GNSS receiver performance evaluation method and device based on joint loss and frequency weight

Abstract

The invention discloses a GNSS (Global Navigation Satellite System) receiver performance evaluation method and device based on joint loss and frequency weight. The method comprises the following steps of S1, giving a to-be-tested interference signal type set, a to-be-tested frequency set and a to-be-tested power value set; s2, measuring to obtain a reference frequency spectrum and a measurement frequency spectrum; s3, calculating the single-frequency cosine similarity between the measurement frequency spectrum and the interference-free reference frequency spectrum; a weighted similarity concept is introduced, and a power sensitive feature index fused with multi-frequency point information is given in combination with a frequency weight; s4, constructing a continuous index model to evaluate the performance of the GNSS receiver; s5, constructing a joint loss function; s6, dynamically updating the frequency weight to obtain a final frequency weight; and step S7, completing real-time GNSS receiver performance evaluation based on the final frequency weight. The method can automatically focus on a frequency band which is more sensitive to interference, not only ensures that the evaluation score is strictly monotonically reduced along with the power, but also realizes cross-modulation type failure threshold alignment.

Description

Technical Field

[0001] This invention relates to the field of GNSS signal reception, and in particular to a method and apparatus for evaluating the performance of GNSS receivers based on joint loss and frequency weight. Background Technology

[0002] Global Navigation Satellite Systems (GNSS), represented by the Global Positioning System (GPS), have become a core component of modern infrastructure, playing an irreplaceable role in critical areas such as aerospace navigation, autonomous driving, precision agriculture, and emergency response. However, the sensitivity of satellite navigation receivers to electromagnetic interference (EMI) poses a significant challenge to their performance. In particular, with the proliferation of wireless devices and increasing human-caused interference, GPS L1 band signals are threatened by various types of interference, including continuous wave (CW), pulse modulation, and BPSK modulation. These interferences can significantly affect receiver performance; therefore, evaluating the performance of GNSS receivers is crucial.

[0003] However, there are still some key issues with the response mechanism of the receiver's RF front end in complex electromagnetic environments. First, the traditional signal-to-interference-plus-noise ratio (SINR) evaluation framework is difficult to quantify the dynamic characteristics of the intermediate frequency output related to navigation faults. The spectrum threshold distribution patterns of different modulation types lack a universal theoretical framework, which limits the general design of anti-interference receivers. Furthermore, it is difficult to automatically focus on frequency bands that are more sensitive to interference, which is not conducive to improving the accuracy of receiver performance evaluation. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a GNSS receiver performance evaluation method and apparatus based on joint loss and frequency weight. This method achieves a quantitative description of the correlation between spectral characteristics and interference parameters and can automatically focus on frequency bands that are more sensitive to interference. It not only ensures that the evaluation score decreases strictly monotonically with power, but also achieves failure threshold alignment across modulation types, thereby significantly improving the robustness and physical consistency of the evaluation results.

[0005] The objective of this invention is achieved through the following technical solution: a GNSS receiver performance evaluation method based on joint loss and frequency weighting, comprising the following steps:

[0006] Step S1. Given the set of interference signal types, frequencies, and power values ​​to be tested;

[0007] Step S2. Generate the interference signal to be tested. When the interference generator is turned off and the interference signal to be tested is transmitted, the signal is received and the spectrum is measured by the GNSS receiver to obtain the reference spectrum and the measured spectrum.

[0008] Step S3. Normalize the interference spectrum and the measurement spectrum, and calculate the single-frequency cosine similarity between the measurement spectrum and the interference-free reference spectrum based on the normalization result; introduce the concept of weighted similarity, combine frequency weights, and give the power sensitive feature index that integrates multi-frequency information;

[0009] Step S4. Based on the power-sensitive feature index, construct a continuous exponential model to evaluate the performance of the GNSS receiver;

[0010] Step S5. Construct a joint loss function based on classification loss, monotonicity loss, and threshold alignment loss;

[0011] Step S6. Dynamically update the frequency weights, then return to step S4 for iterative processing until the joint loss function converges, and obtain the final frequency weights.

[0012] Step S7. Based on the final frequency weights, complete the real-time GNSS receiver performance evaluation.

[0013] A GNSS receiver performance evaluation device based on joint loss and frequency weighting includes:

[0014] The spectrum measurement module takes a set of interference signal types, frequencies, and power values ​​to be tested; generates the interference signal to be tested; and receives and measures the spectrum through a GNSS receiver when the interference generator is off and when the interference signal to be tested is being transmitted, to obtain the reference spectrum and the measured spectrum.

[0015] The power-sensitive feature index calculation module normalizes the interference spectrum and the measurement spectrum, and calculates the single-frequency cosine similarity between the measurement spectrum and the interference-free reference spectrum based on the normalization result; it introduces the concept of weighted similarity and combines it with frequency weights to give a power-sensitive feature index that integrates multi-frequency information.

[0016] The performance evaluation module uses a continuous exponential model based on power-sensitive feature indexing to evaluate the performance of GNSS receivers.

[0017] The loss construction module builds a joint loss function based on classification loss, monotonicity loss, and threshold alignment loss;

[0018] The frequency weight update module dynamically updates the frequency weights and then iterates until the joint loss function converges to obtain the final frequency weights, which are then used by the performance evaluation module for performance evaluation.

[0019] The beneficial effects of the present invention are: (1) The present invention uses the L2 norm to normalize the spectrum vector, eliminating the influence of amplitude difference on subsequent calculations; (2) In order to reflect the device's sensitivity to power, the present invention first introduces an exponential penalty term in the single-frequency cosine similarity to obtain the single-frequency weighted similarity. Then, in order to reduce the influence of frequency fluctuations, the average value of the single-frequency weighted similarity is calculated to obtain the final weighted similarity of the method power level. By using frequency weighting to integrate multi-frequency information, the power sensitive feature index is finally obtained. Then, a "feature index-power" model is constructed, and after fitting, it can complete the performance evaluation of GNSS receivers with arbitrary measured power; (3) It can automatically focus on the frequency band that is more sensitive to interference, which not only ensures that the evaluation score decreases strictly monotonically with power, but also realizes the failure threshold alignment across modulation types, thereby significantly improving the robustness and physical consistency of the evaluation results. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0021] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0022] like Figure 1 As shown, the GNSS receiver performance evaluation method based on joint loss and frequency weighting includes the following steps:

[0023] Step S1. Given the set of interference signal types, frequencies, and power values ​​to be tested;

[0024] Given a set of interference signal types to be tested, M= The frequency set is: The power value set is ;

[0025] Where, N m N f N P These represent the total number of types, frequencies, and power values ​​of the interference signals to be tested, respectively.

[0026] Step S2. Generate the interference signal to be tested. When the interference generator is turned off and the interference signal to be tested is transmitted, the signal is received and the spectrum is measured by the GNSS receiver to obtain the reference spectrum and the measured spectrum.

[0027] Given the interference signal type m∈M, interference signal power p∈P, and interference signal frequency f∈F, an interference signal to be tested is generated and transmitted via an interference generator. The GNSS receiver then receives the GNSS signal under the interference signal and performs spectrum measurements to obtain the measured spectrum. ;

[0028] When the interference generator is off, the GNSS receiver receives signals at multiple consecutive moments and performs spectrum measurements to obtain the spectrum vector under the "interference-free baseline," which is denoted as the reference spectrum. .

[0029] Step S3. Normalize the interference spectrum and the measurement spectrum, and calculate the single-frequency cosine similarity between the measurement spectrum and the interference-free reference spectrum based on the normalization result; introduce the concept of weighted similarity, combine frequency weights, and give the power sensitive feature index that integrates multi-frequency information;

[0030] S301. Measuring the spectrum using L2 norm pairs Reference spectrum After normalization, we get:

[0031] ;

[0032] in, Indicates the measurement spectrum The result obtained after normalization Indicates the reference spectrum The result obtained after normalization; Represents the L2 norm;

[0033] S302. Calculate the single-frequency cosine similarity between the measured spectrum and the interference-free reference spectrum based on the normalization processing results. :

[0034] ;

[0035] in, The inner product is expressed as follows: at frequency f, the single-frequency cosine similarity between the normalized measured spectrum and the reference spectrum is equivalent to their inner product.

[0036] S303. Introducing the concept of weighted similarity and combining it with frequency weights, a power-sensitive feature index that integrates multi-frequency information is given:

[0037] To reflect the device's sensitivity to power, an exponential penalty term is first introduced into the single-frequency cosine similarity to obtain the single-frequency weighted similarity. :

[0038] ;

[0039] make Represents the power penalty term, where The attenuation factor represents the factor that controls the attenuation rate.

[0040] When the power approaches the reference power p0, the penalty term approaches 1, and the weighted similarity approaches the single-frequency cosine similarity. As the power increases, the penalty term grows exponentially, and the weighted similarity decays exponentially. This indicates that for electromagnetically sensitive devices, once the power exceeds the threshold, the performance will drop sharply.

[0041] The power-sensitive feature index is obtained by integrating multi-frequency information using frequency weighting. :

[0042] ;

[0043] in, These are weighting coefficients, with values ​​ranging from [0,1], representing frequency weights. Initialize to , To measure the Euclidean distance difference between the measured spectrum and the reference spectrum under the conditions of interference signal type m, interference power p, and interference frequency f:

[0044] .

[0045] Step S4. Based on the power-sensitive feature index, construct a continuous exponential model to evaluate the performance of the GNSS receiver;

[0046] S401. First, when traversing the power set P for power p, repeat steps S1~S3 to obtain the corresponding values ​​for multiple power values. ;

[0047] S402. Constructing a continuous exponential model By utilizing different powers p and corresponding Scaling factor fitted by least squares method and attenuation coefficient ;

[0048] S403. For the measured interference signal power p, input it into the fitted continuous exponential model to obtain... Then it is compared with the interference sensitivity threshold. If it exceeds the threshold, the receiver is sensitive.

[0049] Step S5. Construct a joint loss function based on classification loss, monotonicity loss, and threshold alignment loss;

[0050] Step S5 includes:

[0051] Construct a joint loss function based on classification loss, monotonicity loss, and threshold alignment loss.

[0052] ;

[0053] in, , , These represent the classification loss, monotonicity loss, and threshold alignment loss, respectively. The weighting coefficients for the monotonicity loss;

[0054] Among them, classification loss for:

[0055] ;

[0056] The score is the GNSS performance evaluation result in step S4. When score=1, it means that the evaluation result is that the GNSS receiver is normal. When score=0, it means that the evaluation result is that the GNSS receiver is sensitive / faulty. The label is the tag, which is obtained by actual measurement of the GNSS receiver. When score=1, it means that the actual measurement result is that the GNSS receiver is normal. When score=0, it means that the actual measurement result is that the GNSS receiver is sensitive / faulty.

[0057] When traversing the sets of interference signal types, frequencies, and power values ​​respectively, the interference signal type m, interference frequency f, and interference power p are combined according to the formula in the classification loss based on the GNSS performance evaluation results and the measured results, thereby completing the classification loss calculation.

[0058] Monotonicity loss for:

[0059]

[0060] In the formula, hard constraints , represents the expected decrease in similarity for every 1 dBm increase in power; sim(i) represents the power sensitivity feature index calculated at the i-th power point;

[0061] Threshold alignment loss for:

[0062]

[0063] in, Similarity at key power points , The similarity between the power points to the left and right of the key point power point in the power value set; The sensitivity threshold is defined as the critical power point, where the critical power point refers to the critical interference power point that the GNSS receiver is sensitive to.

[0064] Step S6. Dynamically update the frequency weights, then return to step S4 for iterative processing until the joint loss function converges, and obtain the final frequency weights.

[0065] Step S6 includes:

[0066] S601. The dynamic update formula for the frequency weight is given:

[0067] ;

[0068] in, This represents the weight of frequency f in the t-th iteration. This represents the weight of the frequency f at the (t+1)th iteration; before the iteration begins, i.e., at t=0, Initialize the value to the initial value. ; For single-step updates, each loss is relative to w. f Gradient determination:

[0069]

[0070] Where ηt is the learning rate;

[0071] S602. In each iteration, based on the current frequency weights, after executing steps S1 to S4, the joint loss function is calculated by step S5, and the frequency weights are updated according to step S601 and used for the next iteration; until the joint loss function converges, the final frequency weights are obtained; the convergence of the joint loss function means that the value of the joint loss function is less than a preset threshold.

[0072] S602. In each iteration, based on the current frequency weights, after executing steps S1 to S4, the joint loss function is calculated by step S5, and the frequency weights are updated according to step S601 and used for the next iteration; until the joint loss function converges, the final frequency weights are obtained; the convergence of the joint loss function means that the value of the joint loss function is less than a preset threshold.

[0073] Step S7. Based on the final frequency weights, complete the real-time GNSS receiver performance evaluation, that is, based on the final frequency weights, execute steps S1 to S4.

[0074] A GNSS receiver performance evaluation device based on joint loss and frequency weighting includes:

[0075] The spectrum measurement module takes a set of interference signal types, frequencies, and power values ​​to be tested; generates the interference signal to be tested; and receives and measures the spectrum through a GNSS receiver when the interference generator is off and when the interference signal to be tested is being transmitted, to obtain the reference spectrum and the measured spectrum.

[0076] The power-sensitive feature index calculation module normalizes the interference spectrum and the measurement spectrum, and calculates the single-frequency cosine similarity between the measurement spectrum and the interference-free reference spectrum based on the normalization result; it introduces the concept of weighted similarity and combines it with frequency weights to give a power-sensitive feature index that integrates multi-frequency information.

[0077] The performance evaluation module uses a continuous exponential model based on power-sensitive feature indexing to evaluate the performance of GNSS receivers.

[0078] The loss construction module builds a joint loss function based on classification loss, monotonicity loss, and threshold alignment loss;

[0079] The frequency weight update module dynamically updates the frequency weights and then iterates until the joint loss function converges to obtain the final frequency weights, which are then used by the performance evaluation module for performance evaluation.

[0080] The foregoing description illustrates and describes a preferred embodiment of the present invention. However, as previously stated, it should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the inventive concept described herein through the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A GNSS receiver performance evaluation method based on joint loss and frequency weighting, characterized in that: Includes the following steps: Step S1. Given the set of interference signal types, frequencies, and power values ​​to be tested; Step S2. Generate the interference signal to be tested. When the interference generator is turned off and the interference signal to be tested is transmitted, the signal is received and the spectrum is measured by the GNSS receiver to obtain the reference spectrum and the measured spectrum. Step S3. Normalize the interference spectrum and the measurement spectrum, and calculate the single-frequency cosine similarity between the measurement spectrum and the interference-free reference spectrum based on the normalization result; introduce the concept of weighted similarity, combine frequency weights, and give the power sensitive feature index that integrates multi-frequency information; Step S4. Based on the power-sensitive feature index, construct a continuous exponential model to evaluate the performance of the GNSS receiver; Step S5. Construct a joint loss function based on classification loss, monotonicity loss, and threshold alignment loss; Step S6. Dynamically update the frequency weights, then return to step S4 for iterative processing until the joint loss function converges, and obtain the final frequency weights. Step S7. Based on the final frequency weights, complete the real-time GNSS receiver performance evaluation.

2. The GNSS receiver performance evaluation method based on joint loss and frequency weight according to claim 1, characterized in that: Step S1 includes: Given a set of interference signal types to be tested, M= The frequency set is: The power value set is In the set of frequency values, each frequency increases sequentially from front to back with the same frequency increment; in the set of power values, each power increases sequentially from front to back with the same power increment. Where, N m N f N P These represent the total number of types, frequencies, and power values ​​of the interference signals to be tested, respectively.

3. The GNSS receiver performance evaluation method based on joint loss and frequency weight according to claim 1, characterized in that: Step S2 includes: Given the interference signal type m∈M, interference signal power p∈P, and interference signal frequency f∈F, an interference signal to be tested is generated and transmitted via an interference generator. The GNSS receiver then receives the GNSS signal under the interference signal and performs spectrum measurements to obtain the measured spectrum. ; When the interference generator is off, the GNSS receiver receives signals at multiple consecutive time points and performs spectrum measurements to obtain the spectrum vector under the "interference-free baseline," which is denoted as the reference spectrum. .

4. The GNSS receiver performance evaluation method based on joint loss and frequency weight according to claim 1, characterized in that: Step S3 includes: S301. Measuring the spectrum using L2 norm pairs Reference spectrum After normalization, we get: ； in, Indicates the measurement spectrum The result obtained after normalization Indicates the reference spectrum The result obtained after normalization; Represents the L2 norm; S302. Calculate the single-frequency cosine similarity between the measured spectrum and the interference-free reference spectrum based on the normalization processing results. : ； in, The inner product is expressed as follows: at frequency f, the single-frequency cosine similarity between the normalized measured spectrum and the reference spectrum is equivalent to their inner product. S303. Introducing the concept of weighted similarity and combining it with frequency weights, a power-sensitive feature index that integrates multi-frequency information is given: To reflect the device's sensitivity to power, an exponential penalty term is first introduced into the single-frequency cosine similarity to obtain the single-frequency weighted similarity. : ； make Represents the power penalty term, where The attenuation factor represents the factor that controls the attenuation rate. When the power approaches the reference power p0, the penalty term approaches 1, and the weighted similarity approaches the single-frequency cosine similarity. As the power increases, the penalty term grows exponentially, and the weighted similarity decays exponentially. This indicates that for electromagnetically sensitive devices, once the power exceeds the threshold, the performance will drop sharply. The power-sensitive feature index is obtained by integrating multi-frequency information using frequency weighting. : ； in, These are weighting coefficients, with values ​​ranging from [0,1], representing frequency weights. Initialize to , To measure the Euclidean distance difference between the measured spectrum and the reference spectrum under the conditions of interference signal type m, interference power p, and interference frequency f: 。 5. The GNSS receiver performance evaluation method based on joint loss and frequency weight according to claim 1, characterized in that: Step S4 includes: S401. First, when traversing the power set P for power p, repeat steps S1~S3 to obtain the corresponding values ​​for multiple power values. ; S402. Constructing a continuous exponential model By utilizing different powers p and corresponding Scaling factor fitted by least squares method and attenuation coefficient ; S403. For the measured interference signal power p, input it into the fitted continuous exponential model to obtain... Then it is compared with the interference sensitivity threshold. If it exceeds the threshold, the receiver is sensitive.

6. The GNSS receiver performance evaluation method based on joint loss and frequency weight according to claim 1, characterized in that: Step S5 includes: Construct a joint loss function based on classification loss, monotonicity loss, and threshold alignment loss. ； in, , , These represent the classification loss, monotonicity loss, and threshold alignment loss, respectively. The weighting coefficients for the monotonicity loss; Among them, classification loss for: ； The score is the GNSS performance evaluation result in step S4. When score=1, it means that the evaluation result is that the GNSS receiver is normal. When score=0, it means that the evaluation result is that the GNSS receiver is sensitive / faulty. The label is the tag, which is obtained by actual measurement of the GNSS receiver. When score=1, it means that the actual measurement result is that the GNSS receiver is normal. When score=0, it means that the actual measurement result is that the GNSS receiver is sensitive / faulty. When traversing the sets of interference signal types, frequencies, and power values ​​respectively, the interference signal type m, interference frequency f, and interference power p are combined according to the formula in the classification loss based on the GNSS performance evaluation results and the measured results, thereby completing the classification loss calculation. Monotonicity loss for: In the formula, hard constraints , represents the expected decrease in similarity for every 1 dBm increase in power; sim(i) represents the power sensitivity feature index calculated at the i-th power point; Threshold alignment loss for: in, Similarity at key power points , The similarity between the power points to the left and right of the key point power point in the power value set; The sensitivity threshold is defined as the critical power point, where the critical power point refers to the critical interference power point that the GNSS receiver is sensitive to.

7. The GNSS receiver performance evaluation method based on joint loss and frequency weight according to claim 1, characterized in that: Step S6 includes: S601. The dynamic update formula for the frequency weight is given: ； in, This represents the weight of frequency f in the t-th iteration. This represents the weight of the frequency f at the (t+1)th iteration; before the iteration begins, i.e., at t=0, Initialize the value to the initial value. ; For single-step updates, each loss is relative to w. f Gradient determination: Where ηt is the learning rate; S602. In each iteration, based on the current frequency weights, after executing steps S1 to S4, the joint loss function is calculated by step S5, and the frequency weights are updated according to step S601 and used for the next iteration; until the joint loss function converges, the final frequency weights are obtained; the convergence of the joint loss function means that the value of the joint loss function is less than a preset threshold.

8. The GNSS receiver performance evaluation method based on joint loss and frequency weight according to claim 1, characterized in that: Step S7 includes: based on the final frequency weight, executing steps S1 to S4 to complete the real-time GNSS receiver performance evaluation.

9. A GNSS receiver performance evaluation device based on joint loss and frequency weighting, employing the method described in any one of claims 1 to 7, characterized in that: include: The spectrum measurement module is given a set of interference signal types, frequencies, and power values ​​to be tested. An interference signal to be tested is generated. When the interference generator is turned off and the interference signal to be tested is transmitted, the signal is received and the spectrum is measured by a GNSS receiver to obtain the reference spectrum and the measured spectrum. The power-sensitive feature index calculation module normalizes the interference spectrum and the measurement spectrum, and calculates the single-frequency cosine similarity between the measurement spectrum and the interference-free reference spectrum based on the normalization result; it introduces the concept of weighted similarity and combines it with frequency weights to give a power-sensitive feature index that integrates multi-frequency information. The performance evaluation module uses a continuous exponential model based on power-sensitive feature indexing to evaluate the performance of GNSS receivers. The loss construction module builds a joint loss function based on classification loss, monotonicity loss, and threshold alignment loss; The frequency weight update module dynamically updates the frequency weights and then iterates until the joint loss function converges to obtain the final frequency weights, which are then used by the performance evaluation module for performance evaluation.

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