A design method of adaptive filter for all-source navigation based on environmental cognition

Abstract

This paper proposes a method for designing an adaptive filter for all-source navigation based on environmental cognition, belonging to the field of cognitive navigation in complex environments. This method assigns different environmental constraint weights to different sensors based on the environment. It calculates the current distance constraint weights for each sensor based on the positioning results of the previous moment, generates the actual constraint weights for each sensor in real time, and normalizes them to achieve multi-source sensor fusion positioning. This method is implemented at the positioning end of the multi-source sensor, making it relatively simple to implement. It can dynamically adjust the fusion weight coefficients of different sensors based on the environment, improving the stability and continuity of multi-source fusion positioning.

Description

Technical Field

[0001] The present invention relates to an all-source navigation adaptive filter design method based on environmental cognition. The method belongs to the field of cognitive navigation in complex environments and is an all-source navigation design method. The method is particularly suitable for adaptive dynamic navigation and positioning in complex and changeable environments. Background Art

[0002] Satellite navigation technology plays an increasingly prominent role in information warfare. While providing precise navigation services, the challenges of preventing jamming and deception and improving the system's overall anti-interference capabilities have become pressing. The emergence of cognitive dynamic systems, exemplified by cognitive radio and cognitive radar, has provided new insights into anti-interference research for satellite navigation systems. Consequently, domestic scholars have proposed the concept of cognitive satellite navigation systems. Based on cognitive principles, cognitive satellite navigation systems proactively search for idle spectrum in space and dynamically adjust system response parameters (such as operating frequency, transmit power, pseudo-code type, and modulation scheme) according to specific principles and standards. Through coordinated and coordinated operation, these systems effectively avoid enemy jamming while also preventing interference with other systems. A cognitive satellite navigation system, based on cognitive technology, is a more intelligent navigation information processing system. Based on perception of the current electromagnetic environment, it understands and learns from it, adjusting its internal configuration in a timely manner to adapt to statistical changes in the external environment. Using appropriate models and mechanisms, it ensures efficient and reliable navigation services. Capable of fully leveraging redundant sensor information for reliable navigation at any time and in any location, it will become a key direction for the future development of intelligent navigation and reliable applications. Summary of the Invention

[0003] The present invention addresses the problem of poor stability of navigation and positioning using a single navigation source in a complex electromagnetic environment. It uses environmental information collected by multi-source sensors to assist the fusion of multi-source sensors in performing flexible navigation and positioning, thereby improving the stability of navigation and positioning.

[0004] The technical solution adopted in the present invention is:

[0005] A method for designing an all-source navigation adaptive filter based on environmental cognition includes the following steps:

[0006] (1) Initialize the positioning results of all sensors in the carrier and assign different environmental weight initial values ​​to different sensors based on environmental information and the positioning accuracy of different sensors. The initial value of the environmental weight is related to the positioning accuracy of the sensor in the corresponding environment. The higher the positioning accuracy, the larger the initial value of the environmental weight.

[0007] (2) Calculate the fusion positioning results of all sensors at the initial moment based on the initial positioning results of each sensor and the corresponding initial values ​​of the environmental weights;

[0008] (3) If the environment does not change significantly, the environmental weight at the next moment t remains unchanged. If the environment changes, different weights are assigned to different sensors, that is: Among them, μ n is the weight constraint coefficient of the nth sensor, α n is the environmental impact factor of the nth sensor, p err,n is the positioning accuracy of the nth sensor;

[0009] (4) Calculate the distance difference c between the positioning results of different sensors at time t and the positioning results at the previous moment n (t), and based on the distance difference c n (t) Calculate the weight adjustment coefficient g n (t);

[0010] (5) Calculate the actual weighted value f at time t n (t), i.e.

[0011] f n (t) = g n (t)ω n (t);

[0012] (6) Calculate the fusion positioning result q according to the actual weighted value at time t and the positioning result of the sensor r (t).

[0013] Furthermore, the weight adjustment coefficient g in step (4) n (t) is:

[0014]

[0015] Where, γ n is the weight adjustment coefficient, β n is the conversion adjustment coefficient between speed and distance, δ is the weight adjustment coefficient, and v(t) is the carrier movement speed.

[0016] Furthermore, the fusion positioning result q in step (6) r (t) is:

[0017]

[0018] Where N is the number of sensors, q n (t) is the positioning result of the nth sensor at time t.

[0019] The beneficial effects of the present invention compared to the prior art are:

[0020] The present invention provides an all-source navigation adaptive filter design method based on environmental cognition. The beneficial effects of this method are mainly reflected in the following aspects:

[0021] 1. The method of the present invention can dynamically adjust the fusion weights of different sensors according to environmental information and random positioning accuracy, thereby improving the navigation positioning stability and continuity of multi-source fusion positioning;

[0022] 2. The method of the present invention is implemented at the result end of navigation and positioning, without considering the differences in data formats of different sensors;

[0023] 3. The method of the present invention has a small amount of calculation and is simple to implement, and will not significantly increase the computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of a method for designing an all-source navigation adaptive filter based on environmental cognition according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The present invention will be further described below in conjunction with the accompanying drawings. Figure 1 FIG. 1 is a flow chart of a method for designing an all-source navigation adaptive filter based on environmental cognition disclosed in an embodiment of the present invention, which specifically includes the following steps:

[0026] (1) Initialize the states of all sensors in the carrier. Suppose the positioning results of N sensors at the initial moment are Q(0) = [q1(0),…q n (0),…q N (0)], where q n (0)=[x n (0),y n (0),z n (0)] is the output coordinate of the nth sensor in three-dimensional space. Different sensors are given different initial values ​​of environmental weights according to the environmental information and the positioning accuracy of different sensors. ω(0)=[ω1(0),…ω n (0),…ω N (0)], we have ω1(0)+…ω n (0)…+ω N (0) = 1, ω n (0) is related to the positioning accuracy of the nth sensor in this environment. The higher the positioning accuracy, the greater the ω n (0) the larger the size;

[0027] (2) Calculate the fusion positioning result at the initial moment, that is:

[0028] q r (0)=ω1(0)q1(0)+…ω n (0)q n (0)+…ω N (0)q N (0);

[0029] (3) If the environment does not change significantly, then at the next moment t, ω n (t) = ω n (t-1); if the environment changes, different weights are assigned to different sensors based on prior knowledge, that is: Among them, μ n is the weight constraint coefficient of the nth sensor, α n is the environmental impact factor of the nth sensor, p err,n is the positioning accuracy of the nth sensor;

[0030] (4) Calculate the distance difference between the positioning results of different sensors at time t and the fusion positioning results at the previous moment, that is: c n (t)=||q n (t)-q r (t-1)||,||*|| means to obtain the 2 norm, and let the distance weight adjustment coefficient g n (t) is:

[0031]

[0032] Among them, γ n is the weight adjustment coefficient, β n is the conversion adjustment coefficient between speed and distance, v(t) is the carrier movement speed, δ is the weight adjustment coefficient, and in practical applications, δ takes a value between 10 and 100 according to the actual situation;

[0033] (5) Calculate the actual weighted value f at time t n (t), i.e.

[0034] f n (t) = g n (t)ω n (t)

[0035] (6) Calculate the fusion positioning result at time t, that is:

[0036]

Claims

1. A method for designing an all-source navigation adaptive filter based on environmental cognition, characterized in that: The following steps are involved: (1) Initialize the positioning results of all sensors in the carrier and assign different environmental weight initial values ​​to different sensors based on environmental information and the positioning accuracy of different sensors. The initial value of the environmental weight is related to the positioning accuracy of the sensor in the corresponding environment. The higher the positioning accuracy, the larger the initial value of the environmental weight. (2) Calculate the fusion positioning results of all sensors at the initial moment based on the initial positioning results of each sensor and the corresponding initial values ​​of the environmental weights; (3) If the environment does not change significantly, the environmental weight at the next moment t remains unchanged. If the environment changes, different weights are assigned to different sensors, that is: Among them, μ n is the weight constraint coefficient of the nth sensor, α n is the environmental impact factor of the nth sensor, p err,n is the positioning accuracy of the nth sensor; (4) Calculate the distance difference c between the positioning results of different sensors at time t and the positioning results at the previous moment n (t), and based on the distance difference c n (t) Calculate the distance weight adjustment coefficient g n (t); (5) Calculate the actual weighted value f at time t n (t), i.e. f n (t)=g n (t)ω n (t); (6) Calculate the fusion positioning result q according to the actual weighted value at time t and the positioning result of the sensor r (t).

2. The method for designing an all-source navigation adaptive filter based on environmental cognition according to claim 1, characterized in that: The distance weight adjustment coefficient g in step (4) n (t) is: Where, γ n is the weight adjustment coefficient, β n is the conversion adjustment coefficient between speed and distance, δ is the weight adjustment coefficient, and v(t) is the carrier movement speed.

3. The method for designing an all-source navigation adaptive filter based on environmental cognition according to claim 1, characterized in that: The fusion positioning result q in step (6) r (t) is: Where N is the number of sensors, q n (t) is the positioning result of the nth sensor at time t.

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