The authors of the paper you supplied give two reasons (as I see it) for not using gyroscopes. The first is that the gyroscopes are subject to a maximum angular velocity. The second is the power consumption. In regards to both, only you will be able to determine whether or not these two short comings will affect you. That being stated, those two issues will not be a problem in most applications. Assuming that they don't affect your application, then I recommend using an IMU that comes with gyro measurements in addition to the linear acceleration (and if possible magnetometer for more accurate heading).
After the hardware consideration has been sorted out, you need to determine whether or not the noise from your measurements is Gaussian (normally distributed) or not. If so, you should consider, as the other answers have suggested, a Kalman Filter (Use original for linear data, Unscented or Extended for non-linear). If your noise does not follow a normal distribution, then you will need to look into non-parametric approaches such as Particle Filters.
The next issue to consider once that is done, is the numerical integration. This is a very tough problem and unfortunately, there is no true solution. Numerical integration is not just prone to error, but essentially guarantees that the result will not be exact, even when dealing with non-noisy data. When you try performing numerical integration on noisy data, the error that you will see in your system explodes. As a result, we can only hope to reduce our error as much as possible to delay the onset of debilitating drift.
For your numerical integration technique, I suggest switching from using the trap rule to using Simpson's Rule. You typically get more accurate results with Simpson's and so your error from integration won't be quite as bad.
As a side note, an idea that I think is really cool, but didn't seem to really take off anywhere is the use of neural networks in numerical integration (Numerical integration based on a neural network algorithm). I would suggest sticking with Simpson's rule, but if you have the time, you may want to try following what the authors present.