System and method for global navigation satellite system (GNSS) spoofing detection
A global navigation satellite system (GNSS) spoofing detection and classification technique is provided. An optimization problem is formulated at the baseband correlator domain by using an optimization algorithm such as the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, for example. A model of correlator tap outputs of the intended received signal is created to form a dictionary of pre-computed waveform functions (e.g., triangle-like-shaped functions). Sparse signal processing can be leveraged to choose a decomposition of pre-computed waveform functions from the dictionary. The optimal solution of this minimization problem can discriminate the presence of a potential spoofing attack peak by observing the decomposition of two different code-phase values (authentic and spoofed) in a sparse vector output. A threshold can be used to mitigate false alarms. Furthermore, a variation of the minimization problem can be provided that enhances the dictionary to a higher resolution.
1. A system for detecting a spoofing attack on a Global Navigation Satellite System (GNSS) receiver, the system comprising:
a global navigation satellite system (GNSS) receiver;
a memory device, the memory device having a dictionary of pre-computed discrete-time waveforms stored therein, the pre-computed discrete-time waveforms comprises a plurality of discrete-time triangle-like-shaped functions; and
a processor configured to perform a spoofing detection algorithm that uses the dictionary of pre-computed discrete-time waveform functions to process an output signal component of a correlator bank of the GNSS receiver to identify a sparse combination of components of the pre-computed discrete-time waveforms in the output signal component of the correlator bank, the identified sparse combination of components corresponding to at least an intended received GNSS signal and a spoofing signal contained in the output signal component of the correlator bank, the sparse combination being identified by:
performing a sparse optimization algorithm that selects components of a sparse vector of the dictionary for one of the plurality of discrete-time triangular-like-shaped functions that best matches a code-phase of the output signal component of the correlator bank.
2. The system of claim 1 , wherein the output signal of the correlator bank comprises a discrete-time triangle-like-shaped function or a combination of discrete-time triangle-like-shaped functions.
3. The system of claim 1 , wherein the sparse optimization algorithm comprises a Least Absolute Shrinkage and Selection Operator (LASSO) algorithm.
4. The system of claim 3 , wherein the selected components of the sparse vector include in-phase and quadrature components.
5. The system of claim 4 , wherein the sparse optimization algorithm comprises a multi-LASSO algorithm.
6. The system of claim 2 , wherein the spoofing detection algorithm identifies at least first and second peaks of first and second discrete-time triangle-like-shaped functions, respectively, corresponding to the intended received GNSS signal and the spoofing signal, respectively, and wherein the spoofing detection algorithm includes a thresholding algorithm that applies a threshold value to magnitudes corresponding to the first and second peaks to reduce a possibility of false detection of the spoofing signal.
7. The system of claim 2 , wherein the selected components of the sparse vector correspond at least to the intended received GNSS signal, the spoofing signal and a multipath (MP) signal, and wherein the spoofing detection algorithm distinguishes between the intended received GNSS signal, the spoofing signal and the MP signal.
8. The system of claim 2 , wherein the sparse optimization algorithm finds an optimal selection according to an approximation error of a cost function to select the components of the sparse vector of the dictionary of pre-computed discrete-time triangular-like-shaped functions.
9. The system of claim 8 , wherein the dictionary comprises sets of pre-computed triangle-like-shaped functions, each set corresponding to an expected triangle-like-shaped waveform of the output signal component of the correlator bank that are discrete-time shifted relative to the expected triangle-like-shaped waveform of the output signal component by predetermined discrete-time shift amounts.
10. A method for detecting a spoofing attack on a Global Navigation Satellite System (GNSS) receiver, the method comprising:
performing, via a processor of a GNSS receiver, a spoofing detection algorithm that:
receives an output signal component of a correlator bank of the GNSS receiver; and
uses a dictionary of pre-computed discrete-time waveform functions to process the output signal component of the correlator bank to identify a sparse combination of components of the pre-computed discrete-time waveforms in the output signal component of the correlator bank, the identified sparse combination of components corresponding to at least an intended received GNSS signal and a spoofing signal contained in the output signal component of the correlator bank, the dictionary of pre-computed discrete-time waveforms being stored in a memory device of the GNSS receiver and read out by the processor, the pre-computed discrete-time waveforms comprising a plurality of discrete-time triangle-like-shaped functions, the sparse combination being identified by:
performing a sparse optimization algorithm that selects components of a sparse vector of the dictionary for one of the plurality of discrete-time triangular-like-shaped functions that best matches a code-phase of the output signal component of the correlator bank.
11. The method of claim 10 , wherein the output signal component of the correlator bank comprises a discrete-time triangle-like-shaped function or a combination of discrete-time triangle-like-shaped functions.
12. The method of claim 10 , wherein the sparse optimization algorithm comprises a Least Absolute Shrinkage and Selection Operator (LASSO) algorithm.
13. The method of claim 12 , wherein the selected components of the sparse vector include in-phase and quadrature components.
14. The method of claim 13 , wherein the sparse optimization algorithm comprises a multi-LASSO algorithm.
15. The method of claim 11 , wherein the spoofing detection algorithm identifies at least first and second peaks of first and second discrete-time triangle-like-shaped functions, respectively, corresponding to the intended received GNSS signal and the spoofing signal, respectively, and wherein the spoofing detection algorithm includes a thresholding algorithm that applies a threshold value to magnitudes corresponding to the first and second peaks to reduce a possibility of false detection of the spoofing signal.
16. The method of claim 11 , wherein the selected components of the sparse vector correspond at least to the intended received GNSS signal, the spoofing signal and a multipath (MP) signal, and wherein the spoofing detection algorithm distinguishes between the intended received GNSS signal, the spoofing signal and the MP signal.
17. The method of claim 11 , wherein the sparse optimization algorithm finds an optimal selection according to an approximation error of a cost function to select the components of the sparse vector of the dictionary of pre-computed discrete-time triangular-like-shaped functions.
18. The method of claim 17 , wherein the dictionary comprises sets of pre-computed triangle-like-shaped functions, each set corresponding to an expected triangle-like-shaped waveform of the output signal component of the correlator bank that are discrete-time shifted relative to the expected triangle-like-shaped waveform of the output signal component by predetermined discrete-time shift amounts.
19. A computer program embodied on a computer-readable medium, the program comprising computer code for execution by a processor to detect a spoofing attack on a Global Navigation Satellite System (GNSS) receiver, the computer program of the GNSS receiver comprising:
a first code portion that receives an output signal component of a correlator bank of the GNSS receiver; and
a second code portion that uses a dictionary of pre-computed discrete-time waveform functions to process the output signal component of the correlator bank to identify a sparse combination of components of the pre-computed discrete-time waveforms in the output signal component of the correlator bank, the identified sparse combination of components corresponding to at least an intended received GNSS signal and a spoofing signal contained in the output signal component of the correlator bank, the dictionary of pre-computed discrete-time waveforms being stored in a memory device of the GNSS receiver and read out by the processor, the pre-computed discrete-time waveforms comprising a plurality of discrete-time triangle-like-shaped functions, the sparse combination being identified by:
performing a sparse optimization algorithm that selects components of a sparse vector of the dictionary for one of the plurality of discrete-time triangular-like-shaped functions that best matches a code-phase of the output signal component of the correlator bank.