CN115061158A - Deception jamming detection method, device, terminal and medium based on altimeter - Google Patents

Abstract

The invention provides a deception jamming detection method, a deception jamming detection device, a terminal and a medium based on an altimeter, wherein the method comprises the following steps: acquiring detection parameters of a barometric altimeter and detection parameters of a GNSS (global navigation satellite system); based on an ARIMA model, respectively denoising the detection parameters of the barometric altimeter and the detection parameters of the GNSS to obtain a first altitude corresponding to the barometric altimeter and a second altitude corresponding to the GNSS; and comparing the first height with the second height to determine whether a deception signal exists. Compared with the prior art, the method has the advantages that detection parameters of the barometric altimeter and detection parameters of the GNSS are combined, deception signals and real signals are distinguished on altitude data, the generated deception signals and the forwarded deception interference are well detected, the detection stability is improved, and the real signals are effectively reserved.

Description

An altimeter-based spoofing jamming detection method, device, terminal and medium

technical field

The present invention relates to the field of spoofing signal detection, in particular to an altimeter-based spoofing interference detection method, device, terminal and medium.

Background technique

At present, the spoofing signal emitted by the spoofing jammer is very similar to the real signal, and it is easy to deceive the user's receiver, causing the user to obtain the wrong time and location. Because the spreading code of the spoofing jamming signal and the real signal is the same, RAIM is used. It is difficult for algorithms to filter out deceptive signals mixed with real signals. In the prior art, methods for detecting GNSS spoofing interference include a C/NO detection method, a signal arrival time detection method, and the like. The C/NO detection method detects the existence of spoofing signals by detecting abnormal changes in C/NO. But when the spoofing signal is transmitted together with noise, it is easy to cause misjudgment. Signal arrival time detection is aimed at the distance between the relayed spoofing jamming and the receiver, which is longer than the real signal, which forms a difference in time, so as to judge whether there is a spoofing signal, but this method has little effect on the generative spoofing signal. , even eliminating the real signal and keeping the spoofed signal.

SUMMARY OF THE INVENTION

The invention provides an altimeter-based spoofing jamming detection method, device, terminal and medium. Based on the detection parameters of the barometric altimeter and the height difference between the barometric altimeter and the GNSS, the detection accuracy is improved through comparative analysis.

In order to solve the above technical problems, an embodiment of the present invention provides an altimeter-based spoofing interference detection method, including:

Obtain barometric altimeter detection parameters and GNSS detection parameters;

Based on the ARIMA model, noise reduction is performed on the barometric altimeter detection parameters and the GNSS detection parameters, respectively, to obtain a first altitude corresponding to the barometric altimeter and a second altitude corresponding to the GNSS;

The first height and the second height are compared to determine whether there is a spoofing signal.

As a preferred solution, the ARIMA model is specifically the ARMIA(p,d,q) model:

Φ(L)Δ ^d x _t =δ+Θ(L)ε _t ;

Δ=1-L;

Among them, {x _t } is the time series, L is the backshift operator, {ε _t } is the white noise obeying the standard normal distribution, t=1,2,...,N,N is the length of the time series, p , d, and q are the orders of the ARIMA(p,d,q) model, and Φ(L), Δ, and Θ(L) are intermediate variables.

As a preferred solution, the first height and the second height are compared to determine whether there is a spoofing signal, specifically:

Calculate the Euclidean distance between the first height and the second height, compare the Euclidean distance with a preset threshold value, and determine that there is a spoofing signal when the Euclidean distance is greater than or equal to the preset threshold value ; when the Euclidean distance is less than the preset threshold value, it is determined that there is no spoofing signal.

As a preferred solution, before the acquisition of the barometric altimeter detection parameters and the GNSS detection parameters, the method further includes:

Acquire barometric altimeter measurement data and GNSS measurement data, perform differential processing on the barometric altimeter measurement data and GNSS measurement data, and obtain the barometric altimeter detection parameters and the GNSS detection parameters.

Correspondingly, an embodiment of the present invention also provides an altimeter-based spoofing interference detection device, including a parameter acquisition module, a noise reduction module, and a comparison analysis module, wherein,

The parameter acquisition module is used for acquiring barometric altimeter detection parameters and GNSS detection parameters;

The noise reduction module is configured to perform noise reduction on the barometric altimeter detection parameters and the GNSS detection parameters based on the ARIMA model, respectively, to obtain a first altitude corresponding to the barometric altimeter and a second altitude corresponding to the GNSS ;

The comparison and analysis module is configured to perform comparison processing between the first height and the second height to determine whether there is a deception signal.

As a preferred solution, the ARIMA model is specifically the ARMIA(p,d,q) model:

Φ(L)Δ ^d x _t =δ+Θ(L)ε _t ;

Δ=1-L;

Among them, {x _t } is the time series, L is the backshift operator, {ε _t } is the white noise obeying the standard normal distribution, t=1,2,...,N,N is the length of the time series, p , d, and q are the orders of the ARIMA(p,d,q) model, and Φ(L), Δ, and Θ(L) are intermediate variables.

As a preferred solution, the comparison analysis module performs comparison processing on the first height and the second height to determine whether there is a spoofing signal, specifically:

The comparison analysis module calculates the Euclidean distance between the first height and the second height, and compares the Euclidean distance with a preset threshold value. When the Euclidean distance is greater than or equal to the preset threshold value , it is determined that there is a spoofing signal; when the Euclidean distance is less than the preset threshold value, it is determined that there is no spoofing signal.

As a preferred solution, the spoofing and jamming detection device further includes a preprocessing module, and the preprocessing module is configured to acquire barometric altimeter measurement data and GNSS measurement data before acquiring the barometric altimeter detection parameters and the GNSS detection parameters. The barometric altimeter measurement data and the GNSS measurement data are subjected to differential processing to obtain the barometric altimeter detection parameters and the GNSS detection parameters.

Correspondingly, an embodiment of the present invention further provides a terminal, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, when the processor executes the computer program The described altimeter-based spoofing jamming detection method is implemented.

An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, wherein when the computer program runs, the device where the computer-readable storage medium is located is controlled to execute the Altimeter-based spoofing jamming detection method.

Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

Embodiments of the present invention provide an altimeter-based spoofing jamming detection method, device, terminal and medium. The method includes: acquiring barometric altimeter detection parameters and GNSS detection parameters; Noise reduction is performed on the GNSS detection parameters to obtain a first altitude corresponding to the barometric altimeter and a second altitude corresponding to the GNSS; a comparison process is performed on the first altitude and the second altitude to determine whether There are spoofing signals. Compared with the prior art, the detection parameters of the barometric altimeter and the GNSS detection parameters are combined, and the spoofing signal and the real signal are distinguished on the altitude data, which has a good detection effect on the generated spoofing signal and the forwarding spoofing interference, The stability of detection is improved, while the true signal is effectively preserved.

Further, when the GNSS receives the spoofing signal, the second height will fluctuate greatly. By adding threshold detection, the effectiveness of the detection method can be improved.

Further, by differential processing the barometric altimeter measurement data and GNSS measurement data, and processing them into barometric altimeter detection parameters and GNSS detection parameters, the applicability of the data to the ARMIA(p,d,q) model can be increased, and better identification can be obtained. Effect.

Description of drawings

FIG. 1 is a schematic flowchart of an embodiment of an altimeter-based spoofing jamming detection method provided by the present invention.

FIG. 2 is a schematic structural diagram of an embodiment of an altimeter-based spoofing jamming detection device provided by the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Embodiment one:

Please refer to FIG. 1. FIG. 1 is an altimeter-based spoofing interference detection method provided by an embodiment of the present invention, including steps S1 to S3, wherein,

Step S1, acquiring barometric altimeter detection parameters and GNSS detection parameters.

In this embodiment, the measurement data of the barometric altimeter and the measurement data of the GNSS are first acquired. and preprocess the measurement data of the barometric altimeter and the measurement data of the GNSS, optionally:

Differential processing is performed on the measurement data of the barometric altimeter and the measurement data of the GNSS, respectively, until the data is stable, so as to obtain the detection parameters of the barometric altimeter and the detection parameters of the GNSS.

Step S2, based on the ARIMA model, noise reduction is performed on the barometric altimeter detection parameters and the GNSS detection parameters, respectively, to obtain a first altitude corresponding to the barometric altimeter and a second altitude corresponding to the GNSS.

In this embodiment, an error analysis is firstly performed on a carrier such as a mobile phone as a receiver, and a motion model of the carrier is constructed. Using the target tracking method, the carrier motion model is established in the vertical direction:

h _k =h _a +δ _k ;

为载体的运动加速度，可以看成是一个零均值的平稳一阶马尔科夫过程，

为载体的运动加速度的导数。Among them, h _k is the real height information of the carrier, _na is the white noise, τ _a is the first-order Markov correlation time, δ _k is the height measurement error, and _ha is the ideal motion height of the carrier,

is the motion acceleration of the carrier, which can be regarded as a stationary first-order Markov process with zero mean,

is the derivative of the motion acceleration of the carrier.

Second, construct the barometric altimeter error model. Generally, the vertical distance from the location of the carrier to the geoid is defined as the altitude. In the case of standardized pressure distribution and temperature distribution, the pressure altitude is equivalent to the altitude. The altitude measurement equation of the barometric altimeter can be expressed as:

H _k =h _a + _ns +γ _b +ζ _q ;

Among them, n _s is white noise, mainly the error caused by the change of motion state and system quantization noise.

ζ _q is the first-order Markov noise, which is mainly caused by the random drift caused by the error of the barometric pressure measurement algorithm, which satisfies:

Among them, τ _b is the correlation time of the first-order Markov noise, which is generally determined by the moving speed and moving range of the carrier, and n _q can be considered as the error caused by the system quantization effect, which is regarded as white noise. The smaller the correlation time, the closer the first-order Gauss-Markov process is to white noise performance (frequent changes), and the larger the correlation time, the closer to random walk performance (slow changes). The relevant time can be specifically set according to the specific situation, for example, the relevant time can be set according to the time scale of the zero offset under the typical motion condition of the carrier.

In addition, the altitude error measured by the barometric altimeter also includes a random constant value γ _b , which can be approximately regarded as a constant value when the altitude is lower than 50 km. In this embodiment, the random constant value is 0.

Then there is the GNSS error model. GPS/BDS uses the WGS-84/CGCS2000 geodetic coordinate system (agreement geodetic coordinate system), and the GPS satellite ephemeris is based on the WGS-84 coordinate system, so the coordinates of GPS single-point positioning and relative positioning are calculated The baseline vector of is in the WGS-84 geodetic coordinate system. The practical measurement results often belong to a national coordinate system or a local coordinate system (ie, a local reference coordinate system), so coordinate conversion must be performed.

The vertical distance between the position of the carrier and the reference ellipsoid is the height of the ellipsoid, that is, the geodetic height H _p , and the height difference from the geoid is the height difference Δh.

And since the ellipsoid normal is not consistent with the vertical line, ha ₊ Δh is not exactly equal to H _p . However, the difference between the two is very small, so this embodiment approximately considers H _p =h _k +Δh. Generally speaking, the default user position is the carrier position, and the carrier coordinate position obtained by calculation is converted to the altitude information in the user coordinate system. At this point, the GNSS altitude measurement equation can be simplified to:

H _w =h _k + _ns ;

Among them, _Hw is the approximate height of the GNSS, and _ns is the noise introduced by the measurement, which is regarded as white noise.

It can be seen from the above analysis that in the actual situation, the directly obtained barometric altimeter data and GNSS data will have noise, which will affect the final detection result. Therefore, the ARIMA model is used to denoise the signal.

For any generalized stationary random process, a certain order ARMA(n,m) model process can be used to describe it. Assuming a stationary, normal, zero-mean time series {x _t }, t=1,2,...,N, the corresponding ARMA(n,m) model is:

Among them, φ ₁ , φ ₂ , ..., φ _p are model autoregressive parameters, θ ₁ , θ ₂ , ..., θ ₁ are model moving average parameters, {ε _t } is white noise and obeys standard normal distribution. p,q is the order of the ARMA(p,q) model, and N is the length of the time series.

However, in practical problems, the considered time series x _t cannot satisfy the three conditions of stationarity, normality and zero mean at the same time. Hence the d-order differencing of {x _t }. Then convert the original sequence to ARIMA(p,d,q) model, the expression is:

Φ(L)Δ ^d x _t =δ+Θ(L)ε _t ;

Δ=1-L;

Among them, {x _t } is the time series, {ε _t } is the white noise obeying the standard normal distribution, t=1,2,...,N,N is the length of the time series, p, d, q are ARIMA( p, d, q) the order of the model, Φ(L), Δ and Θ(L) are intermediate variables, and L is the backshift operator, that is, Lx _t =x _t-1 .

The improved ARIMA model algorithm can effectively suppress the random noise in the GNSS satellite navigation system and barometric altimeter signals. Specifically, an ARMA model is established for the detection parameters of the barometric altimeter and the GNSS detection parameters, and the modeling process is used to determine the order of the model based on criteria such as AIC. Preferably, ARIMA(1,1,1) is the first-order difference between the GNSS satellite navigation system and the barometric altimeter. The applicable model of the altitude signal, and then obtain the first altitude corresponding to the barometric altimeter and the second altitude corresponding to the GNSS.

Step S3, compare the first height and the second height to determine whether there is a spoofing signal.

In this embodiment, the Euclidean distance between the first height and the second height is calculated, and the Euclidean distance is compared with a preset threshold value. When the Euclidean distance is greater than or equal to the preset threshold value , it is determined that there is a spoofing signal; when the Euclidean distance is less than the preset threshold value, it is determined that there is no spoofing signal.

According to common knowledge, for point X={x _i } and point Y={y _i }, the Euclidean distance algorithm is as follows:

其中，i＝1,2,...,n。

where i=1,2,...,n.

Substitute the first altitude and the second altitude into X and Y, respectively, to obtain the Euclidean distance between the barometric altimeter detection altitude and the GNSS detection altitude.

When the Euclidean distance between the barometric altimeter detection altitude and the GNSS detection altitude (that is, the Euclidean distance between the first altitude and the second altitude) is greater than or equal to the threshold value, it is determined that there is a spoofing signal; when the Euclidean distance is less than the specified Euclidean distance When the preset threshold value is exceeded, it is determined that there is no spoofing signal.

As an example of this embodiment, the following experiment can be designed to verify whether there is a spoofing signal in the satellite signal received by the receiver:

In the experiment, the real satellite signal is broadcast in the first half of the receiver's reception, and the spoofed satellite signal is added in the second half, and the altitude data changes are observed. In the first half of the experiment, it was basically confirmed that the height data fluctuated within a small range. When the spoofing signal is added, the GNSS satellite altitude data received by the receiver obviously jumps, while the altimeter data does not fluctuate greatly before and after the spoofing signal is added.

In the normal positioning stage, the actual altitude measured by the real satellite and the altitude measured by the barometric altimeter generally do not differ by more than 1 meter. After adding the spoofing signal, the satellite altitude data will fluctuate greatly, such as tens of meters, while the barometric altitude will fluctuate greatly. The data remains unchanged. Based on this, we can set the threshold to, 1.5 meters, 2 meters, 3 meters and so on. Furthermore, when the Euclidean distance between the altitude measured by the real satellite signal and the altitude measured by the barometric altimeter is smaller than the threshold value, it is considered that there is no spoofing signal at this time. When the Euclidean distance between the altitude measured by the real satellite signal and the altitude measured by the barometric altimeter is greater than the preset threshold value, it is considered that there is a spoofing signal.

Correspondingly, referring to FIG. 2 , this embodiment also provides an altimeter-based spoofing interference detection device, including a parameter acquisition module 101 , a noise reduction module 102 and a comparison analysis module 103 , wherein,

The parameter acquisition module 101 is used for acquiring barometric altimeter detection parameters and GNSS detection parameters;

The noise reduction module 102 is configured to perform noise reduction on the barometric altimeter detection parameters and the GNSS detection parameters based on the ARIMA model, respectively, to obtain a first altitude corresponding to the barometric altimeter and a second altitude corresponding to the GNSS. high;

The comparison analysis module 103 is configured to perform comparison processing between the first height and the second height to determine whether there is a spoofing signal.

Preferably, the ARIMA model is specifically the ARMIA(p,d,q) model:

Φ(L)Δ ^d x _t =δ+Θ(L)ε _t ;

Δ=1-L;

Among them, {x _t } is the time series, L is the backshift operator, {ε _t } is the white noise obeying the standard normal distribution, t=1,2,...,N,N is the length of the time series, p , d, and q are the orders of the ARIMA(p,d,q) model, and Φ(L), Δ, and Θ(L) are intermediate variables.

Exemplarily, the comparison analysis module 103 performs comparison processing on the first height and the second height to determine whether there is a spoofing signal, specifically:

The comparison analysis module 103 calculates the Euclidean distance between the first height and the second height, and compares the Euclidean distance with a preset threshold value. When the Euclidean distance is greater than or equal to the preset threshold value, it is determined that there is a spoofing signal; when the Euclidean distance is less than the preset threshold value, it is determined that there is no spoofing signal.

In this embodiment, the spoofing jamming detection device further includes a preprocessing module, and the preprocessing module is configured to acquire the barometric altimeter measurement data and the GNSS measurement data before the acquisition of the barometric altimeter detection parameters and the GNSS detection parameters. The barometric altimeter measurement data and the GNSS measurement data are subjected to differential processing to obtain the barometric altimeter detection parameters and the GNSS detection parameters.

Correspondingly, an embodiment of the present invention further provides a terminal, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, which is implemented when the processor executes the computer program The altimeter-based spoofing jamming detection method.

The processor may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf processors Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal, and uses various interfaces and lines to connect various parts of the entire terminal.

The memory can be used to store the computer program, and the processor implements various functions of the terminal by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a stored program area and a stored data area, wherein the stored program area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the storage data area may store Data (such as audio data, phonebook, etc.) created according to the usage of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, internal memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.

Correspondingly, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, wherein when the computer program runs, a device where the computer-readable storage medium is located is controlled The altimeter-based spoofing jamming detection method is performed.

Wherein, if the integrated module of the altimeter-based spoofing interference detection device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed by the processor, the steps of the foregoing method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, and the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

Embodiments of the present invention provide an altimeter-based spoofing jamming detection method, device, terminal and medium. The method includes: acquiring barometric altimeter detection parameters and GNSS detection parameters; Noise reduction is performed on the GNSS detection parameters to obtain a first altitude corresponding to the barometric altimeter and a second altitude corresponding to the GNSS; a comparison process is performed on the first altitude and the second altitude to determine whether There are spoofing signals. Compared with the prior art, the detection parameters of the barometric altimeter and the GNSS detection parameters are combined, and the spoofing signal and the real signal are distinguished on the altitude data, which has a good detection effect on the generated spoofing signal and the forwarding spoofing interference, The stability of detection is improved, while the true signal is effectively preserved.

Further, when the GNSS receives the spoofing signal, the second height will fluctuate greatly. By adding threshold detection, the effectiveness of the detection method can be improved.

Further, by differential processing the barometric altimeter measurement data and GNSS measurement data, and processing them into barometric altimeter detection parameters and GNSS detection parameters, the applicability of the data to the ARMIA(p,d,q) model can be increased, and better identification can be obtained. Effect.

The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above descriptions are only specific embodiments of the present invention, and are not intended to limit the protection scope of the present invention. . It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included within the protection scope of the present invention.

Claims (10)

1. An altimeter-based spoof-jamming detection method, comprising:

acquiring detection parameters of a barometric altimeter and detection parameters of a GNSS (global navigation satellite system);

based on an ARIMA model, respectively denoising the detection parameters of the barometric altimeter and the detection parameters of the GNSS to obtain a first altitude corresponding to the barometric altimeter and a second altitude corresponding to the GNSS;

and comparing the first height with the second height to determine whether a deception signal exists.

2. An altimeter-based deception jamming detection method according to claim 1, characterized in that said ARIMA model is specifically an ARIMA (p, d, q) model:

Φ(L)Δ ^d x _t ＝δ+Θ(L)ε _t ；

Δ＝1-L；

wherein, { x _t Is the time series, L is the backshifting operator, { ε _t White noise subject to a standard normal distribution, t 1,2, N being the time series length, p, d, q being the order of the ARIMA (p, d, q) model, Φ (L), Δ, and Θ (L) being intermediate variables.

3. The method as claimed in claim 1, wherein the comparing process is performed on the first altitude and the second altitude to determine whether a spoof signal exists, specifically:

calculating Euclidean distances of a first height and a second height, comparing the Euclidean distances with a preset threshold value, and determining that a deception signal exists when the Euclidean distances are larger than or equal to the preset threshold value; and when the Euclidean distance is smaller than the preset threshold value, determining that no deception signal exists.

4. The altimeter-based spoof-jamming detection method of any one of claims 1-3, further comprising, prior to said obtaining barometric altimeter sensed parameters and GNSS sensed parameters:

acquiring measurement data of a barometric altimeter and measurement data of a GNSS (global navigation satellite system), and performing difference processing on the measurement data of the barometric altimeter and the measurement data of the GNSS to obtain detection parameters of the barometric altimeter and the detection parameters of the GNSS.

5. A deception jamming detection device based on an altimeter is characterized by comprising a parameter acquisition module, a noise reduction module and a comparison analysis module, wherein,

the parameter acquisition module is used for acquiring detection parameters of the barometric altimeter and GNSS detection parameters;

the noise reduction module is used for respectively reducing noise of the detection parameters of the barometric altimeter and the detection parameters of the GNSS based on an ARIMA model to obtain a first height corresponding to the barometric altimeter and a second height corresponding to the GNSS;

the comparison analysis module is used for comparing the first height and the second height to determine whether a deception signal exists.

6. An altimeter-based spoof interference detecting device as recited in claim 5, wherein said ARIMA model is specifically ARMIA (p, d, q) model:

Φ(L)Δ ^d x _t ＝δ+Θ(L)ε _t ；

Δ＝1-L；

wherein, { x _t Is the time sequence, L is the backshifting operator, { ε _t White noise subject to a standard normal distribution, t 1,2, N being the time series length, p, d, q being the order of the ARIMA (p, d, q) model, Φ (L), Δ, and Θ (L) being intermediate variables.

7. The apparatus of claim 5, wherein the comparison analysis module compares the first altitude with the second altitude to determine whether a spoof signal exists, specifically:

the comparison analysis module calculates Euclidean distances of a first height and a second height, compares the Euclidean distances with a preset threshold value, and determines that a deception signal exists when the Euclidean distances are larger than or equal to the preset threshold value; and when the Euclidean distance is smaller than the preset threshold value, determining that no deception signal exists.

8. The altimeter-based spoof-interference detecting device of any one of claims 5-7, further comprising a preprocessing module, wherein said preprocessing module is configured to obtain the altimeter measurement data and the GNSS measurement data before said obtaining the altimeter detection parameters and the GNSS detection parameters, and to perform a difference processing on the altimeter measurement data and the GNSS measurement data to obtain the altimeter detection parameters and the GNSS detection parameters.

9. A terminal comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor when executing the computer program implementing the altimeter-based spoofed jamming detection method of any one of claims 1-4.

10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program when executed controls an apparatus in which the computer-readable storage medium is located to perform the altimeter-based spoof interference detection method of any one of claims 1 to 4.

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