Mobile jammer 4g | simple mobile jammer lammy

By Jenna R. Tong, Robert J. Watson, and Cathryn N. Mitchell, University of Bath Using signal-to-noise measurements from a single commercial-grade L1 GPS receiver, it is possible to detect interference or jamming that is above the thermal noise floor and below a power that causes loss of position. Interference, intentional or unintentional, is an acknowledged vulnerability of GPS systems. Many of the potential sources of interference are unintentional: interference can caused by harmonics of out-of-band signals, electronic noise, or malfunctioning equipment. The effect, however, is the same independent of intent. The presence of high-power interference which causes continual denial of service is fairly easy to detect, but lower power interference may still degrade performance, for example by causing loss of lock on some satellites, thus increasing position dilution of precision, although the receiver continues to output a position. Short periods of denial of service caused by intermittent high-power interference may not be immediately detected depending on the timing and ability of the system in use to deal with temporary loss of signal. Therefore, to fully characterize an antenna environment requires a 24/7 system, whether the purpose is to determine whether a location is suitable prior to installation, to identify whether problems at an existing site are due to interference, or to provide warnings of the presence of interference on a continuous basis. In particular, information on timing — for example finding a time of day or day of the week when interference is regularly seen — may assist in determining the source of the interference. This research forms part of the GNSS Availability Accuracy Reliability anD Integrity Assessment for timing and Navigation (GAARDIAN) project, which provides a mesh of sensors to monitor the integrity, reliability, continuity, and accuracy of the locally received GPS (or other GNSS) and eLoran signals continuously and to detect anomalous conditions such as local interference, differentiating between possible sources of errors such as interference, multipath, satellite errors, or space weather. Here we look at using the signal-to-noise ratio (SNR) values from a single-frequency GPS receiver to detect interference. There are two stages to the algorithm: determining the local environment of the antenna in terms of multipath and interference, and identifying and recording potential interference events. Since this method uses values output from a GPS receiver, characterizing the response to interference of the receiver used in the probe is necessary, to indicate what level interference can be detected with the system, as well as ensuring that false positives are not produced, and the effects of interference can be separated from those of multipath and scintillation, which can also cause decreases in SNR. We used a commercial, single-frequency receiver, recording this data from NMEA messags for analysis: SNR, in dB, reported as an integer elevation, in degrees, reported as an integer azimuth, in degrees, reported as an integer carrier lock time, in seconds. Algorithm. To determine the presence of interference, the normal state of the receiver must first be calculated. Initially it is assumed the receiver is fixed with an unchanging multipath environment. SNR and elevation values from all satellites are accumulated for several hours. To reduce influence of the unknown multipath environment, values from satellites below 10 degrees elevation and from those where the carrier lock time is less than four minutes are removed from the data set. A polynomial fit between elevation and SNR is then calculated from the remaining data. A second- or third-degree polynomial generally fits the high-elevation data with deviations from the profile at low elevations being primarily due to multipath where interference is not present. The standard deviation of SNR at each elevation is then calculated. The combination of the polynomial and these values of standard deviation characterize the normal environment of the receiver, for the case where interference is not present in the data gathered (Figure 1). Figure 1. Raw SNR data against elevation, for all satellites in view over a period of 12 hours (blue), and a polynomial fitting to the same data (green). To confirm that the threshold values returned by the first stage of the algorithm are valid, a value is calculated for the elevation where the SNR value drops below the polynomial curve by the greatest amount. If interference is not present, this is normally found at the point where multipath begins to influence the incoming signal and can be considered as a rough multipath cutoff, used to remove signals that may be influenced by multipath from later stages of the analysis. Assuming a well-sited antenna, a value greater than 25 degrees for this value indicates the possible presence of interference in the data used to calculate the polynomial. In cases where this value is high, the data in question would be rejected, and optionally a user may be warned that there may be pre-existing interference. If the antenna-receiver combination has been previously calibrated in a known good environment, it would be also possible to identify interference based on the difference in polynomial and standard deviation values between that environment and the location being tested. Figure 2 shows the value of this multipath cutoff (in degrees) for a set of data where interference was known to be present initially, against the start time for the data used to calculate the polynomial and multipath cutoff values, by number of hours from the start of the file. Once the mask is developed, a threshold value can be set to be n standard deviations below the polynomial, and events are detected by the combination of: At least four satellites with elevations above the multipath cutoff which are below the threshold value or which were above the multipath cutoff previous to losing lock. This status is continuous for more than a set time t. Requiring multiple satellites limits the effects of other influences on SNR such as multipath; requiring an extended time period removes very short-term fluctuations. The number of false positives and the power of interference required to cause an alarm then depends primarily on the value of the threshold factor n, and on the time period t, which here we kept at a constant of 30 seconds. Testing To avoid radiating interference, we constructed an RF network to facilitate injection of jamming signals into the GPS signal path. The GPS signal from a roof-mounted choke-ring antenna was passed through an amplifier and attenuator chain to provide 0 dB forward gain, but around 40 dB reverse isolation. An additional stepped attenuator (0–40 dB in 1 dB steps) was also included. The buffered signal from the antenna was then combined with the output of a vector signal generator used to provide the jamming signal. The combined signal was then fed into the GPS receiver via a DC-block to remove the antenna bias voltage. The signal generator is capable of producing a wide variety of jamming including matched spectrum wideband noise, CW, and pulsed signals. The adjustment of both the signal generator output power and the signal attenuator a llow the replication of a variety of signal-to-noise and jammer-to-noise scenarios. With the receiver locked onto a stable position, CW signals at L1 frequency were introduced into the receiver at levels from –125 dBm to –90 dBm in steps of 5 dBm, with at least 15 minutes of buffer time for the receiver to recover between each step (Table 1). Data was logged at 1 Hz throughout. We collected 20 hours of data, to calculate threshold values from data with no known interference. Table 1. Results Twelve hours of data from a period where no known interference was present was used to form the SNR mask, and events longer than 30 seconds were looked for using various values of n for the threshold across all 20 hours of data. A false alarm was considered to be any event where interference was detected while the signal generator was off. Table 2 summarizes the response for different threshold levels. Table 2. In this test, CW interference of –100 dBm was required before the number of satellites with carrier lock dropped below four even for a single epoch, and –90 dBm was required to cause a sustained loss of lock, but jamming of –105 dBm was still detectable by this system with no false positives returned. Decreasing the threshold began to produce false positives without detecting the smaller interference signals. This is not surprising as the thermal noise floor, assuming 2 MHz bandwidth, is about –110 dBm. In the raw data from the detected events, a sharp dip in SNR is often seen at the beginning of an event, followed by recovery as the receiver compensates. In this particular case, where the aim is to detect the interference, this could lead to interference going undetected if the initial sharp dip was underneath the time threshold (30 seconds) and the recovery took the SNR of some of the satellites above the SNR threshold (Figure 3). Figure 3. Value of polynomial mask (blue) and actual SNR (red) as recorded for four satellites during the period around the injection of the -100 dBm CW signal, showing initial dip and partial recovery. Conclusion Using only SNR values from a low-cost L1 GPS receiver, it is possible to detect CW interference which is above the thermal noise floor and below a power that causes loss of position. Different types of interference are expected to produce a different response, and unintentional interference is likely to be broadband or not directly centered on L1. The antenna used may also have a strong effect. These factors have not been examined here, although in practice the algorithm has run in multiple locations with different antennas, both direct and via splitters. Regardless of the precise type of interference, the system would be expected to detect any interfering signal which impacts the SNR of the receiver, and to do so even if the signal strength was below a level which caused denial of service in that area. The results are specific to the receiver used and its response to interference, although the algorithm would be capable of using data from any receiver that provided SNR values. Ideally the system used for measurement would have little or no built-in interference rejection. Although this data was collected and then examined after the fact for signs of interference, the system works in precisely the same way in real time. Further trials will test the algorithm’s performance in real time and with different jamming scenarios, and compare results from multiple receivers in a single location and the performance of the algorithm with different antennas. Acknowledgments This work was funded by the Engineering and Physical Sciences Research Council and the Technology Strategy Board. Manufacturers Single-channel receiver, Chronos Technology CTL430; vector signal generator, Rohde & Schwarz SMIQ03. Jenna R. Tong is a postdoctoral researcher in electronic and electrical engineering at the University of Bath. Her Ph.D. in electron tomography is from the University of Cambridge. Robert J. Watson received a Ph.D. degree in electronic engineering from the University of Essex, and is senior lecturer in electronic and electrical engineering at the University of Bath. Cathryn N. Mitchell is a professor of engineering at the University of Bath and the Director of Invert Centre for Imaging Science. She received a Ph.D. from the University of Wales Aberystwyth.