CN113381797A - Unmanned aerial vehicle information monitoring method based on generalized tensor compression - Google Patents

Unmanned aerial vehicle information monitoring method based on generalized tensor compression Download PDF

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CN113381797A
CN113381797A CN202110597040.9A CN202110597040A CN113381797A CN 113381797 A CN113381797 A CN 113381797A CN 202110597040 A CN202110597040 A CN 202110597040A CN 113381797 A CN113381797 A CN 113381797A
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aerial vehicle
unmanned aerial
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khatri
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韩曦
师嘉晨
刘芹
虞欣
王立军
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North China University of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
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    • H04B7/18506—Communications with or from aircraft, i.e. aeronautical mobile service
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Abstract

The invention discloses an unmanned aerial vehicle information monitoring method based on generalized tensor compression, which comprises the following steps: step 1, carrying out random initialization on a signal matrix and a channel matrix acquired by an unmanned aerial vehicle; step 2, calculating least square method estimation of a signal matrix and a coding matrix Khatri-Rao product; step 3, calculating a signal matrix by using the inverse operation of the Khatri-Rao product; step 4, solving a channel matrix from the user to the base station by using generalized expansion of a tensor of a signal received by the unmanned aerial vehicle and Khatri-Rao product inverse operation; step 5, calculating least square estimation of a channel matrix from the base station to the unmanned aerial vehicle; and 6, repeating the steps until the convergence condition is met, wherein the difference of the absolute values of the obtained iterative cost functions is smaller than a minimum value. The generalized tensor compressed unmanned aerial vehicle information monitoring method effectively avoids using a training sequence, can obtain accurate information, has high convergence speed and does not need prior information.

Description

Unmanned aerial vehicle information monitoring method based on generalized tensor compression
Technical Field
The invention belongs to the field of information monitoring, and particularly relates to an unmanned aerial vehicle information monitoring method based on generalized tensor compression.
Background
The application of information technology plays an increasingly important role in the field of modern military, and an unmanned aerial vehicle as one of the key technologies of modern wireless communication plays an important role in military conflict, particularly in severe battlefield environment. An unmanned aerial vehicle needs to acquire information at high altitude to complete information transmission tasks such as massive images, videos and remote sensing measurement data, accurate information is acquired in the process, some information acquisition methods are already used for monitoring information, for example, signal acquisition under a time-varying fading channel needs a training sequence, a semi-blind signal estimation algorithm needs prior information, and related research in the field is relatively few.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle information monitoring method based on generalized tensor compression, and aims to solve the problems that a related algorithm in the background technology needs a training sequence, prior information and the like.
The invention provides an unmanned aerial vehicle information monitoring system based on generalized tensor compression, which comprises the following steps:
step 1, performing random initialization on a signal matrix and a channel matrix;
step 2, calculating least square estimation of a Khatri-Rao product;
step 3, calculating a signal matrix by using Khatri-Rao product inverse operation;
step 4, solving a channel matrix H according to tensor generalized expansion and Khatri-Rao inverse operation1；
Step 5, calculating a channel matrix H2Estimating by a least square method;
and 6, repeating the steps until the convergence condition is smaller than the minimum value.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, a transmission signal of an information source is constructed into a matrix Khatri-Rao product structure, the signal can be expressed into a PARAFAC model after being transmitted through a channel, and the derivation of a receiver based on an iterative recursive least square method is simplified. Compared with a two-step training algorithm and a zero-forcing algorithm, the generalized tensor compression method is more effective, avoids using a training sequence, and has similar performance to the zero-forcing algorithm and does not need prior information.
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FIG. 1 is a system model of the present invention;
FIG. 2 is a bit error rate curve of a generalized tensor compression algorithm, a two-step training algorithm and a zero forcing algorithm.
Detailed Description
The present invention is described in detail with reference to the embodiments shown in the drawings, but it should be understood that these embodiments are not intended to limit the present invention, and those skilled in the art should understand that functional, methodological, or structural equivalents or substitutions made by these embodiments are within the scope of the present invention.
The embodiment provides an unmanned aerial vehicle information monitoring method based on generalized tensor compression, which comprises the following steps:
step 1, performing random initialization on a signal matrix and a channel matrix;
step 2, calculating least square estimation of a Khatri-Rao product;
step 3, calculating a signal matrix by using Khatri-Rao product inverse operation;
step 4, solving a channel matrix H according to tensor generalized expansion and Khatri-Rao inverse operation1；
Step 5, calculating a channel matrix H2Estimating by a least square method;
and 6, repeating the steps until the convergence condition is smaller than the minimum value.
The invention has obvious advantages compared with other inventions, including:
(1) compared with the zero-forcing algorithm, the two-step training algorithm has the advantages that the generalized tensor compression is more effective, and the training sequence is avoided.
(2) It has similar performance to the zero-forcing algorithm and does not require a priori information.
In order to verify the performance of the proposed algorithm, a system model is built to analyze the method. It should be noted that: once an item is defined in a drawing, it need not be further defined and explained in subsequent drawings.
Referring to fig. 1, fig. 1 is a system model diagram of the present invention.
Step 1, for signal matrix S and channel matrix H1、H2And (4) random initialization.
Figure BDA0003091532800000021
Figure BDA0003091532800000022
Figure BDA0003091532800000023
H1And H2Respectively user-to-base station and base station-to-user joint matrices, H11And H12Representing the channel matrix, H, of user 1 to base station 1 and user 2 to base station 221And H22Representing the channel matrix from base station 1 to drone and from base station 2 to drone, S1And S2Signal matrices for user 1 and user 2, M1And M2Denotes the number of antennas, M, for user 1 and user 2B1And MB2Denotes the number of antennas, M, of base station 1 and base station 2SRepresenting the number of antennas of the drone and N representing the noise matrix.
Step 2, calculating the least square estimation of the Khatri-Rao product
Figure BDA0003091532800000031
S represents a signal matrix, C represents an encoding matrix, G represents an amplification matrix,
Figure BDA0003091532800000032
representing the tensor of the received signal.
Step 3, solving the signal matrix after the nth iteration by using the inverse operation of the Khatri-Rao product
Figure BDA0003091532800000033
Let the Khatri-Rao product operate as
Figure BDA0003091532800000034
Is the inverse operation of the Khari-Rao product, and since C is known, it is solved by inv _ KR operation according to equation (4)
Figure BDA0003091532800000035
Step 4, generalized expansion according to tensor
Figure BDA0003091532800000036
Computing
Figure BDA0003091532800000037
Using the inverse of the Khatri-Rao product, one obtains
Figure BDA0003091532800000038
Step 5, according to
Figure BDA0003091532800000039
A channel matrix H can be calculated1。
Step 6, calculating the channel matrix H from the following formula2Is estimated by least squares
Figure BDA00030915328000000310
Step 7, repeating the steps 2 to 6 until
Figure BDA00030915328000000311
Wherein the cost function is
Figure BDA00030915328000000312
The error rate graphs of the GTC-LS algorithm, the TST algorithm and the ZF algorithm are shown in figure 2, the ZF receiver assumes that all channel matrixes are completely accommodated, and it can be seen that the GTC-LS algorithm does not need prior information and avoids the use of training sequences, therefore, the GTC-LS algorithm can obtain more accurate information, the convergence speed is higher, and the validity of the GTC-LS algorithm is verified through identifiability conditions and simulation results.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

Claims (1)

1. An unmanned aerial vehicle information monitoring method based on generalized tensor compression comprises the following steps:
step 1, performing random initialization on a signal matrix and a channel matrix;
step 2, calculating least square method estimation of a signal matrix and a coding matrix Khatri-Rao product;
step 3, calculating a signal matrix by using the inverse operation of the Khatri-Rao product;
step 4, solving a channel matrix from the user to the base station by using generalized expansion of a tensor of a signal received by the unmanned aerial vehicle and Khatri-Rao product inverse operation;
step 5, calculating least square estimation of a channel matrix from the base station to the unmanned aerial vehicle;
and 6, repeating the steps until the convergence condition is met.
CN202110597040.9A 2021-05-31 2021-05-31 Unmanned aerial vehicle information monitoring method based on generalized tensor compression Pending CN113381797A (en)

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