I'm wondering about a method to find an unknown frequency. I want to intercept the data from my hydro meter, but I don't know the exact frequency. I'm pretty sure it's in the range 902-928 MHz, but my rtlsdr only has a bandwidth of about 2 or 3 MHz. I'm new to radio stuff and signal processing, so I'm wondering how I hone in on the data. I read the meter also uses frequency hopping.
Ideally I'd like to setup my rtlsdr dongle to rotate on bands like so:
listen on 902 for an hour
listen on 904 for an hour
listen on 906 for an hour
...
listen on 928 for an hour
Then I want to only record something when there is a "blip" - this way I could take these recordings and analyze them.
Is there a better way?
Does your hydro meter have an FCC ID? If it has, then there's extensive documentation on the system on the FCC's website, including a detailed description which frequencies, bandwidths and powers the device uses, and possibly also which modulation.
Your meter probably works in an ISM band, meaning that unless you put everything in an RF-proof box, there will be a lot of stuff going through the ether around the frequencies you want to observer, so your 1h listening mode doesn't sound too promising. Also, there's no guarantee there's a "blip" – transmit power might be really low and spread out over a larger bandwidth (search for "spread spectrum" technology to understand how).
If observation is your best approach, I'd go for using gr-osmosdr and the osmocom_fft -W tool that comes with it.
Related
I have location data from a large number of users (hundreds of thousands). I store the current position and a few historical data points (minute data going back one hour).
How would I go about detecting crowds that gather around natural events like birthday parties etc.? Even smaller crowds (let's say starting from 5 people) should be detected.
The algorithm needs to work in almost real time (or at least once a minute) to detect crowds as they happen.
I have looked into many cluster analysis algorithms, but most of them seem like a bad choice. They either take too long (I have seen O(n^3) and O(2^n)) or need to know how many clusters there are beforehand.
Can someone help me? Thank you!
Let each user be it's own cluster. When she gets within distance R to another user form a new cluster and separate again when the person leaves. You have your event when:
Number of people is greater than N
They are in the same place for the timer greater than T
The party is not moving (might indicate a public transport)
It's not located in public service buildings (hospital, school etc.)
(good number of other conditions)
One minute is plenty of time to get it done even on hundreds of thousands of people. In naive implementation it would be O(n^2), but mind there is no point in comparing location of each individual, only those in close neighbourhood. In first approximation you can divide the "world" into sectors, which also makes it easy to make the task parallel - and in turn easily scale. More users? Just add a few more nodes and downscale.
One idea would be to think in terms of 'mass' and centre of gravity. First of all, do not mark something as event until the mass is not greater than e.g. 15 units. Sure, location is imprecise, but in case of events it should average around centre of the event. If your cluster grows in any direction without adding substantial mass, then most likely it isn't right. Look at methods like DBSCAN (density-based clustering), good inspiration can be also taken from physical systems, even Ising model (here you think in terms of temperature and "flipping" someone to join the crowd)ale at time of limited activity.
How to avoid "single-linkage problem" mentioned by author in comments? One idea would be to think in terms of 'mass' and centre of gravity. First of all, do not mark something as event until the mass is not greater than e.g. 15 units. Sure, location is imprecise, but in case of events it should average around centre of the event. If your cluster grows in any direction without adding substantial mass, then most likely it isn't right. Look at methods like DBSCAN (density-based clustering), good inspiration can be also taken from physical systems, even Ising model (here you think in terms of temperature and "flipping" someone to join the crowd). It is not a novel problem and I am sure there are papers that cover it (partially), e.g. Is There a Crowd? Experiences in Using Density-Based Clustering and Outlier Detection.
There is little use in doing a full clustering.
Just uses good database index.
Keep a database of the current positions.
Whenever you get a new coordinate, query the database with the desired radius, say 50 meters. A good index will do this in O(log n) for a small radius. If you get enough results, this may be an event, or someone joining an ongoing event.
I'm pretty new to using GNURadio and I'm having trouble recovering the data from a signal that I've saved into a file. The signal is a carrier frequency of 56KHz with a frequency shift key of +/- 200hz at 600 baud.
So far, I've been able to demodulate the signal that looks similar to the signal I get from the source:
I'm trying to get this into a repeating string of 1s and 0s (the whole telegram is 38 bytes long and it continuously repeats). I've tried to use a clock recovery block in order to have only one byte per sample, but I'm not having much luck. Using the M&M clock recovery block, the whole telegram sometimes comes out correct, but it is not consistent. I've tried to adjust the omega and Mu values, but it doesn't seem to help that much. I've also tried using the Polyphase Clock sync, but I keep getting a runtime error of 'please specify a filter'. Is this asking me to add a tap? what tap would i use?
So I guess my overall question would be: What's the best way to get the telegram out of the demodulated fsk signal?
Again, pretty new at this so please let me know if I've missed something crucial. GNU flow graph below:
You're recovering the bit timing, but you're not recovering the byte boundaries – that needs to happen "one level higher", eg. by a well-known packet format with a defined preamble that you can look for.
I'm trying to demodulate a signal using GNU Radio Companion. The signal is FSK (Frequency-shift keying), with mark and space frequencies at 1200 and 2200 Hz, respectively.
The data in the signal text data generated by a device called GeoStamp Audio. The device generates audio of GPS data fed into it in real time, and it can also decode that audio. I have the decoded text version of the audio for reference.
I have set up a flow graph in GNU Radio (see below), and it runs without error, but with all the variations I've tried, I still can't get the data.
The output of the flow graph should be binary (1s and 0s) that I can later convert to normal text, right?
Is it correct to feed in a wav audio file the way I am?
How can I recover the data from the demodulated signal -- am I missing something in my flow graph?
This is a FFT plot of the wav audio file before demodulation:
This is the result of the scope sink after demodulation (maybe looks promising?):
UPDATE (August 2, 2016): I'm still working on this problem (occasionally), and unfortunately still cannot retrieve the data. The result is a promising-looking string of 1's and 0's, but nothing intelligible.
If anyone has suggestions for figuring out the settings on the Polyphase Clock Sync or Clock Recovery MM blocks, or the gain on the Quad Demod block, I would greatly appreciate it.
Here is one version of an updated flow graph based on Marcus's answer (also trying other versions with polyphase clock recovery):
However, I'm still unable to recover data that makes any sense. The result is a long string of 1's and 0's, but not the right ones. I've tried tweaking nearly all the settings in all the blocks. I thought maybe the clock recovery was off, but I've tried a wide range of values with no improvement.
So, at first sight, my approach here would look something like:
What happens here is that we take the input, shift it in frequency domain so that mark and space are at +-500 Hz, and then use quadrature demod.
"Logically", we can then just make a "sign decision". I'll share the configuration of the Xlating FIR here:
Notice that the signal is first shifted so that the center frequency (middle between 2200 and 1200 Hz) ends up at 0Hz, and then filtered by a low pass (gain = 1.0, Stopband starts at 1 kHz, Passband ends at 1 kHz - 400 Hz = 600 Hz). At this point, the actual bandwidth that's still present in the signal is much lower than the sample rate, so you might also just downsample without losses (set decimation to something higher, e.g. 16), but for the sake of analysis, we won't do that.
The time sink should now show better values. Have a look at the edges; they are probably not extremely steep. For clock sync I'd hence recommend to just go and try the polyphase clock recovery instead of Müller & Mueller; chosing about any "somewhat round" pulse shape could work.
For fun and giggles, I clicked together a quick demo demod (GRC here):
which shows:
I have a bunch of questions concerning Timing Advance in GSM :
When is it defined ?
Is it the phone or the BTS who's in charge of defining it's value ?
is it dynamic, does it depends on certain situations ?
Let's say that I figured out a way to get the exact value of the Timing Advance (GSM Layer 1 Transmission level) from the phone's modem :
In order to verify my solution, I'm supposed to put my phone over and over in a situation where he have to use/change the Timing Advance while I log its value...
How can I do that ?
Thanks
In the GSM cellular mobile phone standard, timing advance value corresponds to the length of time a signal takes to reach the base station from a mobile phone. GSM uses TDMA technology in the radio interface to share a single frequency between several users, assigning sequential timeslots to the individual users sharing a frequency. Each user transmits periodically for less than one-eighth of the time within one of the eight timeslots. Since the users are at various distances from the base station and radio waves travel at the finite speed of light, the precise arrival-time within the slot can be used by the base station to determine the distance to the mobile phone. The time at which the phone is allowed to transmit a burst of traffic within a timeslot must be adjusted accordingly to prevent collisions with adjacent users. Timing Advance (TA) is the variable controlling this adjustment.
Technical Specifications 3GPP TS 05.10[1] and TS 45.010[2] describe the TA value adjustment procedures. The TA value is normally between 0 and 63, with each step representing an advance of one bit period (approximately 3.69 microseconds). With radio waves travelling at about 300,000,000 metres per second (that is 300 metres per microsecond), one TA step then represents a change in round-trip distance (twice the propagation range) of about 1,100 metres. This means that the TA value changes for each 550-metre change in the range between a mobile and the base station. This limit of 63 × 550 metres is the maximum 35 kilometres that a device can be from a base station and is the upper bound on cell placement distance.
A continually adjusted TA value avoids interference to and from other users in adjacent timeslots, thereby minimizing data loss and maintaining Mobile QoS (call quality-of-service).
Timing Advance is significant for privacy and communications security, as its combination with other variables can allow GSM localization to find the device's position and tracking the mobile phone user. TA is also used to adjust transmission power in Space-division multiple access systems.
This limited the original range of a GSM cell site to 35km as mandated by the duration of the standard timeslots defined in the GSM specification. The maximum distance is given by the maximum time that the signal from the mobile/BTS needs to reach the receiver of the mobile/BTS on time to be successfully heard. At the air interface the delay between the transmission of the downlink (BTS) and the uplink (mobile) has an offset of 3 timeslots. Until now the mobile station has used a timing advance to compensate for the propagation delay as the distance to the BTS changes. The timing advance values are coded by 6 bits, which gives the theoretical maximum BTS/mobile separation as 35km.
By implementing the Extended Range feature, the BTS is able to receive the uplink signal in two adjacent timeslots instead of one. When the mobile station reaches its maximum timing advance, i.e. maximum range, the BTS expands its hearing window with an internal timing advance that gives the necessary time for the mobile to be heard by the BTS even from the extended distance. This extra advance is the duration of a single timeslot, a 156 bit period. This gives roughly 120 km range for a cell.[3] and is implemented in sparsely populated areas and to reach islands for example.
Hope this Answer the question:)
It's defined everytime the BTS needs to set the define the phone's transmission power, which happens quite often.
It's the core system (BTS in GSM) who totally in charge of defining it's value.
It's very dynamic, and change a lot. Globally, the GSM core system is constantly trying to find the exact distance between the BTS and the MS, so it constantly make a kind of "ping" to calculate it. The result of such operations is generally not that accurate since there are a lot of obstacles between the mobile and the BTS (it's not a direct link in an open space).
Such operations happens a lot, so use your smartphone. Simply.
Most people are limited to about 5 or 6 locations on a daily basis (work, home, school, store, etc). I want to speed up address display by caching a few of these most visited locations. I've been able to get the address info using both google maps GPS and JSON and Locator.reverseGeocode. What would be the best way to cache this information and to check proximity quickly? I found this GPS distance calculation example and have it working. Is there a faster way to check for proximity?
Please see similar question first: Optimization of a distance calculation function
There are several things we can change in distance calculations to improve performance:
Measure device speed and decrease or increase period of proximity test accordingly
Trigonometric calculations takes most of performence, but it may done much faster. First make bold distance calculations using lookup table method, then if distance is less than proximity limit + uncertainty limit, use CORDIC method for more precise calculation.
Use constants for Math.PI/180.0 and 180.0/Math.PI
several links that may be helpful:
Very useful explanations of CORDIC, especially doc from Parallax for dummies
Fast transcendent / trigonometric functions for Java
Cordic.java at Trac by Thomas B. Preusser
Cordic.java at seng440 proj
Sin/Cos look-up table source at processing.org by toxi