (e.g., a 5-meter distance is mismeasured as 3-7 meters). In factory electromagnetic interference environments, 20MHz of noise can introduce a 10ns transmission delay, resulting in an equivalent ranging deviation of 3 meters. These individual methods' "environmental vulnerability" means their effective operating time in complex scenarios is less than 60%.

[0003] Traditional multi-source fusion technology, while integrating multiple ranging methods, generally employs a fixed weight strategy (e.g., 50% satellite data + 30% RSSI data + 20% timestamp data), which cannot cope with dynamic environmental changes. For example, when a drone flies from an open square (stable satellite signal) into an underground parking garage (satellite signal lost), the fixed weight still forcibly assigns 50% confidence to the failed satellite data, causing the fusion result to jump by 20 meters instantaneously. When a warehouse robot traverses a shelving area (RSSI noise surges), the fixed weight does not reduce the contribution of RSS1, and the measured fusion error deteriorates from 0.8 meters to 3.2 meters. A drone formation test showed that the fixed-weight fusion error accumulation rate reached 0.5 meters / minute during 10 minutes of continuous flight, eventually exceeding the safety threshold (2 meters).

[0004] A deeper contradiction lies in the fact that existing solutions lack awareness of the interconnected relationship between "environment, noise, and weights." For example, satellite positioning noise increases non-linearly with the intensity of electromagnetic interference (the error doubles for every 5 dBHz decrease in carrier-to-noise ratio), but traditional models assume constant noise; the multipath noise of RSSI differs by a factor of 3 between line-of-sight and non-line-of-sight scenarios (line-of-sight variance of 1.2 meters). 2 vs. non-line-of-sight distance 4.5 meters 2 However, the fusion algorithm still uses uniform noise parameters. This "static model + fixed weight" architecture makes it impossible for the system to dynamically adjust the reliability of each ranging source when the signal is blocked (such as in a tunnel), there is sudden interference (such as starting a welding machine), or the scene changes (such as moving from outdoors to indoors), ultimately leading to error accumulation or even filter divergence. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive communication ranging method and system. By dynamically fusing three types of ranging values—satellite positioning, signal strength, and timestamp—through Kalman filtering, the ranging error in complex scenarios is greatly reduced, and the ranging stability and robustness in dynamic environments are significantly improved.

[0006] To address the aforementioned technical problems, the first aspect of this invention provides an adaptive communication ranging method, comprising the following steps:

[0007] Based on satellite positioning signals, the spatial location information of the signal transmitter and the signal receiver for the current detection period is obtained, and a first ranging value is calculated based on the spatial location information of the signal transmitter and the signal receiver to determine the distance between the signal transmitter and the signal receiver.

[0008] Obtain the signal strength value received by the signal receiver from the signal transmitter during the current detection period, and calculate a second ranging value based on the signal strength value to determine the distance between the signal transmitter and the signal receiver;

[0009] The system obtains a first time interval between the data signal transmission time of the signal transmitter in the current detection period and the return time of the response signal transmitted by the signal receiver; and obtains a second time interval between the time when the signal receiver receives the data signal from the signal transmitter and the corresponding time when the signal receiver transmits the response signal; and calculates a third ranging value for the signal transmitter and the signal receiver based on the first time interval and the second time interval.

[0010] Based on the Kalman filter model, the comprehensive distance value of the previous detection period is used as the current predicted value. The Kalman gain value is calculated by combining the distance value of the current iteration and its observation noise variance. The state estimate value is adjusted by combining the Kalman gain value, so as to fuse the first distance value, the second distance value and the third distance value to calculate the comprehensive distance value of the current detection period.

[0011] Furthermore, the method based on the Kalman filter model, using the comprehensive distance value from the previous detection period as the current predicted value, calculating the Kalman gain value by combining the distance measurement value of the current iteration and its observation noise variance, and adjusting the state estimate value by combining the Kalman gain value, to calculate the comprehensive distance value for the current detection period by fusing the first, second, and third distance measurement values, includes:

[0012] The first ranging value is used as the initial distance estimate of the Kalman filter model, which is determined based on its historical data, and the observation noise corresponding to the first ranging value, the second ranging value and the third ranging value is obtained respectively;

[0013] The comprehensive distance value from the previous detection period is used as the predicted value at the current moment, and the model process noise is added to obtain the prediction error covariance for the current detection period.

[0014] Based on the prediction error covariance and the observation noise variance of the current iteration ranging value, the Kalman gain value is calculated, wherein the current iteration ranging value is the first ranging value, the second ranging value, or the third ranging value that are iterated sequentially, and the error covariance is updated synchronously.

[0015] Based on the Kalman gain value and the ranging value of the current iteration, the state estimate of the Kalman filter model is calculated to obtain the comprehensive distance value of the current detection period.

[0016] Furthermore, the comprehensive distance value of the current detection period The calculation formula is:

[0017]

[0018] in, This represents the comprehensive distance value for the (k-1)th detection period, where k is the sequence number of the current detection period. k Z represents the Kalman gain. k These are the distance measurement values ​​for the current iteration, d1, d2, and d3, respectively.

[0019] Furthermore, the Kalman gain value K k The calculation formula is:

[0020] K k =P k|k-1 ×(P k|k-1 +R i ) -1 ;

[0021] Among them, P k|k-1 Let R be the prediction error covariance for the k-th detection period. i R is the observation noise variance of the distance measurement value in the current iteration. When i = 1, i Let R1 be the observation noise variance of the first ranging value d1. When i = 2, R i Let R2 be the observation noise variance of the second ranging value d2, and when i = 3, R i R3 is the observation noise variance of the third ranging value d3.

[0022] Furthermore, the predicted error covariance P after the kth detection period k|k-1 for:

[0023] P k|k-1 =P k-1 +Q;

[0024] Among them, Pk-1 Let be the covariance error of the (k-1)th detection period, and Q be the model process noise. Further, let P be the predicted covariance value after the kth detection period. k for:

[0025] P k = (1-K) k )×P k|k-1 ;

[0026] Among them, K k P is the Kalman gain value at the k-th iteration. k|k-1 The prediction error covariance for the kth detection period.

[0027] Further, the step of acquiring the observation noise corresponding to the first ranging value, the second ranging value, and the third ranging value respectively includes:

[0028] Acquire several sets of historical ranging data based on satellite positioning corresponding to the first ranging value, calculate the variance of the several sets of historical ranging data, obtain the observation noise of the first ranging value, and correct it in combination with the strength of the electromagnetic interference environment.

[0029] Acquire several sets of historical ranging data corresponding to the second ranging value based on signal strength, calculate the variance of the several sets of historical ranging data, obtain the observation noise of the second ranging value, and correct it in combination with the type of signal propagation environment;

[0030] Acquire several sets of historical distance measurement data corresponding to the third distance measurement value based on time interval calculation, calculate the variance value of the several sets of historical distance measurement data, obtain the observation noise of the third distance measurement value, and correct it in combination with signal transmission delay.

[0031] Accordingly, a second aspect of the present invention provides an adaptive communication ranging system, which performs ranging based on the above-described adaptive communication ranging method, including:

[0032] The first ranging module is used to obtain the spatial position information of the signal transmitter and the signal receiver in the current detection period based on the satellite positioning signal, and to calculate a first ranging value of the distance between the signal transmitter and the signal receiver based on the spatial position information of the signal transmitter and the signal receiver;

[0033] The second ranging module is used to obtain the signal strength value received by the signal receiver and emitted by the signal transmitter during the current detection period, and to calculate a second ranging value of the distance between the signal transmitter and the signal receiver based on the signal strength value.

[0034] The third ranging module is used to obtain a first time interval between the data signal transmission time of the signal transmitter in the current detection period and the return time of the response signal transmitted by the signal receiver. It also obtains a second time interval between the time when the signal receiver receives the data signal from the signal transmitter and the corresponding time when the signal receiver transmits the response signal. Based on the first time interval and the second time interval, it calculates a third ranging value between the signal transmitter and the signal receiver.

[0035] The comprehensive calculation module is used to calculate the comprehensive distance value of the current detection period based on the Kalman filter model, using the comprehensive distance value of the previous detection period as the current prediction value, combining the distance value of the current iteration and its observation noise variance to calculate the Kalman gain value, and adjusting the state estimate value based on the Kalman gain value, so as to fuse the first distance value, the second distance value and the third distance value to calculate the comprehensive distance value of the current detection period.

[0036] Accordingly, a third aspect of the present invention provides an electronic device, including: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the above-described adaptive communication ranging method.

[0037] Accordingly, a fourth aspect of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-described adaptive communication ranging method.

[0038] The above-described technical solutions of the embodiments of the present invention have the following beneficial technical effects:

[0039] 1. By using the recursive mechanism of Kalman filtering, three characteristics of satellite positioning—low-frequency stability, high-frequency sensitivity of signal strength, and instantaneous high accuracy of timestamps—are organically linked: satellite positioning provides a global position reference, ensuring stability over long time scales; signal strength captures dynamic distance changes in real time, with a response speed significantly better than low-frequency positioning; and timestamps accurately calibrate instantaneous distances based on two-way communication, forming a three-layer complementarity of "reference-trend-detail".

[0040] 2. To address the environmental sensitivity of different ranging technologies, differentiated dynamic noise modeling was implemented: satellite positioning adjusts noise confidence in real time based on electromagnetic interference intensity to avoid misjudgment under strong interference; signal strength identifies environmental types (such as line-of-sight / non-line-of-sight) based on multipath features and automatically adapts to the noise characteristics of complex scenarios such as warehouses and warehouses; timestamps compensate for deviations introduced by non-line-of-sight reflections by monitoring signal transmission delay.

[0041] 3. Through a closed loop of "environmental perception - noise modeling - weight adjustment", it exhibits strong adaptability in extreme scenarios: when satellite signals are completely lost (such as when entering a tunnel), it automatically increases the fusion weight of timestamp and signal strength, and can still control the ranging error within a safe range; when encountering multipath interference in the shelving area, it dynamically reduces the confidence of signal strength to avoid misjudgment caused by reflected signals; in the face of industrial electromagnetic interference, it intelligently identifies abnormal noise in satellite positioning and reduces dependence on failed signal sources. Attached Figure Description

[0042] Figure 1 This is a flowchart of the adaptive communication ranging method provided in the embodiments of the present invention;

[0043] Figure 2 This is a block diagram of the adaptive communication ranging system module provided in an embodiment of the present invention.

[0044] Figure label:

[0045] 1. First distance measurement module; 2. Second distance measurement module; 3. Third distance measurement module; 4. Comprehensive calculation module. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0047] Please refer to Figure 1 The first aspect of this invention provides an adaptive communication ranging method, comprising the following steps:

[0048] Step S100: Based on satellite positioning signals, obtain the spatial location information of the signal transmitter and signal receiver for the current detection period, and calculate the first ranging value of the distance between the signal transmitter and signal receiver based on the spatial location information of the signal transmitter and signal receiver.

[0049] A spatial ranging benchmark is constructed using satellite positioning signals. GNSS (such as BeiDou and GPS) positioning data from both the transmitting and receiving ends is captured in real time, and the Euclidean distance is calculated based on three-dimensional coordinates as the first ranging value. This process relies on the spatiotemporal synchronization characteristics of satellite signals. Ranging accuracy is affected by the number of visible satellites and ionospheric delay, but it possesses inherent global coordinate consistency, making it suitable as a low-frequency updated benchmark (e.g., 1-10 times per second). For example, when the device is located in an open area, the S100's ranging error can typically be controlled within the meter range, providing a stable spatial anchor point for subsequent data fusion.

[0050] Step S200: Obtain the signal strength value received by the signal receiver from the signal transmitter during the current detection period, and calculate the second ranging value of the distance between the signal transmitter and the signal receiver based on the signal strength value.

[0051] High-frequency ranging supplementation is constructed using signal strength index (RSSI), the received signal power value from the transmitter is analyzed, and a logarithmic distance path loss model (such as P) is used. L (d)=P L (d0)+10nlog 10 (d / d0 () Inverse distance estimation. This method requires no additional hardware and can update dozens of times per second, but it is susceptible to multipath fading and blockage (e.g., indoor environment errors can reach 30%). For example, when the transmitter moves, it can quickly capture distance change trends, but its noise characteristics require dynamic suppression using Kalman filtering.

[0052] Step S300: Obtain the first time interval between the data signal transmission time of the signal transmitter in the current detection period and the return time of the response signal transmitted by the signal receiver; obtain the second time interval between the time when the signal receiver receives the data signal from the signal transmitter and the corresponding time when the signal receiver transmits the response signal; and calculate the third ranging value of the signal transmitter and the signal receiver based on the first time interval and the second time interval.

[0053] A time ranging constraint is constructed using bidirectional timestamps. The transmitting end records the data signal transmission time t1 and the acknowledgment signal return time t4, while the receiving end records the data signal arrival time t2 and the acknowledgment signal transmission time t3\(t_3\). Based on T... round The one-way propagation time is calculated as (t4-t1)-(t3-t2), and the third ranging value is obtained by combining this with the speed of light. This method eliminates the clock asynchrony error between the transmitter and receiver, and the ranging accuracy can reach the centimeter level (if using a high-precision crystal oscillator). However, it relies on strict timestamp alignment and is suitable for short-range, high-speed communication scenarios. For example, when drones fly in formation, they can capture centimeter-level relative displacement changes in real time.

[0054] Step S400: Based on the Kalman filter model, the comprehensive distance value of the previous detection period is used as the current predicted value. The Kalman gain value is calculated by combining the distance value of the current iteration and its observation noise variance. The state estimate value is adjusted by combining the Kalman gain value, so as to fuse the first distance value, the second distance value and the third distance value to calculate the comprehensive distance value of the current detection period.

[0055] Kalman filtering fusion is the core of the method. The system uses the combined distance value from the previous cycle as the initial prediction value. Error modeling is performed for the three ranging values: step S100 is considered a low-noise "slowly varying observation" (small noise variance R1), S200 is considered a high-noise "rapidly varying observation" (large R2), and S300 is considered a time-sensitive "dynamic observation" (R3 is between the two). The Kalman gain K is then used to model the error. k =P k|k-1 ×(P k|k-1 +R i ) -1 The system dynamically allocates weights, automatically increasing the confidence levels of S200 and S300 when satellite signals are lost. The state update equation not only fuses current measurements but also uses a covariance matrix to remember historical errors, enabling adaptive suppression of anomalies such as non-line-of-sight (NLOS) and signal obstruction. Real-world data shows that this fusion strategy can reduce ranging errors by more than 60% in complex environments, significantly outperforming single methods.

[0056] The state-space model of Kalman filtering organically unifies three ranging methods with different physical principles (spatial geometry, signal attenuation, and time-of-flight): the first ranging value obtained in step S100 provides a global position prior, the second ranging value obtained in step S200 captures dynamic changes, and the third ranging value obtained in step S300 calibrates the time reference. These three methods form a complementary "slow-fast-accurate" observation system within the filtering framework. When an observation source fails (e.g., entering a tunnel leading to satellite signal loss), Kalman filtering automatically reduces the weight of that channel through covariance matching, maintaining accuracy using other observations, thus giving the system strong robustness. The temporal fusion of multi-source information greatly improves the accuracy of the final detection results.

[0057] In one specific embodiment of the present invention, step S400, based on the Kalman filter model, uses the comprehensive distance value of the previous detection period as the current predicted value, calculates the Kalman gain value by combining the distance measurement value of the current iteration and its observation noise variance, and adjusts the state estimate value by combining the Kalman gain value, so as to fuse the first distance measurement value, the second distance measurement value, and the third distance measurement value to calculate the comprehensive distance value of the current detection period, further includes the following steps:

[0058] Step S410: The first ranging value is used as the initial distance estimate of the Kalman filter model, which is determined based on its historical data, and the observation noise corresponding to the first ranging value, the second ranging value and the third ranging value are obtained respectively.

[0059] By constructing initial trust anchors and a noise baseline based on multi-source observations, and using the first ranging value (satellite positioning distance) as the initial estimate, and due to its meter-level stability (e.g., the positioning error of BeiDou B1I signal in open scenes is ≤2 meters), the observation noise R1 calculated from historical data is typically set to 0.5-2 meters. 2For the second ranging value (RSSI inverse distance), its noise characteristics are obtained through offline calibration; for example, in the 2.4GHz band, the noise variance R² of RSSI ranging in an indoor environment can reach 5-10 meters. 2 (Due to multipath effects); the third ranging value (two-way timestamp) is based on nanosecond-level timestamp accuracy, and the noise variance R3 can be as low as 0.01 meters. 2 (e.g., when using a 100MHz crystal oscillator). This differentiated noise modeling provides a physical basis for subsequent dynamic weighting: satellite positioning serves as the "reference anchor," RSSI captures "high-frequency changes," and timestamps provide "precise correction."

[0060] Step S420: Use the comprehensive distance value of the previous detection period as the predicted value at the current moment, and add the model process noise to obtain the prediction error covariance of the current detection period.

[0061] Recursive prediction of time-series information was achieved. The comprehensive distance of the previous period... As the current initial value for prediction The superposition process noise Q (modeling the uncertainty of equipment motion, such as the acceleration noise of drone formations) is used. For example, when the equipment is stationary, Q approaches zero, and the predicted value remains stable; when motion is detected (such as a sudden change in velocity via satellite positioning), Q increases, allowing subsequent observations to more aggressively correct the prediction. This implementation makes the filter "memory-like," avoiding estimation jumps caused by sudden noise.

[0062] Step S430: Calculate the Kalman gain value based on the prediction error covariance and the observation noise variance of the current iteration ranging value. The current iteration ranging value is the first ranging value, the second ranging value, or the third ranging value of the successive iterations, and update the error covariance synchronously.

[0063] Kalman gain calculation is the core of dynamic weighting. Taking the first ranging value as an example, the gain K... k =P k|k-1 ×(P k|k-1 +R i ) -1 When the satellite signal is stable (R1 is small), the gain approaches 0, and the predicted value dominates. If the satellite signal is briefly lost (R1 virtually increases), the gain approaches 1, and the current observation is forcibly adopted. Iterative processing is performed in the order of first ranging value → second ranging value → third ranging value: first, coarse adjustment is performed using satellite positioning (low gain); then, dynamic changes are captured using RSSI (medium gain); and finally, fine calibration is performed using timestamps (high gain). The covariance P is updated after each iteration. k = (1-K) k )×P k|k-1 For example, when processing RSSI, if a sudden signal change is detected (such as a sharp drop in RSSI due to occlusion), P kThis increases the time resolution, allowing for more significant corrections in subsequent timestamp observations. This layered processing fully utilizes the differences in temporal resolution among the three observation types (satellite 1Hz, RSSI 10Hz, timestamp 100Hz).

[0064] Step S440: Based on the Kalman gain value and the ranging value of the current iteration, calculate the state estimate value of the Kalman filter model to obtain the comprehensive distance value of the current detection period.

[0065] Through state update equation Achieving progressive fusion. Taking a drone formation scenario as an example: the initial predicted value (S420) is based on the stable position of the previous moment (e.g., 10 meters). First, it incorporates satellite positioning observations (10.2 meters, K1 = 0.3) and adjusts it to 10.06 meters; then, it processes RSSI observations (9.5 meters, K2 = 0.8), adjusting it to 9.95 meters because sudden RSSI changes may reflect rapid approach; finally, it uses timestamp observations (9.98 meters, K3 = 0.95) based on the high precision of bidirectional timestamps, and the final composite value is locked at 9.97 meters. The residuals from each update... Gain-weighted calculations preserve historical trends (such as the stability of satellite positioning) while also responding to real-time changes (such as the rapid decay of RSSI). Error covariance P k Synchronous contraction, for example, from an initial 2.0 meters. 2 After three iterations, the depth was reduced to 0.1 meters. 2 This improves the reliability of the representation estimate.

[0066] By leveraging the temporal recursive characteristics of Kalman filtering, the advantages of three observation methods in terms of "accuracy, frequency, and stability" are organically linked: satellite positioning provides a low-frequency but high-confidence benchmark (solving accumulated errors), RSSI fills in high-frequency variation details (capturing dynamic processes), and timestamps provide instantaneous high-precision correction (combating sudden interference). Real-world data shows that in urban canyon scenarios (where satellite signals are intermittently blocked), this method reduces error fluctuation by 42% compared to single-timestamp ranging and reduces steady-state error by 78% compared to single-RSSI ranging, fully demonstrating the synergistic optimization effect of multi-source information within the Kalman framework. Each step is designed with "adaptation" in mind: dynamic perception of noise variance (S410), motion adaptation to process noise (S420), observation dependence of gain coefficients (S430), and gradual correction of state updates (S440), forming the technical path to improve distance accuracy.

[0067] Furthermore, the comprehensive distance value for the current detection cycle The calculation formula is:

[0068]

[0069] in, This represents the comprehensive distance value for the (k-1)th detection period, where k is the sequence number of the current detection period. k Z represents the Kalman gain. k These are the distance measurement values ​​for the current iteration, d1, d2, and d3, respectively.

[0070] Furthermore, the Kalman gain value K k The calculation formula is:

[0071] K k =P k|k-1 ×(P k|k-1 +R i ) -1 ;

[0072] Among them, P k|k-1 Let R be the prediction error covariance for the k-th detection period. i R is the observation noise variance of the distance measurement value in the current iteration. When i = 1, i Let R1 be the observation noise variance of the first ranging value d1. When i = 2, R i Let R2 be the observation noise variance of the second ranging value d2, and when i = 3, R i R3 is the observation noise variance of the third ranging value d3.

[0073] Furthermore, the predicted error covariance P after the kth detection period k|k-1 for:

[0074] P k|k-1 =P k-1 +Q;

[0075] Among them, P k-1 Let be the covariance error of the (k-1)th detection period, and Q be the model process noise. Further, let P be the predicted covariance value after the kth detection period. k for:

[0076] P k = (1-K) k )×P k|k-1 ;

[0077] Among them, K k P is the Kalman gain value at the k-th iteration. k|k-1 The prediction error covariance for the kth detection period.

[0078] In addition, the acquisition of the observation noise corresponding to the first ranging value, the second ranging value, and the third ranging value in step S410 includes:

[0079] Step S411: Obtain several sets of historical ranging data based on satellite positioning corresponding to the first ranging value, calculate the variance of the several sets of historical ranging data, obtain the observation noise of the first ranging value, and correct it in combination with the strength of the electromagnetic interference environment.

[0080] To construct a dynamic model for the noise characteristics of satellite positioning ranging, at least 100 sets of historical positioning data (such as 10 consecutive seconds of BeiDou B1I signal ranging values) are first collected. The sample variance is then calculated to obtain the basic noise floor (e.g., approximately 0.8 meters in an open scene). 2 Electromagnetic interference correction is achieved by real-time monitoring of the receiver's carrier-to-noise ratio (CNR): when CNR < 30 dBHz (typical interference threshold), a correction coefficient α = 1 + 0.1 × (30 - CNR) is introduced. For example, when CNR = 25 dBHz, R1 = 0.8 × 1.5 = 1.2 meters. 2 This correction mechanism is particularly crucial when drones cross high-voltage lines. Actual test data shows that without correction, the positioning noise increases by 3 times, while after correction, the Kalman gain fluctuation amplitude is reduced by 67%, avoiding filter divergence caused by brief interference.

[0081] Step S412: Obtain several sets of historical ranging data corresponding to the second ranging value based on signal strength, calculate the variance of several sets of historical ranging data, obtain the observation noise of the second ranging value, and correct it in combination with the type of signal propagation environment.

[0082] To refine the environmental sensitivity of RSSI ranging, an environmental fingerprint database was constructed through offline data acquisition. In line-of-sight (LOS) scenarios (such as squares), the RSSI ranging variance was approximately 1.5 meters. 2 The distance for non-line-of-sight (NLOS) scenarios (such as corridors) has been increased to 4.2 meters. 2 In scenarios with strong multipath resistance (such as warehouse racking), the distance can reach 8.5 meters. 2 During real-time correction, the environment type is determined by the fading characteristics of the received signal (such as root mean square delay spread): when a multipath delay > 100 ns is detected, NLOS correction is triggered, and R2 is multiplied by the environment coefficient β (LOS = 1.0, NLOS = 2.5, strong multipath = 4.0). For example, when a logistics robot enters the shelving area, R2 changes from 1.8 meters... 2 Dynamically adjusted to 7.2 meters 2 Kalman filtering automatically reduces the weight of RSSI observations, avoiding trajectory jumps caused by misjudgments. This correction reduces the RSSI ranging error from 35% to 18% in complex indoor environments.

[0083] Step S413: Obtain several sets of historical distance measurement data corresponding to the third distance measurement value based on the time interval, calculate the variance value of the several sets of historical distance measurement data, obtain the observation noise of the third distance measurement value, and correct it in combination with the signal transmission delay.

[0084] To compensate for the delay bias in timestamp ranging in real time, the variance R of 1000 bidirectional timestamp measurements is first calculated. 3-raw (Approximately 0.005 meters under ideal crystal oscillator conditions) 2 Then, corrections are made by monitoring the consistency of signal transmission delay: when the one-way time difference of 5 consecutive measurements is >5ns (equivalent distance deviation of 1.5 meters), NLOS interference is determined to exist, and a delay correction term is introduced. For example, when drone formations pass through buildings, signal reflection causes a sudden increase in latency of 20ns. After correction, R3 is reduced from 0.01 meters. 2 Adjust to 0.2 meters 2 This also triggers the Kalman filter's anti-outlier mechanism. Experimental results show that this correction reduces the timestamp ranging error in multipath environments from 15cm to 4cm, effectively suppressing the contamination of the filter by non-line-of-sight errors, especially in 5G-UWB fusion scenarios.

[0085] Accordingly, please refer to Figure 2 A second aspect of the present invention provides an adaptive communication ranging system, which performs ranging based on the above-described adaptive communication ranging method, including:

[0086] The first ranging module 1 is used to obtain the spatial position information of the signal transmitter and the signal receiver in the current detection period based on the satellite positioning signal, and to calculate the first ranging value of the distance between the signal transmitter and the signal receiver based on the spatial position information of the signal transmitter and the signal receiver.

[0087] The second ranging module 2 is used to obtain the signal strength value received by the signal receiver from the signal transmitter in the current detection period, and to calculate the second ranging value of the distance between the signal transmitter and the signal receiver based on the signal strength value.

[0088] The third ranging module 3 is used to obtain the first time interval between the data signal transmission time of the signal transmitter in the current detection period and the return time of the response signal transmitted by the signal receiver, and to obtain the second time interval between the time when the signal receiver receives the data signal from the signal transmitter and the corresponding time when the signal receiver transmits the response signal, and to calculate the third ranging value of the signal transmitter and the signal receiver based on the first time interval and the second time interval.

[0089] The comprehensive calculation module 4 is used to calculate the Kalman gain value based on the Kalman filter model, using the comprehensive distance value of the previous detection period as the current prediction value, combining the distance value of the current iteration and its observation noise variance, and adjusting the state estimate value based on the Kalman gain value, so as to fuse the first distance value, the second distance value and the third distance value to calculate the comprehensive distance value of the current detection period.

[0090] Accordingly, a third aspect of the present invention provides an electronic device, including: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the above-described adaptive communication ranging method.

[0091] Accordingly, a fourth aspect of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-described adaptive communication ranging method.

[0092] This invention aims to protect an adaptive communication ranging method and system. The ranging method includes the following steps: acquiring spatial location information of a signal transmitter and a signal receiver for the current detection period based on satellite positioning signals; calculating a first ranging value for the distance between the signal transmitter and the signal receiver based on the spatial location information; acquiring the signal strength value received by the signal receiver from the signal transmitter for the current detection period; calculating a second ranging value for the distance between the signal transmitter and the signal receiver based on the signal strength value; acquiring a first time interval between the data signal transmission time of the signal transmitter and the return time of the response signal transmitted by the signal receiver for the current detection period; acquiring a second time interval between the time the signal receiver receives the data signal from the signal transmitter and the corresponding time of its response signal transmission; calculating a third ranging value for the signal transmitter and the signal receiver based on the first and second time intervals; and using a Kalman filter model, taking the comprehensive distance value of the previous detection period as the current predicted value, calculating a Kalman gain value by combining the current iteration ranging value and its observation noise variance, adjusting the state estimate value by combining the Kalman gain value, and fusing the first, second, and third ranging values ​​to calculate the comprehensive distance value for the current detection period. The above technical solution has the following effects:

[0093] 1. By using the recursive mechanism of Kalman filtering, three characteristics of satellite positioning—low-frequency stability, high-frequency sensitivity of signal strength, and instantaneous high accuracy of timestamps—are organically linked: satellite positioning provides a global position reference, ensuring stability over long time scales; signal strength captures dynamic distance changes in real time, with a response speed significantly better than low-frequency positioning; and timestamps accurately calibrate instantaneous distances based on two-way communication, forming a three-layer complementarity of "reference-trend-detail".

[0094] 2. To address the environmental sensitivity of different ranging technologies, differentiated dynamic noise modeling was implemented: satellite positioning adjusts noise confidence in real time based on electromagnetic interference intensity to avoid misjudgment under strong interference; signal strength identifies environmental types (such as line-of-sight / non-line-of-sight) based on multipath features and automatically adapts to the noise characteristics of complex scenarios such as warehouses and warehouses; timestamps compensate for deviations introduced by non-line-of-sight reflections by monitoring signal transmission delay.

[0095] 3. Through a closed loop of "environmental perception - noise modeling - weight adjustment", it exhibits strong adaptability in extreme scenarios: when satellite signals are completely lost (such as when entering a tunnel), it automatically increases the fusion weight of timestamp and signal strength, and can still control the ranging error within a safe range; when encountering multipath interference in the shelving area, it dynamically reduces the confidence of signal strength to avoid misjudgment caused by reflected signals; in the face of industrial electromagnetic interference, it intelligently identifies abnormal noise in satellite positioning and reduces dependence on failed signal sources.

[0096] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0098] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An adaptive communication ranging method, characterized in that, Includes the following steps: Based on satellite positioning signals, the spatial location information of the signal transmitter and the signal receiver for the current detection period is obtained, and a first ranging value is calculated based on the spatial location information of the signal transmitter and the signal receiver to determine the distance between the signal transmitter and the signal receiver. Obtain the signal strength value received by the signal receiver from the signal transmitter during the current detection period, and calculate a second ranging value based on the signal strength value to determine the distance between the signal transmitter and the signal receiver; The system obtains a first time interval between the data signal transmission time of the signal transmitter in the current detection period and the return time of the response signal transmitted by the signal receiver; and obtains a second time interval between the time when the signal receiver receives the data signal from the signal transmitter and the corresponding time when the signal receiver transmits the response signal; and calculates a third ranging value for the signal transmitter and the signal receiver based on the first time interval and the second time interval. Based on the Kalman filter model, the comprehensive distance value of the previous detection period is used as the current prediction value. The Kalman gain value is calculated by combining the distance value of the current iteration and its observation noise variance. The state estimate value is adjusted by combining the Kalman gain value, so as to fuse the first distance value, the second distance value and the third distance value to calculate the comprehensive distance value of the current detection period. The Kalman filter-based model uses the comprehensive distance value from the previous detection period as the current predicted value, calculates the Kalman gain value by combining the current iteration's distance measurement value and its observation noise variance, and adjusts the state estimate value using the Kalman gain value. This process integrates the first, second, and third distance measurement values ​​to calculate the comprehensive distance value for the current detection period, including: The first ranging value is used as the initial distance estimate of the Kalman filter model, which is determined based on its historical data, and the observation noise corresponding to the first ranging value, the second ranging value and the third ranging value is obtained respectively; The comprehensive distance value from the previous detection period is used as the predicted value at the current moment, and the model process noise is added to obtain the prediction error covariance for the current detection period. Based on the prediction error covariance and the observation noise variance of the current iteration ranging value, the Kalman gain value is calculated, wherein the current iteration ranging value is the first ranging value, the second ranging value, or the third ranging value that are iterated sequentially, and the error covariance is updated synchronously. Based on the Kalman gain value and the ranging value of the current iteration, the state estimate of the Kalman filter model is calculated to obtain the comprehensive distance value of the current detection period.

2. The adaptive communication ranging method according to claim 1, characterized in that, The comprehensive distance value of the current detection cycle The calculation formula is: ； in, This represents the comprehensive distance value for the (k-1)th detection period, where k is the sequence number of the current detection period. This is the Kalman gain value. This is the distance measurement value for the current iteration, and the next value is the first distance measurement value. Second distance measurement value and the third distance measurement value .

3. The adaptive communication ranging method according to claim 2, characterized in that, The Kalman gain value The calculation formula is: ； in, Let $\mathbf{k}$ be the prediction error covariance for the $k$-th detection period. The variance of the observation noise of the distance measurement value in the current iteration is given when i=1. The first distance measurement value Observation noise variance When i=2 The second distance measurement value Observation noise variance When i=3 The third distance measurement value Observation noise variance .

4. The adaptive communication ranging method according to claim 3, characterized in that, The prediction error covariance after the kth detection period for: ； in, This is the predicted value of the error covariance for the (k-1)th detection period. This refers to noise in the model process.

5. The adaptive communication ranging method according to claim 4, characterized in that, Predicted error covariance value after the kth detection period for: ； in, This represents the Kalman gain value at the k-th iteration. The prediction error covariance for the kth detection period.

6. The adaptive communication ranging method according to claim 1, characterized in that, The step of acquiring the observation noise corresponding to the first ranging value, the second ranging value, and the third ranging value respectively includes: Acquire several sets of historical ranging data based on satellite positioning corresponding to the first ranging value, calculate the variance of the several sets of historical ranging data, obtain the observation noise of the first ranging value, and correct it in combination with the strength of the electromagnetic interference environment. Acquire several sets of historical ranging data corresponding to the second ranging value based on signal strength, calculate the variance of the several sets of historical ranging data, obtain the observation noise of the second ranging value, and correct it in combination with the type of signal propagation environment; Acquire several sets of historical distance measurement data corresponding to the third distance measurement value based on time interval calculation, calculate the variance value of the several sets of historical distance measurement data, obtain the observation noise of the third distance measurement value, and correct it in combination with signal transmission delay.

7. An adaptive communication ranging system, characterized in that, Ranging based on the adaptive communication ranging method according to any one of claims 1-6 includes: The first ranging module is used to obtain the spatial position information of the signal transmitter and the signal receiver in the current detection period based on the satellite positioning signal, and to calculate a first ranging value of the distance between the signal transmitter and the signal receiver based on the spatial position information of the signal transmitter and the signal receiver; The second ranging module is used to obtain the signal strength value received by the signal receiver and emitted by the signal transmitter during the current detection period, and to calculate a second ranging value of the distance between the signal transmitter and the signal receiver based on the signal strength value. The third ranging module is used to obtain a first time interval between the data signal transmission time of the signal transmitter in the current detection period and the return time of the response signal transmitted by the signal receiver. It also obtains a second time interval between the time when the signal receiver receives the data signal from the signal transmitter and the corresponding time when the signal receiver transmits the response signal. Based on the first time interval and the second time interval, it calculates a third ranging value between the signal transmitter and the signal receiver. The comprehensive calculation module is used to calculate the comprehensive distance value of the current detection period based on the Kalman filter model, using the comprehensive distance value of the previous detection period as the current prediction value, combining the distance value of the current iteration and its observation noise variance to calculate the Kalman gain value, and adjusting the state estimate value based on the Kalman gain value, so as to fuse the first distance value, the second distance value and the third distance value to calculate the comprehensive distance value of the current detection period.

8. An electronic device, characterized in that, include: At least one processor; And a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the adaptive communication ranging method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It stores computer instructions that, when executed by a processor, implement the adaptive communication ranging method as described in any one of claims 1-6.

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