Since the received GNSS signals are inherently very weak, a simple GNSS signal jammer can easily degrade the measurement quality, or completely deny GNSS signal reception at the receiver. In the context of collaborative navigation, the user-to-user radio links help the cooperating receivers/nodes to navigate in the compromised environment. Possibility to share information between the nodes allows not only for improving positioning and situational awareness of the cooperating group of users, but also for pro-active localization of the identified jamming source. The focus of this paper is on localization of Radio Frequency Interference (RFI) by a group of collaborating users. A synthetic array principle based on the Carrier-to-Noise Density Ratio (C/N0) measurements is used and applied to a multi-user scenario. To evaluate the performance of multi-user RFI localization, a series of experiments in a controlled lab environment have been carried out. This paper discusses the results as well as the limitations of the implemented technique.
The signal strength of a GNSS signal, when it is received on the Earth, is merely -160 dBW
(or 0.1fW) [
To make a system resilient against GNSS signal jamming, we need to detect the source and
mitigate its efects on the target receiver. Jamming source localization capability is also
desirable in order to identify and prevent the disruption of system operations. Various techniques
that range from the use of complex antenna designs for steering a null towards the angle
of arrival of an undesired signal source from an airborne platform [
A broad overview of diferent available techniques for RFI localization is presented in [
Use of C/N0 measurements for jamming source localization is an attractive approach as
it does not require any complex equipment and can be implemented using Commercial Of
The Shelf (COTS) GNSS receivers. In this paper, we investigate the applicability of the
synthetic array based technique presented in [
The rest of the paper is organized as follows: Section 2 explains the RFI location technique
for a single receiver case that is based on the synthetic array principle, section 3 provides a brief
overview of the collaborative navigation along with certain considerations taken into account
for this work, Section 4 discusses the localization technique for multi-user case, Section 5 briefly
explains the experimental setup and scenario description, Section 6 presents and discusses the
simulation results whereas, Section 7 draws the conclusion and points out the limitations and
possible directions for the future work.
2. C/N0-based RFI Source Localization
A single receiver can localize the RFI source using the synthetic array principle that suggests
that a set of sensors located at diferent positions can be replaced by a single sensor moving
along a known trajectory [
In the presence of a jamming signal, the efect of interference on signal quality is best
where, Ci is the ith satellite’s received power, N0 is noise power spectral density, J is the
received jamming signal power and kα is the Spectral Separation Coeficient, which is a factor
that determines the degradation of actual C/N0 in presence of interference by determining the
overlapping between power spectral densities of the received GNSS and interference signals[
C/N0 can be reformulated as Ci ⃓⃓ N0 ⃓⃓ eff
= β i + 10α log10 (d)
where β i is obtained through receiver calibration, α is the path loss exponent and d is the
separation between the target receiver and the jammer. For some reference jammer power J0
observed at distance d0, β i can be calculated using the following expression
described by the efective C/N 0 parameter [
Ci ⃓⃓ where, the distance d[n] between the jammer located at unknown location (xj , yj ) and the receiver situated at (x[n], y[n]) can be given as d[n] = √︂
(x[n] − xj )2 + (y[n] − yj )2
The objective function or cost function can now be formally defined as shown in (8) by substituting (7) in (6) and rearranging the resulting equation to bring all the parameters to β i =
Ci ⃓⃓ N0 ⃓⃓ dB− Hz − 10 log10 k ︃(
J0 )︃ α N0 − 10α log10 (d0)
As suggested in [
Ca N0 =
1 Nsat
Nsat− 1 ∑︂ i = 0
Ci N0 where, Nsat is the number of satellites the receiver is tracking. Using (4), model defined in (2) can be updated for all the available satellites and rewritten as the right side of the equality sign. In this case, we attempt to employ the optimizer algorithm to calculate the point (xj , yj ) at which global minima is achieved.
N− 1 [︄ Ca[n] ⃓⃓ J (xj , yj ) = ∑︂ ⃓ n=0 N0 ⃓ eff
]︄2
− β −
where, N is the number of instants for which the jamming event is observed and this factor
reflects the size of synthetic array to be considered for localization purpose. N determines the
length of synthetic array in terms of number of receiver position points. An expanded form of
d[n] is not shown in (8) just for the sake of compactness but can be found in [
The notion of collaborative navigation system (CNS) is partially derived from the wireless
sensor networks (WSNs). But unlike WSNs, the nodes in CNS aim to assist each other in
navigating in the environment with reduced or compromised reception of GNSS signals. While a
single, or multiple nodes within the network might experience reduced GNSS signal availability,
collectively, the CNS may be able to receive enough GNSS signals augmented by inter-nodal
ranging measurements and other navigation sensor data. The network is often designed to work
in diferent configurations with node information processed either centrally or in a distributed
manner. The generic key components of a node (building block of a CNS) include multiple
sensors, signals and technologies, such as GNSS, WiFi, Inertial Measurement Units (IMUs),
magnetometers, barometers, Visual Based Navigation (VBN) sensors, acoustic or Ultra Wide
Band (UWB) signals, etc. [
To assist the nodes to navigate, the network functions by employing various information
sharing mechanisms among the units or nodes. In a typical collaborative navigation scenario,
the cooperating nodes exchanging range and other important information while having diferent
levels of the reception of the GNSS signals. Within a network, the nodes could share the
navigation states, uncertainty information, or the node to node range information (obtained
using for example UWB radios). To maintain temporal situational awareness about the nodes’
current states, they could store and forward the last detected or measured position along with
its uncertainty. The detailed explanation of these mechanisms and their associated advantages
and disadvantages are out the scope of this paper and could be found in [
Due to the capability of the nodes to exchange information, positioning of all the nodes in the network can still be accomplished even if GNSS signal availability/quality at one or multiple nodes is degraded. And since to successfully disrupt the operation of the overall network, the majority of cooperating nodes must be compromised that is often dificult for a jamming source to accomplish, the cooperating systems are much harder to attack.
This section briefly explains the extension of the C/N 0-based RFI localization concept to incorporate the desired notion of collaboration. In the collaborative paradigm, all the cooperative nodes take part in the localization process. Since, each node is following its own trajectory, it would have its distinct impact on the overall solution. The core concept behind collaborative RFI localization is same as that explained previously in section 2. Each of the cooperating nodes takes C/N0 measurements for all satellites in view at regular intervals and when the jamming signal is present determines the source location by solving for (8). By applying the synthetic array principle on each of the nodes, the capability of single node to determine jammer location is preserved. Such setup allows each of the nodes to take proactive measures even when the information exchange with other collaborating nodes is not possible. In the study presented herein, it is assumed that in addition to the information required for basic node positioning (i.e. inter-nodal range measurements, uncertainty, etc.), jammer coordinates estimated by each node can also be exchanged allowing for more accurate source localization.
The problem of determining jammers location by m nodes providing m node-jammer distance measurements can be formulated by defining a system of nonlinear equations that in its simplest form considered in this study would have two unknowns (for the 2-D cases) and m equations depending on the number of participating nodes. Such system of nonlinear equations is solved by the Least Squares method that requires an initial guess to start the execution. An issue with selecting initial guess far from correct solution is pointed in [12], which suggests that by doing so the optimization search could converge to local minima (as in correct case) leading to incorrect localization results.
In order to proceed with the localization of the RFI source within the cooperating network of users, the jamming signal first has to be detected. A number of diferent jamming detection techniques have been proposed both at precorrelation stage[13] and post-correlation stage[14]. As the focus of the study presented in this paper is on RFI source localization, detection is not considered further. For simplicity of implementation, prescient detection is assumed.
To evaluate the performance of the dynamic multi-node C/N0-based RFI localization, a set of experiments have been conducted in a protected lab environment. For the study presented in this paper, two similar experiments were conducted in which the RFI source transmits omnidirectionally the wideband noise in the first experiment and a chirp jamming signal in the second. This section describes the scenario for the RFI localization along with the implementation details. The procedures to calibrate the receiver and interference simulation is also discussed later in this section.
To simulate the operation of a collaborative navigation system, four identically equipped dynamic nodes were considered. Each of the nodes was moving along a predefined trajectory in a 2km × 2km region. For simplicity, it has been assumed that the nodes are operating in an area with good satellites visibility and no multipath or atmospheric efects. The simulated four nodes follow a rectilinear trajectory with varying dynamics, where each of the nodes (1,2,3 and 4) advances gradually in the operating environment with constant azimuths of 135o,315o,225o, and 45o respectively. Fig. 1 shows the velocity profile of individual nodes showing their behavior during the scenario.
The GNSS signals were generated by Spirent GSS8000 HW simulator and combined with an interfering signal generated by the Spirent MS3055 Interference Signal Generator. The combined signal was fed to the antenna input of the NovAtel OEM7 receiver. In the study presented herein, for implementation simplicity, only the GPS L1 C/A signal was considered. When running simulations, a number of received signal parameters including the C/N0 measurements were logged to a file by means of a command line utility provided by NovAtel. MATLAB was used for post-processing of the data.
The efects of wideband and chirp jamming on the average C/N 0 values that are calculated using (4) are illustrated in Fig.2. The variations in the C/N0 values indicate the presence of a jamming source in the environment. They also highlight the time periods in the scenario, where the jammer is turned ON/OFF. As it can be observed from the plots in Fig.2, there is a slight diference between the level of degradation caused by the chirp jammer when compared to the wideband noise efect. This is due to the inherent characteristics of continuous frequency variations of the chirp signals. The maximum average C/N0 degradation is observed at the minimum distance of each node from the simulated jammer location. For the simulation scenario discussed in this paper, the jamming signal power levels have been selected high enough to cause substantial C/N0 degradation, but not complete loss of PVT capability.
To demonstrate the efect of RFI source and collect the data the algorithm, the jamming signal is injected for about 180 seconds during the scenario run. The duration of whole scenario is roughly 480 seconds (8 minutes), and the nodes follow the trajectories according to the assigned profiles. The jammer is turned ON at time t = 120 sec and turned OFF at t = 300 sec. In the simulation scenario presented in this paper, the power of the jammer is adjusted to avoid the loss of lock situations for most of the participating nodes. The jamming power of the wideband jamming signal is set to -55dBm. For the chirp case, the power level is set to -40dBm, while other design parameters are given in Table 1. It is reminded here that the frequency values are chosen to simulate an attack on GPS L1 signals only(fL1 = 1575.42 MHz).
As stated earlier, in order to apply the synthetic phase array principle, it is necessary to
calibrate the receiver performance in presence of jamming to determine the β parameter. As
discussed in [
This section includes the discussion of the localization results both for the single user case discussed in Section 2 and multi-user case introduced in Section 4. Performance results of these approaches are quantified by calculating the Root Mean Square Error (RMSE) of the estimated jammer location. Results for Geometric Dilution of Precision (GDOP) are also presented to illustrate the impact of the node geometry on the solution.
To illustrate the results obtained in the single user/node case, solutions obtained for Nodes 1 and 3 will be used in this section. It is noted that as similar results were obtained for Nodes 2 and 4, they are not included in this paper to avoid repetition. An objective function of the form shown in (8) is defined using the averaged version of C/N 0 measurements observed by each node, β value obtained after calibrating the receiver and the node’s coordinates as measured during the simulation. Localization has been performed for diferent number of epochs N (N=2,10 and 20) that in other words represents diferent lengths of the synthetic array. The samples are collected at the receiver’s sampling rate 1 Hz, which means, for example, N = 2 corresponds to 2 samples collected in 2 seconds. A solver based method is used to perform minimization without considering any constraints. The focus is on observing the results obtained by each of the nodes in a scenario attempting to localize the jammer independently. The jammer localization results for Nodes 1 and 3 in terms of the absolute error calculated as the diference between the known/simulated and estimated jammer position along East and North, as well as the obtained horizontal RMSE are shown in Tables 2 and 3.
As it can be observed from the results in Table 2, errors in the solution obtained by Node 1 are much larger compared to Node 3. This can be explained by Node 1 being further away from the jamming source during the simulation, and therefore much less afected. Due to limited observability results obtained for Node 1 are much less accurate. While, the increase in estimation accuracy reflects the benefit of using more epochs to evaluate the cost function given in (8) using a longer period of time might not always be practical in dynamic scenarios.
This section discusses the results of collaborative localization when the nodes following the trajectories as illustrated in Section 5.1. Table 6 summarizes the results of the multi-user scenarios presenting the minimum, maximum and RMSE values for the horizontal position estimates. For fair comparison, same number of epochs have been considered as for single user scenario to get the estimates prior to combining the results. In comparison with the localization results of individual nodes discussed in previous subsection, considerable improvement in jammer location estimation is achieved, however, due to an increased RMSE considering the dynamic range, the less number of measurements does not seem to fit well in our model.
One of the factors impacting the accuracy of the estimated jammer location is the uncertainty in the position of the nodes. The diference between the simulated true and measured positions in terms of RMSE is shown in Fig.3. As mentioned earlier, good satellite visibility and no additional error sources have been simulated in this scenario. The only factors afecting the uncertainty in the position of the nodes are the node dynamics, receiver noise and the injected jamming signal.
Fig.4 shows the RMSE of the estimated jamming source location and GDOP result for the segment of the scenario when the jamming signal is injected considering the wideband noise jammer case. The error variance tends to increase when more measurements are included to localize the jammer.
It is also evident from the figure, node geometry has a significant impact on the result. The maximum RMSE of the jammer solution for wideband noise is at the instant when the nodes are not evenly distributed around the jammer clustering on one side in a nearly co-linear arrangement. The method to determine GDOP for various geometrical formations in 2-D cases is as described in [15]. Similar results were obtained for the chirp signal case, therefore they are not presented here to avoid repetition. Fig.5 illustrates best and worst case geometry examples for the simulated scenarios. As we can see in the case of the best GDOP, the nodes have good visibility volume of the jammer, which allows the nodes to accurately estimate the jammer location. In worst geometry case, the jammer is not even present inside the polygon formed by the nodes resulting in the maximum error in the jammer localization solution.
This paper extends the synthetic array based jammer localization concept [
As indicated in [