Improving RTKLIB solution: Fix-and-Hold

Integer ambiguity resolution is done by RTKLIB after calculating a float solution to the carrier phase ambiguities to attempt to take advantage of the fact that the carrier phase ambiguities must be integers. Constraining the solution to integers improves both the accuracy and convergence time.

RTKLIB provides three options for the integer ambiguity resolution: instantaneous, continuous, and fix-and-hold. “Instantaneous” is the most conservative and relies on recalculating the phase bias estimates every epoch. “Continuous” is the default option and uses the kalman filter to estimate the phase biases continuously over many epochs. In general, this provides much less noisy estimates of the phase biases and is essential for working with low cost receivers. The downside of the continuous solution is that if bad data is allowed to get into the filter outputs it can remain for multiple epochs and corrupt subsequent solutions, so some care must be taken to avoid that.

Fix-and-Hold” takes the concept of feeding information derived from the current epoch forward to subsequent epochs one step farther. In the “Continuous” method, only the float solution is used to update the phase bias states in the kalman filter. In the “Fix-and-Hold” method, an additional kalman filter update is done using pseudo-measurements derived from the fixed solution. This is only done if the fixed solution is determined to be valid. All of this is explained in greater detail in Appendix E.7(5) of the RTKLIB user manual.

In general, taking advantage of all the information we have this epoch to improve the solution for the following epochs seems to be a good idea, especially given the high levels of noise in the low-cost receivers and antennas. Even more so than in the “Continuous” method, however, we need to be careful about feeding erroneous data into the kalman filter which may take a long time to work its way out.

Testing the “Fix-and-Hold” method on a few data sets shows the results we would expect, the solution usually does improve but can occasionally cause it to have difficulty recovering from a period of bad data where we have corrupted the phase-bias estimates.

To minimize the chance of this happening, we need to do a better job of validating the solution before feeding it forward. Looking at a couple examples of bad data, what I find is that the errors are introduced when solutions are derived from a very small number of valid satellites. The current integer ambiguity validation is based only on the ratio of residuals between the best solution and the second best solution and does not take into account the number of satellites used to find the solutions.

I have introduced two new input parameters, “minfixsats”, and “minholdsats” into the code. These set the number of satellites required to declare a fixed solution as valid, and the number of satellites required to implement the fix-and-hold feature. I have found setting the minimum number of satellites for fix to 4 and fix-and-hold to 5 seems to work quite well for my data, but I have made them input parameters in the config file to allow them to be set to any value.

I won’t include all the code here, but it is available on GitHub at on the demo1 branch

The two key changes are:

For minfix, in resamb_LAMBDA() from:

if ((nb=ddmat(rtk,D))<=0) {
no valid double-difference\n”);


if ((nb=ddmat(rtk,D))<(rtk->opt.minfixsats-1)) { // nb is sat pairs
    errmsg(rtk,”not enough valid double-differences\n”);

and for minhold, in holdamb() from:

if (nv>0) {
    for (i=0;i<nv;i++) R[i+i*nv]=VAR_HOLDAMB;

    /* update states with constraints */
    if ((info=filter(rtk->x,rtk->P,H,v,R,rtk->nx,nv))) {
    errmsg(rtk,”filter error (info=%d)\n”,info);


if (nv>=rtk->opt.minholdsats-1) { // nv=sat pairs, so subtract 1
    for (i=0;i<nv;i++) R[i+i*nv]=VAR_HOLDAMB;

    /* update states with constraints */
    if ((info=filter(rtk->x,rtk->P,H,v,R,rtk->nx,nv))) {
    errmsg(rtk,”filter error (info=%d)\n”,info);

Again, the changes to the position solution are subtle and difficult to see in a plot, but are much more obvious in the Ratio Factor for AR validation. A higher ratio represents greater confidence in the solution. The green is before the changes, the blue is after. The RTKLIB code limits the AR ratio to a maximum of 1000.


As you can see, there is a very significant increase in the AR ratio with the change.  The improvement is coming from switching the ambiguity resolution from continuous to fix-and-hold, not from the code changes which we made to try and reduce the occasional occurence of corrupting the filter states with bad data.

Let’s also look at the metrics described earlier for both this change and the bug fix described in the previous post. On the x-axis, 1-3 are from before the two previous changes, 4 is with the bias sum bug fix and 5 is with fix-and-hold and the min sat thresholds for fix and hold.


From the metric plots you can see that at this point we have maxed out the % fixed solutions and median AR ratio metrics and have got the distance metric down below 0.5 cm so I will stop here and declare success, at least for this data set.

If you’d like to try this code with your data, the code for all the changes I have made so far are available at on the demo1 branch.

If anyone does try this code on their own data sets please let me know how it goes. I’m hoping these changes are fairly universally useful, at least for open skies and low velocities, but the only way to know is to test it on multiple data sets.

Update 4/17/16:  Enabling the “fix-and-hold” feature on other data sets has had mixed success.  I still see “fix-and-hold” locking to bad fixes and corrupting the states.  It looks like the extra checks I added are not sufficient to prevent this.  So, for know,I recommend turning off this feature, or if you do use it, be aware that it can cause problems.  I hope to take another look at this soon.


Improving RTKLIB solution: Phase-bias sum error

While working through the code to add comments as described in the last post, I stumbled across what looks like another bug. This one is more subtle than the previous bug (arlockcnt) and only has a small effect on the results, but still I thought it was worth discussing.

During each epoch, RTKLIB estimates the phase-bias for each satellite using the differences between the raw carrier-phase measurements and the raw pseudorange measurements. In a perfect system, these should always differ by an integer number of cycles because the carrier-phase measurement have an uncertainty in the number of cycles whereas the pseudorange measurements do not. These phase-bias estimates are then used to update the kalman filter.

Before updating the kalman filter however, RTKLIB calculates the common component between all satellites and subtracts this off of each phase-bias states (the kalman filter outputs) My guess is that this code is left over from the GPS-only days where all satellites operated at the same frequency since the estimates are all in units of cycles. As long as the frequencies of each satellite are identical, it is fine to add the cycles from one satellite to another, but this doesn’t work anymore once the satellite cycles are of different frequencies.

My code modification simply converts the biases to meters first using the wavelength for each satellite before summing them and then converting back to cycles for each satellite. The changes all occur in the udbias() routine. The following lines of code:

1) bias[i]=cp-pr/lami;

2) offset+=bias[i]-rtk->x[IB(sat[i],f,&rtk->opt)];

3) if (rtk->x[IB(i,f,&rtk->opt)]!=0.0) rtk->x[IB(i,f,&rtk->opt)]+=offset/j;


1) bias[i]=cp*lami-pr;

2) lami=nav->lam[sat[i]-1][f];

3)   if (rtk->x[IB(i,f,&rtk->opt)]!=0.0) {

This improves the solution slightly but is most obvious when the receiver does a clock update. Since the receivers use inexpensive crystal clocks, unlike the satellites which use atomic clocks, there is a continuous drift between the two. At some point, when the difference between these two clocks gets too large, the receiver will update its clock to remove the error. Most of the time the error introduced by this bug is too small to easily pick out of the noise, but when there is a large adjustment from the clock correction it becomes quite obvious and shows up as a large spike in the residuals. Adding this code change eliminates the spikes from the residuals.

While exploring this issue, I modified the code that outputs to the state file to also output the phase-biases since I felt they provided a fair bit of insight to what was happening in the solution. What I found however when looking at these phase-bias outputs is that they are dominated by this common term (presumably some sort of clock bias) and it is difficult to see the behavior of the individual phase biases. To avoid this problem I made another modification to the code. Instead of adding the common component to every phase-bias state, I saved it in a separate common bias variable and used this when initializing phase-biases for new satellites. Since all position calculations are done with the difference of the phase-biases and not the phase-biases themselves, this change does not have any effect on the output solution. It does however remove the large common variation from the phase-bias states and makes them easier to analyze.

Here are the residuals before and after the code modification (zoomed way out to see the spike).


The position solution doesn’t change much, the improvement is more obvious in the confidence of the solution which can be seen in the Ratio Factor for AR validation. Here it is before (green) and after (blue) the code modification.


As you can see there is a small but persistent increase in the AR ratio with this change.

Anyone else run across this issue and solved it in a different way? Or believe this is not a bug, and is actually the intended behavior? Or possibly know of other cases where cycles of different wavelengths are not handled properly in RTKLIB?

RTKLIB: Code comments + new Github repository

If you’ve spent any time perusing the RTKLIB source code you will surely be aware that it is very much what I would call “old-school” code. Comments are sparse and variable names tend to be short and not always very descriptive. The upside is that the code is quite compact, significant pieces of it can be seen on a single page which can sometimes be quite nice. The downside is it can be difficult to follow if you have not been poring over it for a long time. To help myself understand the code I have added a fair bit of commenting to most of the code used to calculate the carrier-phase solution, which is really the heart of the RTKLIB kinematic solution.  This is mostly in the rtkpos.c file.

The changes are too extensive to spell out here so I have forked the RTKLIB code in Github and posted my commented code there. If you are interested, you can download it from the demo1 branch at  

All the previous code changes are also there as well as a couple I have not yet posted about.  The receiver data I have been using for these posts is also there in the rtklib/data/demo1 folder.  The solution file updates for VS2010 c++ are in the rtklib/app/rnx2rtkp/msc folder and the modified executable is in the rtklib/bin folder.

Define metrics for evaluating the quality of a solution

So far, the improvements in the quality of the solution with each change have been quite obvious in the resulting plots from RTKLIB. Going forward though, the improvements are going to be more subtle and it will be more difficult to evaluate the changes.

Let us define some metrics we can use to help evaluate these future changes.

There are many metrics we could come up with, but I will start with these:

1) Stdev of xy distances between base and rover: This is a standard deviation of the distance between receivers. The distance between receivers should remain constant regardless of the change in orientation as discussed in a previous post, so ideally the standard deviation should be zero.

2) Median AR (ambiguity ratio) between best solution and 2nd best solution: This is a measure of the confidence of the solution coming from the integer ambiguity resolution algorithm. In general higher is better, but there is a trade-off with number of satellites used. A lower AR with a large number of satellites may be preferable to a high AR with only a small number of satellites

3) Ratio of epochs with fixed solution to total epochs: A measure of how often the result of the integer ambiguity resolution algorithm exceeds the threshold for using a fixed solution. Higher is better provided there are no significant errors in any of the fixed solutions. For integrity, we rely on a fixed solution indicating a very high confidence in the solution.

4) Median number of satellites used in float solution: This is the total number of satellites used to determine position. A higher number of satellites gives us more confidence in the result provided, if all other metrics are equal. Including low quality satellites in the solution will increase this metric while decreasing other metrics.

5) Median number of satellites used in fixed solution: This is the number of satellites considered high enough quality to use for the fixed solution. Similar trade-offs apply as with the number of satellites used in the float solution.

6) Fraction of incorrect fixed solutions: Number of erroneous fixed solution epochs divided by total number of fixed solution epochs. Any fixed solutions with significant errors dramatically reduces our confidence in the answer, so this metric should always be zero or very close to zero. We need to define a threshold for this metric based on how much error we consider acceptable in the solution.

Some of these metrics will probably prove to be more valuable than others, but I will start by monitoring all of these until have a better understanding of which are most important. Some of these metrics will tend to move in opposite directions. For example, if the algorithm excludes all but the best quality satellites, the median AR and ratio of fixed epochs will increase because the quality of the data is better, but the number of satellites used will decrease. Increasing the number of satellites used will increase the confidence that the solution is correct but may reduce some of the metrics because of the inclusion of lower quality data.

All of the metrics except #5 can be derived from data output to the status file, which is generated when RTKLIB calculates a solution. Metric #5, the satellites used in the fixed solution, requires a code modification to make a small change to the format of the status data. The code currently calculates the solution status for each satellite separately, but at the end of each epoch, changes the solution status of all satellites to the status of the solution. In other words, if five of eight satellites are used to determine the fixed solution, but the fixed solution does not meet the AR threshold criteria, the solution status for all 8 satellites are set to float. The code modification comments out one line from the end of the relpos() function in rtkpos.c as shown below in green to retain the individual status for each satellite. This change does not affect the functionality of the code in any way since this these variables are not used again after they are changed.

for (i=0;i<MAXSAT;i++) for (j=0;j<nf;j++)  {
      // Don’t lose track of which sats were used to try and resolve the ambiguities
     // if (rtk->ssat[i].fix[j]==2&&stat!=SOLQ_FIX) rtk->ssat[i].fix[j]=1;
     if (rtk->ssat[i].slip[j]&1) rtk->ssat[i].slipc[j]++;

I will track these metrics as I make changes in input parameters and algorithm to quantify any improvement. To add further insight, I will track the three different conditions in my input data separately. Those are static, moving slowly with open skies, and moving quickly with partially obstructed skies.

Here is the plot of the metrics for the changes we have made so far in the three previous posts for the period when the receivers are moving.


Case 1 on the x-axis represents the baseline parameters, case 2 is after increasing eratio1 from 100 to 300, case 3 is after fixing the bug in lock count with arlockcnt=20, and case 4 is after increasing arlockcnt to 120.

The metrics in the first three plots are all moving in the desired direction. The most important, stdev of the error is reduced from over 15 cm to less than 0.5 cm indicating a significant improvement in accuracy. Increases in % fixed solutions and median AR ratio both indicate increased confidence in the solution. In the last plot, the large difference between number of satellites used for the float solution and number of satellites used for the fixed solution is because we have disabled integer ambiguity resolution for the GLONASS satellites. This suggests a large amount of information is not being used and represents opportunity for future improvement. The slight drop in number of satellites used for the fixed solution in cases 3 and 4 occur because we are not using position data from satellites that have lost lock until the kalman filter has had some time to reconverge.

Plots for the other two environments, (receivers stationary, and receivers moving quickly) show similar trends.

For all three cases after the first improvement, the last metric, fraction of incorrect fixed positions, is zero for the period when the receivers were moving, which is what we want to see. I defined an erroneous position as anytime an error in a fixed solution exceeded 5 cm. Note that for the period of time when the receivers were moving quickly, this value was non-zero and hence this setup probably would not be adequate for that environment.

I’m interested in what metrics other people are using to evaluate the quality of solutions? Please leave a comment if you have any thoughts or other metrics that might be more useful.

Improving RTKLIB solution: (AR lock count and elevation mask)

In the previous post, after a couple changes to the input parameters, we ended up with the following solution for the difference between two stationary, then moving Ublox receivers:

During the time when the receivers were stationary (17:25 to 17:45), we were able to hold a fixed solution (green in plot) continuously after the initial convergence, but started to revert to float solution (yellow in plot) fairly frequently once the receivers started moving.

Let’s look at the data a little closer to see if we can discern why we are reverting back to the float solution. If we switch to the “Nsat” view in RTKPLOT plotted below, we can see a couple of useful plots.


The top plot shows the number of satellites used for the solution and the bottom plot show the AR ratio factor. The AR ratio factor is the residuals ratio between the best solution and the second best solution while attempting to resolve the integer cycle ambiguities in the carrier-phase data. In general, the larger this number, the higher confidence we have in the fixed solution. If the AR ratio exceeds a set threshold, then the fixed solution is used, otherwise the float solution is used. I have left this threshold at its default value of 3.0.

Notice in the plot that each time we revert to the fixed solution, it happens when the number of valid satellites increases. At first this seems counter-intuitive, you would think that more information would improve the solution, not degrade it. What is happening though, is that the solution is not using the satellite data directly, but rather, the kalman filter estimate of the data. This estimate improves as the number of samples increases. This is also why the solution takes some time to converge at the beginning of the data. By using the data from the new satellite before the kalman filter has had time to converge, we are adding large errors to the solution, forcing us back to the float solution.

Fortunately, RTKLIB has a parameter in the input configuration file to address this issue. “Pos2-arlockcnt” sets the minimum lock count which has to be exceeded before using a satellite for integer ambiguity resolution. This value defaults to zero, but let’s change it to 20 and see what happens. While we are it, we will also change the related parameter “pos2-arelmask” which excludes satellites with elevations lower than this number from being used. We will change this from it’s default of 0 to 15 degrees.

Unfortunately, re-running the solution with these changes makes almost no difference as seen below. The jumps back to float solutions still occur immediately after a new satellite is included, and the additional satellites are being included at the same epochs. Something is clearly wrong.


Digging into the code a bit reveals the problem. First, the arlockcnt input parameter is copied to a variable called “rtk->opt.minlock” Then the relevant lines of code from rtkpos.c are:


if (rtk->ssat[i-k].lock[f]>0&&!(rtk->ssat[i-k].slip[f]&2)&&
     rtk->ssat[i-k].azel[1]>=rtk->opt.elmaskar) {
     rtk->ssat[i-k].fix[f]=2; /* fix */
else rtk->ssat[i-k].fix[f]=1;

So far, everything looks OK, but when we look at the definition for the structure member “lock” in rtklib.h we find:

unsigned int lock [NFREQ]; /* lock counter of phase */

This won’t work. We are assigning a negative number (-rtk->opt.minlock) to an unsigned variable, then checking if the unsigned variable is greater than zero, which by definition, will always be true. Hence, satellites are always used immediately, regardless of lock count.

We fix this bug by changing the definition of lock from unsigned to signed:

int lock [NFREQ]; /* lock counter of phase */

Rerunning after rebuilding the code, gives us the following solution:

Much better! All but a couple of the jumps back to float solutions have been eliminated.

Looking at the plot of the AR ratio for this solution, we can see that the kalman filter requires more than 20 seconds (the value we chose for arlockcnt) to re-converge. Two minutes looks like a more reasonable value from the plot, so let’s try rerunning the solution with arlockcnt=120.

Even better.  That eliminates the last couple of jumps back to the float solution and also significantly reduces the number of jumps in the AR ratio plot as shown below. The green line in the bottom plot is arlockcnt=20 and the blue line is arlockcnt=120.


It’s possible we will need to revisit this number after looking at more data, but for now we will use 120 secs.

As always, please leave a comment if you have any comments, discussion points, or questions.

Update 4/11/16:  

  • In the above discussion, my data is one epoch/sec and I equate epochs with seconds.  Arlockcnt is specified in epochs, not seconds, so if your data is at a different sample rate you will need to adjust accordingly.
  • The suggestion above to set arlock to 120 secs is probably too conservative for more challenging environments.  If there are few satellites and/or many cycle slips for example it is probably better to start using a just-locked satellite before it is fully converged.  I have started using 30 secs rather than 120 secs for my solutions.
  • You can pull this code fix from my github repository either by itself from the arlockcnt branch or as part of a larger set in the demo1 branch

Improving RTKLIB solution: (Receiver Dynamics and Error Ratio)

In the previous posts, I demonstrated reasonably clean looking solutions using the default RTKLIB configuration options for rover data from a roving M8N receiver and base data from a stationary COREX base station.

I also showed a second solution for the slightly more challenging problem where roving M8N receivers are used for both the base and rover inputs. I mentioned that run was not done with the default settings but did not go into the details. Let us now go back and try that run using the default settings and see what happens. Remember what we hope to see is a circle with radius equal to the separation between the two receivers, ideally plotted in green, indicating RTKLIB was able to resolve the integer ambiguities and provide a fixed solution.

For reference, here is the solution we previously calculated with RNX2RTKP using non-default settings and code. The plot on the left is from the ground track option in the RTKPLOT menu which plots the x axis vs the y axis and the second plot is the position option which plots all 3 directions separately vs time.

Below is what we get running with the default settings with the two basic modifications I mentioned earlier. The red indicates the kinematic solution is unable to converge and the plotted result is done in single mode, which is not differential, and therefore is indicating absolute position, and not distance between the two receivers.

Clearly, the default settings are not adequate when both receivers are lower quality and both are moving. Let’s see if we can improve this.

First, let’s make a few small changes that won’t improve the solution but will make the input assumptions better match our problem.

Edit the input configuration file for rnx2rtkp to make the following changes. These are in addition to the two changes we made earlier (pos1-posmode and ant2-postype)

  1. pos1-frequency: L1+L2 → L1, both receivers are L1 only, so no need to look for L2 data
  2. pos1-navsys: 1 → 5, we have GLONASS data, so let’s use it
  3. pos2-gloarmode: on->off, not ready for this yet, in current state, it prevents fixed solution
  4. pos2-bdsarmode: on->off, no Bediou data so disable
  5. out-solformat: llh → enu, save solution to output file with equal units in x and y axis
  6. out-outstat: off → residual, save residuals for later analysis

Rerunning RNX2RTKP gives us a solution very similar to the previous one with little improvement, even with the additional data from the GLONASS satellites.  I won’t bother to plot it here.

Next, let’s turn on receiver dynamics with the “pos1-dynamics” input option. This adds nine states to the kalman filter, 3 for receiver position, 3 for velocity, and 3 for acceleration. This enables the solution to take into account the previous position of the receiver when calculating the new solution and allows us to better define likely changes in position, velocity, and acceleration. For now, we will use the default filter settings. With this change, the solution looks like this:

Not quite there yet, but much better. We can now see the expected circle but there are fairly large errors when the solution is not fixed. We do sometimes resolve the integer ambiguities but only 26.6% of the time.

Next, we’ll take a very brief look at the input parameters defining the error characteristics of the input data. For the kalman filter to effectively combine the data from multiple inputs, it requires information about the error distribution of each input.  RTKLIB calculates the variances of each code and carrier phase input based on a set of input parameters and a simple model based on satellite elevation, the assumption being that lower elevation satellites are noisier. For now, we will look at only the first input parameter. This is “stats-eratio1” and it sets the ratio of standard deviations of the pseudorange errors to the standard deviations of the carrier-phase errors. I believe it is not unreasonable to expect, with the lower quality receiver and antenna we are using, that the pseudorange errors will degrade more quickly than the carrier-phase errors. If this is true, then it would make sense to increase this ratio from the default of 100. Setting this to 300 give the following solution.

This looks a lot better. We converge much more quickly to a fixed solution and when it loses lock it also re-converges more quickly.

We still need to make a few more changes to get to the quality of the solution at the top of this post, but most of those changes require making some code changes so I will leave them to another post.

The physical justification for using 300 for the error ratio is a bit weak at this point but we’ll just go with it for now and I hope to come back and do a more complete analysis of the way RTKLIB generates the variances and covariances for input to the kalman filter later.

Anyone else come up with a good way to optimize the  RTKLIB input parameters for the kalman filter convariances?  If so, please leave a comment, I’m interested in how other people handle this problem.

Update 6/12/16:  Since writing this post, I have found that increasing eratio1 can have a tendency to increase the number of false fixes if “pos2-rejionno” is not also increased at the same time.  I now recommend that if you increase eratio1 that you also increase rejionno from 30 to 1000.  This should eliminate false rejection of outliers.  For more details see this post.


Building RTKLIB 2.4.3 code with Visual C++ 2010

One of my goals in this project is to make some changes to the RTKLIB solution algorithms and evaluate them specifically for my set of inputs. RTKLIB is intended to work in many different environments. I would like to customize it to be optimal for calculating differential solutions for two single frequency receivers assuming low velocities and open skies. Some of the changes I would like to make are based on what other people have already tried but for which code is not available. Other changes are based on looking at cases in my data where RTKLIB did not provide a good solution, understanding why, and changing the algorithm appropriately.

To make these changes, I need to be able to modify the RTKLIB code and rebuild it. Since I am doing this initial evaluation work on a Windows PC I will need to build the code in that environment. The GUI versions of the RTKLIB programs require the Embarcadero C++ compiler which I do not have and which is fairly expensive to purchase. The CUI versions can be built with Visual C++, which is available for free, so I have chosen to go that path. As mentioned before, I also find the CUI interface with a matlab wrapper better for tracking configuration information for multiple runs.

The project files for Visual C++ are in the rtklib\app\”app name”\msc folders. They are configured for Visual C++ 2008. Since I already have Visual C++ 2010 loaded on my machine I had to make a few changes to make the project files work. Some of the changes would also be required with VS 2008 since it does not look like the project files have been kept up to date with recent RTKLIB changes.

  1. Convert solution files to VS 2010: Click on msc.sln file in rtklib\app\rnx2rtkp\msc folder. This will bring up the visual studio conversion wizard which will convert the VC 2008 project files to VC 2010.
  2. Add new src files to project: Add tides.c and ppp_corr.c to the list of source files in the “Solution Explorer” window by right-clicking on the source folder and selecting “Add”. This prevents build errors from unfound symbols.
  3. Modify Include Directories: Select “Properties” from “Project” tab. Select “General” under “C/C++” under “Configuration Properties” in the menu on the left hand side. Replace the existing entry with “..\..\..\src” to make it independent of code path. This enables the compiler to find the rtklib.h file.
  4. Modify Target Name: Select “General” under “Configuration Properties” in the the menu on the left hand side and change “Target Name” to “rnx2rtkp” to avoid linking errors.
  5. Add winmm library: Select “Input” under “Linker” under “Configuration Properties” in the menu on the left hand side, and add “winmm.lib” to “Additional Dependencies”. This avoids linker errors for an unresolved TimeGetTime symbol.
  6. Fix LPCWSTR conversion warning: Select “General” under “Configuration Properties” in the the menu on the left hand side and change “Character Set” from “Use Unicode” to “Not Set”

To run these apps from the data folders you will need to add the paths for the executables to your Windows path variable. The executables are in app/”app name”/Release/”app name”.exe or app/”app name”/Debug/”app name”.exe depending on whether you built in Debug or Release mode. I always build in Debug mode and point my path variable to that folder just to make it easier to switch between debugging with VS and running stand-alone, but either will work.