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Upgrading legacy designs based on analog filters

Analog filters have been around since the beginning of electronics, ranging from simple inductor-capacitor networks to more advanced active filters with op-amps. As such, there is a rich collection of tried and tested legacy filter designs for a broad range of sensor measurement applications. However, with the performance requirements of modern IoT (Internet of Things) sensor measurement applications and lower product costs, digital filters integrated into the microcontroller’s application code are becoming the norm, but how can we get the best of both worlds?

Rather than re-inventing the wheel, product designers can take an existing analog filter transfer function, transform it to digital (via a transform) and implement it as digital filter in a microcontroller or DSP (digital signal processor). Although  analog-to-digital transforms have been around for decades, the availability of DSP design tooling for tweaking the ‘transformed digital filter’ has been somewhat limited, hindering the design and validation process.

A 2nd order analog lowpass filter is shown below, and in its simplest form, only 5 components are required to build the filter, which sounds easy. Right?

The pros

The most obvious advantage is that analog filters have an excellent resolution, as there are no ‘number of bits’ to consider. Analog filters have good EMC (electromagnetic compatibility) properties as there is no clock generating noise. There are no effects of aliasing, which is certainly true for the simpler op-amps, which don’t have any fancy chopping or auto-calibration circuitry built into them, and analog designs can be cheap which is great for cost sensitive applications.

Sound great, but what’s the bad news?

Analog filters have several significant disadvantages that affect filter performance, such as component aging, temperature drift and component tolerance. Also, good performance requires good analog design skills and good PCB (printed circuit board) layout, which is hard to find in the contemporary skills market.

These disadvantages make digital filters much more attractive for modern applications, that require high repeatability of characteristics.  Looking at an example, let’s say that you want to manufacture 1000 measurement modules after optimising your filter design. With a digital solution you can be sure that the performance of your filter will be identical in all modules. This is certainly not the case with analog, as component tolerance, component aging and temperature drift mean that each module’s filter will have its own characteristics. Also, an analog filter’s frequency response remains fixed, i.e. a Butterworth filter will always be a Butterworth filter – any changes the frequency response would require physically changing components on the PCB – not ideal!

Digital filters are adaptive and flexible, we can design and implement a filter with any frequency response that we want, deploy it and then update the filter coefficients without changing anything on the PCB! It’s also easy to design digital filters with linear phase and at very low sampling frequencies – two things that are tricky with analog.

Laplace to discrete/digital transforms

The three methods discussed herein essentially involve transforming a Laplace (analog) transfer function, \(H(s)\) into a discrete transfer function, \(H(z)\) such that an tried and tested analog filter that is already used in a design may be implemented on a microcontroller or DSP.

A selection of some useful Laplace to z-transforms are given in table below:

\(
\begin{array}{ccc}\hline
H(s) &\longleftrightarrow & H(z) \\ \hline
1 &\longleftrightarrow & 1 \\
\frac{\displaystyle1}{\displaystyle s}
&\longleftrightarrow& \frac{\displaystyle 1}{\displaystyle 1-z^{\scriptstyle -1}}\\
\frac{\displaystyle 1}{\displaystyle s^{\scriptstyle 2}} &\longleftrightarrow& \frac{\displaystyle
Tz^{\scriptstyle-1}}{\displaystyle (1-z^{\scriptstyle -1})^2}\\
\frac{\displaystyle 1}{\displaystyle s+a}
&\longleftrightarrow&
\frac{\displaystyle 1}{\displaystyle 1-e^{-aT}z^{-1}}\\
\frac{\displaystyle 1}{\displaystyle (s+a)^2}
&\longleftrightarrow& \frac{\displaystyle z^{-1}(1-e^{-aT})}{\displaystyle a(1-z^{-1})(1-e^{-aT}z^{-1})}\\\hline
\end{array}
\)
A table of useful Laplace and z-transforms

The Bilinear z-transform (BZT)

The Bilinear z-transform (BZT), simply converts an analog transfer function, \(H(s)\) into a discrete transfer function, \(H(z)\) by replacing all \(s\) terms with the following:

\(\displaystyle
s=\frac{2}{T}\frac{1-z^{-1}}{1+z^{-1}} \label{bzt}\)

where, \(T\) is the discrete system’s sampling period. However, substituting \(s=j\Omega\) and \(z=e^{jwT}\) into the BZT equation and simplifying, notice that there is actually a non-linear relationship between the analog, \(\Omega\) and discrete, \(w\) frequencies. This relationship is shown below, and is due to the nonlinearity of the arctangent function.

\(\displaystyle
\omega=2\tan^{-1}\left(\frac{\Omega T}{2}\right)
\label{bzt_warp_def1}
\)

Analysing the equation, it can be seen that the equally spaced  analog frequencies in the range \(-\infty<\Omega<\infty\) are nonlinearly compressed in the frequency range \(-\pi<w<\pi\) in the discrete domain. This relationship is referred to as frequency warping, and may be compensated for by pre-warping the analog frequencies by:

\(\displaystyle
\Omega_c=\frac{2}{T}\tan\left(\frac{\Omega_d T}{2}\right)
\label{bzt_warp_def2}
\)

where, \(\displaystyle\Omega_c\) is the compensated or pre-warped analog frequency, and \(\displaystyle\Omega_d\) is the desired analog frequency.

The ASN FilterScript command \(\texttt{bilinear}\) may be used convert a Laplace transfer function into its discrete equivalent using the BZT transform. An example is given below.

The Impulse Invariant Transform

The second transform, is referred to as the impulse invariant transform (IIT), since the poles of the Laplace transfer function are converted into their discrete equivalents, such that the discrete impulse response, \(h(n)\) is identical to a regularly sampled representation of the analog impulse response (i.e., \(h(n)=h(nT)\), where \(T\) is the sampling rate, and \(t=nT\)). The IIT is a much more tedious transformation technique than the BZT, since the Laplace transfer function must be firstly expanded using partial fractions before applying the transform.

The transformation technique is defined below:

\(\displaystyle
\frac{K}{s+a} \quad\longrightarrow\quad
\frac{K}{1-e^{-aT}z^{-1}} \label{iit_def}
\)

This method suffers from several constraints, since it does not allow for the transformation of zeros or individual constant terms (once expanded), and must have a high sampling rate in order to overcome the effects of spectral aliasing. Indeed, the effects of aliasing hinder this method considerably, such that the method should only be used when the requirement is to match the analog transfer function’s impulse response, since the resulting discrete model may have a different magnitude and phase spectrum (frequency response) to that of the original analog system. Consequently, the impulse invariant method is unsuitable for modelling highpass filters, and is therefore limited to the modelling of lowpass or bandpass type filters.

Due to the aforementioned limitations of the IIT method, it is currently not supported in ASN Filterscript.

The Matched-z transformation

Another analog to discrete modelling technique is the matched-z transformation. As the name suggests, the transform converts the poles and zeros from the analog transfer function directly into poles and zeros in the z-plane. The transformation is described below, where \(T\) is the sampling rate.

\(\displaystyle
\frac{\prod\limits_{k=1}^q(s+b_k)}{\prod\limits_{k=1}^p(s+a_k)}
\quad\longrightarrow\quad
\frac{\prod\limits_{k=1}^q(1-e^{-b_kT}z^{-1})}{\prod\limits_{k=1}^p(1-e^{-a_kT}z^{-1})}
\label{matchedz_def}
\)

Analysing the transform equation, it can be seen that the transformed z-plane poles will be identical to the poles obtained with the impulse invariant method. However, notice that the positions of the zeros will be different, since the impulse invariant method cannot transform them.

The ASN Filterscript command \(\texttt{mztrans}\) is available for this method.

A detailed example

In order to demonstrate the ease of transforming analog filters into their discrete/digital equivalents using the analog to discrete transforms, an example of modelling with the BZT will now follow for a 2nd order lowpass analog filter.

A generalised 2nd order lowpass analog filter is given by:

\(\displaystyle
H(s)=\frac{w_c^2}{s^2+2\zeta w_c s + w_c^2}
\)

where, \(w_c=2\pi f_c\) is the cut-off frequency and \(\zeta\) sets the damping of the filter,  where a  \(\zeta=1/\sqrt{2}\) is said to be critically damped or equal to -3dB at \(w_c\). Many analog engineers choose to specify a quality factor, \(Q =  \displaystyle\frac{1}{2\zeta}\) or peaking factor for their designs. Substituting \(Q\) into \(H(s)\), we obtain:

\(\displaystyle
H(s)=\frac{w_c^2}{s^2+ \displaystyle{\frac{w_c}{Q}s} + w_c^2}
\)

Analysing, \(H(s)\) notice that \(Q=1/\sqrt{2} = 0.707\) also results in a critically damped response. Various values of \(Q\) are shown below, and as seen when \(Q>1/\sqrt{2}\) peaking occurs.

2nd order lowpass filter prototype magnitude spectrum for various value of Q:
notice that when \(Q>1/\sqrt{2}\) peaking occurs.

 

Before applying the BZT in ASN FilterScript, the analog transfer function must be specified in an analog filter object. The following code sets up an analog filter object for the 2nd order lowpass prototype considered herein:

Main()

wc=2*pi*fc;
Nb={0,0,wc^2};
Na={1,wc/Q,wc^2};

Ha=analogtf(Nb,Na,1,"symbolic"); // make analog filter object

The \(\texttt{symbolic}\) keyword generates a symbolic transfer function representation in the command window. For a sampling rate of \(f_s=500Hz\) and \(f_c=30Hz\) and \(Q=0.707\), we obtain:

ASN FilterScript analog command

Applying the BZT via the \(\texttt{bilinear}\) command,

 Hd=bilinear(Ha,"symbolic"); 

ASN FilterScript bilinear command

The complete frequency response of the transformed digital filter is shown below, where it can be seen that the at \(30Hz\) the magnitude is \(-3dB\) and the phase is \( -90^{\circ}\), which is as expected. Notice also how the filter’s magnitude roll-off  is affected by the double zero pair at Nyquist (see the z-plane chart below), leading to differences from its analog cousin. Pole-zero chart 2nd order LPF

2nd order LPF

The pole-zero positions may be tweaked within ASN Filterscript or via the ASN Filter Designer’s interactive pole-zero z-plane plot editor by just using the mouse!

Implementation

The complete code for transforming a generalised 2nd order analog  lowpass filter prototype into its digital equivalent using the BZT via ASN FilterScript is given below:


ClearH1;  // clear primary filter from cascade
interface Q = {0.1,10,0.02,0.707};
interface fc = {10,200,10,40};

Main()

wc=2*pi*fc;
Nb={0,0,wc^2};
Na={1,wc/Q,wc^2};

Ha=analogtf(Nb,Na,1,"symbolic"); // make analog filter object
Hd=bilinear(Ha,"symbolic"); // transform Ha via BZT into digital object, Hd

Num=getnum(Hd);
Den=getden(Hd);
Gain=getgain(Hd);

 

 

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Licencing information

 

The sensor measurement challenge

Sensors come in all type of shapes and forms…There are sensors for audio, pressure, temperature, weight, strain, light, humidity…the list is almost endless.

The challenge for most, is that many sensors used in these IoT measurement applications require filtering in order to improve the performance of the sensor’s measurement data in order to make it useful for analysis.

Before jumping into the disussion, let’s first have a look at what sensor data really is….

All sensors produce measurement data. These measurement data contain two types of components:

  • Wanted components, i.e. information what we want to know
  • Unwanted components, measurement noise, 50/60Hz powerline interference, glitches etc – what we don’t want to know

Unwanted components degrade system performance and need to be removed.

So, the challenge for every designer is first to identify what aspects of the data we want to keep, i.e. ‘the wanted components’ and what we need to filter out, the so called ‘unwanted components’. After establishing what need to be filtered out, the challenge then which domain do we tackle this problem in, i.e. the analog domain or in the digital domain ? Each domain has its pros and cons, as we will now discuss for a practical classic sensor measurement challenge using a loadcell.

Loadcell analog

A classic sensor measurement challenge using a loadcell is shown below.

Looking at the hardware setup, we see that have a loadcell excited by a DC excitation voltage, and the general idea is that the sensor’s differential bridge voltage is amplifier by the instrumentation amplifier (IA) when strain is applied.

For those of you unfamiliar with this type of technology, a loadcell is a strain measurement sensor that is comprised of 4 strain gauges, it’s also referred to as a Wheatstone bridge, hence the terminology bridge sensor.

Analysing the signals in the schematic, we see that the differential voltage is passed through 2 filters in order to remove powerline interference and reduce measurement noise.

What are the challenges?

The Instrumentation amplifier (IA) has high impedance inputs, which makes it easy to connect EMI (electromagnet interference) filters to the inputs. However, any mismatches with these filters will generally degrade the instrumentation amplifier’s common-mode rejection ratio, which is undesirable.

The instrumentation amplifier usually has a large gain (100 is quite typical), so any unwanted differential voltage on the inputs will be amplified. Looking at the filters, the notch depth of the powerline cancellation (50Hz/60Hz) filter will be dependent on component tolerances, and will vary over time and with temperature…This is problematic as we’ll discuss in the following section.

Finally, any analog filter or filters will require careful PCB layout and eat up precious board space, which is undesirable for many modern devices.

Loadcell digital – is digital any better ?

Replacing the instrumentation amplifier with a 24bit sigma-delta ADC (analog-to-digital converter), we simplify the circuitry – although many ADCs don’t tolerate high impedance at their inputs, which may be problematic for good RFI (radio frequency interference) filter design.

Nevertheless, some sigma-delta devices have an in-built 50/60Hz notch filter which simplifies the filtering requirement. Although these devices are more expensive, and the choice of sampling frequency is limited, they may be good enough for some applications.

ASP vs DSP

So, which domain is best for solving our measurement challenge, i.e. do we use analog signal processing (ASP) or digital signal processing (DSP)? In order to answer this objectively, we need to first breakdown the pros and cons of each domain.

Analog filters

Let’s first look at an implementation using ASP.

The most obvious advantage is that analog filters have excellent resolution, as there are no ‘number of bits’ to consider. Analog filters have good EMC properties as there is no clock generating noise. There are no effects of aliasing, which is certainly true for the simpler op-amps, which don’t have any fancy chopping or auto-calibration circuitry built into them, and analog designs can be cheap which is great for cost sensitive applications.

Sound great, but what’s the bad news?

Analog filters have several significant disadvantages that affect filter performance, such as component aging, temperature drift and component tolerance. Also, good performance requires good analog design skills and good PCB layout, which is hard to find in the contemporary skills market.

One big minus point is that filter’s frequency response remains fixed, i.e. a Butterworth filter will always be a Butterworth filter – any changes the frequency response would require physically changing components on the PCB – not ideal!

Digital filters

Let’s now look at an implementation using DSP.

The first impression is that a digital solution is more complicated, as seen above with the five building blocks. However, digital filters have high repeatability of characteristics, and as an example, let’s say that you want to manufacture 1000 measurement modules after optimising your filter design. With a digital solution you can be sure that the performance of your filter will be identical in all modules. This is certainly not the case with analog, as component tolerance, component aging and temperature drift mean that each module’s filter will have its own characteristics.

Digital filters are adaptive and flexible, we can design and implement a filter with any frequency response that we want, deploy it and then update the filter coefficients without changing anything on the PCB!

It’s also easy to design filters with linear phase and at very low sampling frequencies – two things that are tricky with analog.

Sound great, but what’s the bad news?

The effect of aliasing and if designing in fixed point, finite word length issues must be taken into account, including the limitation of the ADC and DAC. As there is clock source, digital designs will produce more EMI than analog filters.

Conclusion

When designing modern IoT sensor measurement applications, digital filters offer a greater degree of design flexibility and high repeatability of characteristics over their analog counterparts.

With the advent of modern processor technology and design tooling, it is estimated that about 80% of IoT smart sensor devices are currently deployed using digital devices, such as Arm’s Cortex-M family. The Arm Cortex-M4 is a very popular choice with hundreds of silicon vendors, as it offers DSP functionality traditionally found in more expensive DSPs. Implementation is further simplified by virtue of ASN’s strong partnership with Arm who together provide a rich offering of easy to use filter design tooling and a free DSP software framework (CMSIS-DSP). These tools and well documented software framework allow you to get your IoT application up and running within minutes.