GPS World, March 2011

INNOVATION Signal Processing 040 Center of mass 3563 dB Hz B 15 20 25 30 35 40 45 50 020 015 010 005 0 C N 0 dB Hz Normalized bin count Center of mass 2248 dB Hz B 15 20 25 30 35 40 45 50 020 015 010 005 0 Normalized bin count C N 0 dB Hz FIGURE 1 Example histograms top a real world urban canyon the San Francisco financial district bottom 3GPP TS 34171 coarsetime assistance test case ther Reading Here no modeling or approximation is involved the receiver or recording instrument is physically operated within the signal environment of interest and its performance in that environment is observed directly The ever All measurements of this type are inherently literal the results of a given test are inseparably linked to the spe gathered In this respect the direct approach resembles the synthetic methods outlined above little randomness exists within the test setup to fully explore a given receivers performance space Designing a practical alternative to the existing GNSS tests particularly one intended to be easy to standardize represents a challenging balancing act If a proposed test is too simple it can be easily standardized but it may fall well short of capturing the complexities of real world signals On the other hand a test laden with many special corner cases data storage or non standard hardware may yield realistic results for a wide variety of signal conditions but it may also With those constraints in mind this article attempts to bridge the gap between the two approaches described above It describes a novel method for generating synthetic scenarios in which the distribution of signal levels closely approximates that observed in real world data sets but with an expand testing coverage through Monte Carlo methods Also the test setup requires only modest data storage and is easily implemented on existing widely deployed hardware making it attractive as a potential candidate for standardization The approach consists of several steps First signal data is gathered in an environment of interest and used to generate a histogram of carrier to noise density C N 0 ratios as reported by a reference receiver paying particular attention to satellite masking to ensure that the probability of signal blockage is calculated accurately The histogram is then combined with a randomized timing model to create a synthetic scenario for a conventional GNSS simulator whose output is fed into the receiver s under test RUTs The performance of the RUTs in response to live and simu usefulness of the histogram based simulation This hybrid ability full control and compactness with those of live testing realistic non static distribution of signal levels while avoiding many of the drawbacks of each Histograms The method explored in this article relies on cumulative histograms of C N 0 values reported by a receiver in a homogeneous signal environment This representation is compact and easy to implement with existing simulator based test setups and provides information that can be particularly useful in tuning acquisition algorithms Motivation and Theoretical Considerations To motivate the proposed approach consider an example histogram constructed from real world data gathered in an environment urban canyon where A GPS would typically be required This is shown in FIGURE 1 together with a representative histogram of a standard coarse time assistance test case as described in the 3GPP Technical Standard 34171 Section 521 for comparison Note that the x axis is actually discontinuous toward the left side of each plot the B column designates blocked signals and thus corresponds to C N 0 From the standpoint of signal distributions it is evident that existing test standards may not always model the real world very accurately The histogram is useful in other ways as well Since the data set is normalized the sum of all bin heights is 10 it GPS World March 2011 www gpsworld com 44