A target tracking method based on fusion of simplified signal strength and candidate positions

Abstract

This invention relates to the field of target tracking technology, specifically to a target tracking method based on the fusion of simplified signal strength and candidate positions. The method includes the following steps: establishing a three-level linear simplified received signal strength model; sending a set of candidate target positions to each autonomous system; collecting target signal strength and calculating the median value of the target distance range; filtering candidate positions within the target distance range; calculating the final tracking confidence level using a two-dimensional weighted average for the retained candidate positions; selecting the candidate position with the highest confidence level as the tracking target; the autonomous system moving towards the tracking target, switching to the next candidate position when the signal strength falls below a preset switching threshold; updating the tracking target during movement, and determining successful capture when the highest signal strength is reached and the final tracking confidence level reaches a preset capture threshold. This invention has low computational complexity and low hardware requirements. The fusion of real-time signals and prior positions improves anti-interference capability and pathfinding efficiency. It adopts a decentralized distributed architecture to avoid single points of failure and path conflicts.

Description

Technical Field

[0001] This invention relates to the field of target tracking technology, and more specifically to a target tracking method based on the fusion of simplified signal strength and candidate position. Background Technology

[0002] Multi-autonomous entity target tracking and pathfinding methods are widely used in disaster relief, regional monitoring, warehousing and logistics, and other fields. Their core lies in achieving rapid target acquisition and tracking through information exchange and collaborative decision-making among multiple autonomous entities. However, existing methods generally suffer from the following two shortcomings: (1) Ranging and positioning based on Received Signal Strength Indication (RSSI) is a common method for target tracking. Its core lies in converting signal strength into distance information through a path loss model. Existing methods mostly rely on complex logarithmic distance path loss models, in which the path loss factor and environmental attenuation parameters need to be pre-measured and calibrated for different application environments. Since signals are easily affected by multipath fading, obstacle obstruction, antenna orientation, and other factors in complex indoor or outdoor environments, the model parameters need to be constantly updated with environmental changes; otherwise, the ranging error will increase significantly. This leads to the need for extensive environmental calibration work before deployment of existing methods, high computational load during positioning, and stringent requirements on the computing power of autonomous system hardware, making it difficult to adapt to low-cost autonomous system clusters.

[0003] (2) In multi-autonomous target tracking tasks, the system often has prior knowledge of the candidate locations where the target may appear, such as key monitoring areas and the target's historical locations. However, existing methods either rely excessively on prior candidate locations for pathfinding, lacking real-time signal guidance and resulting in strong blindness; or rely entirely on real-time RSSI signals for tracking, lacking an effective fusion mechanism between prior location information and real-time signals, with each being used in isolation. In addition, in existing pathfinding architectures, although centralized methods can achieve global optimization, they have the risk of single-point failure; once the central controller fails, the entire system is paralyzed. Although distributed methods have strong scalability, they are insufficient in terms of collaborative obstacle avoidance and formation stability. How to achieve decentralized collaborative obstacle avoidance while fully integrating prior location information and real-time sensor data has become a technical problem that urgently needs to be solved in this field.

[0004] Therefore, there is an urgent need for a distributed target tracking method that can make full use of prior candidate location information, effectively integrate real-time signal data, and has low computational cost, low hardware requirements, and no central node dependency, in order to overcome the above-mentioned shortcomings of existing technologies. Summary of the Invention

[0005] To address the problems in existing technologies, such as the high computational cost and hardware requirements due to the reliance on complex path loss models for signal strength ranging, the disconnect between prior location information and real-time sensor data leading to ineffective fusion, the single-point-of-failure risk in centralized architectures, and the insufficient collaborative obstacle avoidance capabilities in distributed architectures, this invention provides a target tracking method based on simplified signal strength and candidate location fusion. By establishing a three-level linear simplified received signal strength model and combining it with a two-dimensional reliability fusion mechanism, this method reduces computational cost while achieving deep binding between real-time signals and prior location information. Based on the fusion result, a fully distributed progressive pathfinding architecture is employed, enabling collaborative obstacle avoidance and target tracking without a central node. The method of this invention includes the following steps: A three-level linear simplified received signal strength model is established, and the target distance range and signal confidence value are matched based on the signal level to which the signal strength belongs. Send a set of target candidate locations to each autonomous region, wherein the target candidate locations include planar coordinates and prior occurrence probabilities; Each autonomous region collects the target signal strength according to the control cycle, inputs it into the three-level linear simplified received signal strength model, and calculates the median value of the target distance range based on the target distance range and the signal confidence value; Calculate the distance between each candidate location and the current location of the autonomous system, filter the candidate locations, and retain the candidate locations that are within the target distance range; For each retained candidate position, a two-dimensional weighted calculation is performed based on the signal confidence value and the prior occurrence probability, and the final tracking confidence of each candidate position is obtained by correcting the deviation of the distance between the candidate position and the current position of the autonomous system relative to the median value. Based on the final tracking confidence level, each candidate position is sorted from high to low, and the candidate position with the highest confidence level is selected as the tracking target for the current control cycle. The autonomous system moves toward the tracking target. When the final tracking confidence of the tracking target is lower than a preset switching threshold, it switches to the next candidate position as the new tracking target according to the sorting results of each candidate position. Each autonomous system broadcasts its own location, current tracking target, and corresponding final tracking credibility to the surrounding area. When multiple autonomous systems select the same tracking target, the autonomous system that is closest to the same tracking target gets priority passage, and the other autonomous systems switch to the next candidate position in their respective candidate position ranking. During its journey, the autonomous system periodically re-executes the steps of collecting target signal strength, screening candidate locations, and calculating final tracking confidence, and updates the tracking target until the collected signal strength reaches the highest signal level and the final tracking confidence reaches the preset capture threshold. At this point, the target is considered successfully captured, the pathfinding is terminated, and the target location is broadcast.

[0006] Furthermore, when the signal strength changes across signal levels during the autonomous system's movement, an emergency fusion calculation is immediately triggered. Each autonomous system stops its current movement toward the target, re-acquires the target signal strength, performs candidate location screening and final tracking confidence calculation, updates the candidate location confidence ranking and the target being tracked, and redetermines the movement path based on the updated results.

[0007] Furthermore, in the three-level linear simplified received signal strength model, the signal levels include strong signal level, medium signal level, and weak signal level. The received signal strength corresponding to the strong signal level is: The corresponding target distance range is The signal reliability value is 1.0. The received signal strength corresponding to the signal level is... and The corresponding target distance range is and The signal reliability value is 0.5. The received signal strength corresponding to the weak signal level is... The corresponding target distance range is and The signal reliability value is 0.2.

[0008] Furthermore, the formula for calculating the final tracking credibility is as follows:

[0009] in, To ultimately track credibility, This represents the signal confidence value. Let the probability of occurrence be prior. This represents the distance between the candidate position and the current position of the autonomous system. This is the median value of the target distance range.

[0010] Furthermore, the prior occurrence probability is obtained in any of the following ways: based on the frequency statistics of the target appearing in each candidate location during a historical period; or based on the preset key area distribution calibration.

[0011] Furthermore, the preset switching threshold is 0.1, and the preset capture threshold is 0.9.

[0012] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention abandons the complex parameter calibration and noise correction process in the traditional path loss model and adopts a three-level linear simplified received signal strength model. The target distance range and signal reliability can be directly mapped by the signal strength amplitude alone, which can be directly adapted to low-cost, low-computing-power autonomous system hardware devices.

[0013] (2) This invention uses a dual-dimensional credibility fusion mechanism to weight and fuse the real-time signal credibility with the prior occurrence probability of the candidate point, and then corrects it by the degree of deviation of the distance between the candidate point and the current position of the autonomous system relative to the median value of the signal distance interval. This achieves a deep binding between real-time sensing data and the target possible position information given by the system. It not only uses prior information to compress the pathfinding range, but also corrects the deviation of the prior point through real-time signals, thus significantly improving the anti-interference capability.

[0014] (3) The priority-based pathfinding logic based on the fusion results of this invention runs independently in each autonomous system without the need for unified scheduling by a central controller. Autonomous systems only coordinate obstacle avoidance through short-distance broadcasts. When multiple autonomous systems select the same target, the right of passage is automatically allocated through distance priority rules, which fundamentally avoids path conflicts and clustering congestion problems, while eliminating the single point of failure risk of centralized architecture.

[0015] (4) The present invention periodically performs fusion calculation and dynamically updates the tracking target during the journey. When the signal strength changes across levels, it immediately responds and corrects the path without having to reach the preset point and then adjust it, which effectively solves the problem of path finding lag in traditional methods. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a target tracking method based on the fusion of simplified signal strength and candidate position according to the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0019] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] This invention provides the following technical solutions: like Figure 1 As shown, this invention provides a target tracking method based on simplified signal strength and candidate location fusion, specifically including the following steps: S1. Establish a three-level linear simplified received signal strength model, and match the corresponding target distance range and signal confidence value based on the signal level to which the signal strength belongs.

[0021] In a preferred embodiment of this application, the three-level linear simplified received signal strength model includes strong signal level, medium signal level, and weak signal level. The received signal strength corresponding to the strong signal level is: The corresponding target distance range is The signal reliability value is 1.0. The received signal strength corresponding to the signal level is... and The corresponding target distance range is and The signal reliability value is 0.5. The received signal strength corresponding to the weak signal level is... The corresponding target distance range is and The signal reliability value is 0.2.

[0022] S2. Send a set of target candidate locations to each autonomous region. The target candidate locations include planar coordinates and prior occurrence probabilities.

[0023] As a preferred embodiment of this application, the prior occurrence probability is obtained in any of the following ways: based on the frequency statistics of the target appearing in each candidate position during a historical period; or based on the preset key area distribution calibration.

[0024] S3. Each autonomous body collects the target signal strength according to the control cycle, inputs it into the three-level linear simplified received signal strength model, and calculates the median value of the target distance range based on the target distance range and the signal confidence value.

[0025] As a preferred embodiment of this application, the formula for calculating the median value of the target distance range is:

[0026] in, This is the midpoint of the target distance range. This represents the lower limit of the target distance range. This represents the upper limit of the target distance range.

[0027] S4. Calculate the distance between each candidate position and the current position of the autonomous system, filter the candidate positions, and retain the candidate positions that are within the target distance range.

[0028] S5. For each retained candidate position, perform a two-dimensional weighted calculation based on the signal confidence value and the prior occurrence probability, and correct it by the degree of deviation of the distance between the candidate position and the current position of the autonomous system relative to the median value, to obtain the final tracking confidence of each candidate position.

[0029] In a preferred embodiment of this application, the formula for calculating the final tracking reliability is as follows:

[0030] in, To ultimately track credibility, This represents the signal confidence value. Let the probability of occurrence be prior. This represents the distance between the candidate position and the current position of the autonomous system. This is the median value of the target distance range.

[0031] S6. Based on the final tracking confidence level, sort the candidate positions from high to low, and select the candidate position with the highest confidence level as the tracking target for the current control cycle.

[0032] S7. The autonomous system moves toward the tracking target. When the final tracking confidence of the tracking target is lower than the preset switching threshold, it switches to the next candidate position as the new tracking target according to the sorting results of each candidate position.

[0033] In a preferred embodiment of this application, the preset switching threshold is 0.1.

[0034] S8. Each autonomous entity broadcasts its own location, current tracking target, and corresponding final tracking credibility to the surrounding area. When multiple autonomous entities select the same tracking target, the autonomous entity that is closest to the same tracking target obtains priority passage, and the remaining autonomous entities switch to the next candidate position in their respective candidate position ranking.

[0035] S9. During the journey, the autonomous system periodically re-executes the steps of collecting target signal strength, screening candidate positions, and calculating the final tracking confidence level, and updates the tracking target until the collected signal strength reaches the highest signal level and the final tracking confidence level reaches the preset capture threshold. Then, the target is determined to be successfully captured, the pathfinding is terminated, and the target position is broadcast.

[0036] In a preferred embodiment of this application, when the signal strength changes across signal levels during the autonomous system's movement, an emergency fusion calculation is immediately triggered. Each autonomous system stops its current movement toward the target, re-acquires the target signal strength, performs candidate position screening and final tracking confidence calculation, updates the candidate position confidence ranking and the target being tracked, and redetermines the movement path based on the updated results.

[0037] In a preferred embodiment of this application, the preset capture threshold is 0.9.

[0038] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A target tracking method based on simplified signal strength and candidate position fusion, characterized in that, Includes the following steps: A three-level linear simplified received signal strength model is established, and the target distance range and signal confidence value are matched based on the signal level to which the signal strength belongs. Send a set of target candidate locations to each autonomous region, wherein the target candidate locations include planar coordinates and prior occurrence probabilities; Each autonomous region collects the target signal strength according to the control cycle, inputs it into the three-level linear simplified received signal strength model, and calculates the median value of the target distance range based on the target distance range and the signal confidence value; Calculate the distance between each candidate location and the current location of the autonomous system, filter the candidate locations, and retain the candidate locations that are within the target distance range; For each retained candidate position, a two-dimensional weighted calculation is performed based on the signal confidence value and the prior occurrence probability, and the final tracking confidence of each candidate position is obtained by correcting the deviation of the distance between the candidate position and the current position of the autonomous system relative to the median value. Based on the final tracking confidence level, each candidate position is sorted from high to low, and the candidate position with the highest confidence level is selected as the tracking target for the current control cycle. The autonomous system moves toward the tracking target. When the final tracking confidence of the tracking target is lower than a preset switching threshold, it switches to the next candidate position as the new tracking target according to the sorting results of each candidate position. Each autonomous system broadcasts its own location, current tracking target, and corresponding final tracking credibility to the surrounding area. When multiple autonomous systems select the same tracking target, the autonomous system that is closest to the same tracking target gets priority passage, and the other autonomous systems switch to the next candidate position in their respective candidate position ranking. During its journey, the autonomous system periodically re-executes the steps of collecting target signal strength, screening candidate locations, and calculating final tracking confidence, and updates the tracking target until the collected signal strength reaches the highest signal level and the final tracking confidence reaches the preset capture threshold. At this point, the target is considered successfully captured, the pathfinding is terminated, and the target location is broadcast.

2. The target tracking method based on simplified signal strength and candidate position fusion according to claim 1, characterized in that, In the three-level linear simplified received signal strength model, the signal levels include strong signal level, medium signal level, and weak signal level.

3. The target tracking method based on simplified signal strength and candidate position fusion according to claim 2, characterized in that, The received signal strength corresponding to the strong signal level is: The corresponding target distance range is The signal confidence level is 1.

0.

4. The target tracking method based on simplified signal strength and candidate position fusion according to claim 2, characterized in that, The received signal strength corresponding to the medium signal level is: and The corresponding target distance range is and The signal reliability value is 0.

5.

5. The target tracking method based on simplified signal strength and candidate position fusion according to claim 2, characterized in that, The received signal strength corresponding to the weak signal level is: The corresponding target distance range is and The signal reliability value is 0.

2.

6. The target tracking method based on simplified signal strength and candidate position fusion according to claim 1, characterized in that, The formula for calculating the final tracking credibility is as follows: in, To ultimately track credibility, This represents the signal confidence value. Let the probability of occurrence be prior. This represents the distance between the candidate position and the current position of the autonomous system. This is the median value of the target distance range.

7. The target tracking method based on simplified signal strength and candidate position fusion according to claim 1, characterized in that, The prior occurrence probability is obtained using any of the following methods: This is based on the frequency statistics of the target appearing at each candidate position during historical periods; The distribution of key areas was determined based on a pre-defined pattern.

8. The target tracking method based on simplified signal strength and candidate position fusion according to claim 1, characterized in that, The preset switching threshold is 0.

1.

9. The target tracking method based on simplified signal strength and candidate position fusion according to claim 1, characterized in that, The preset capture threshold is 0.9.

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