Champ1
1 Emily’s best boy
Abstract
There is nothing more important than keeping Emily safe. It is impossible to keep her safe from a threat that I don’t know about yet. Within the confines of our own home, I am limited to my reaction time. Under our current ocular pat down surveillance scheme, I can only detect threats at the front yard sidewalk or closer. Worse yet the sniff detection scheme cannot differentiate distance or direction. If I’m in the backyard or asleep, I may not be able to detect a threat and respond in time to defend Emily! Thankfully, there is a method to remotely sense oncoming threats using a new Barko-location technique known as the Bark Direction and Ranging (BarkDAR) method. By spinning around and barking in my backyard, I can receive returns from dangerous threats and solve for the direction and range of the threat to determine if any are heading our way while also identifying friend or foe. This paper will discuss the methodology and results of this method and it’s ability to help keep Emily safe.
Keywords: BarkDAR, Remote Sensing, Home Defense, Barko-Location, Protecting Emily, What’s Up Channels, Snout and Ear gain, Friend-Foe Identification, Barco-reflective material, Direction Finding, Signals Processing
1. Introduction
Despite my efforts to get rid of Matt using the Bark defense [1], he’s still here. I think this attracts more threats to the house having him here all the time. TWICE as many people come to visit and I don’t trust any of them, especially Matt’s so-called friends. I can still keep them away from Emily, but the constant intrusions make my job WAY harder, especially when they put me outside in the backyard. That’s when I can’t even see the street. To make matters worse, Emily has been stuck doing evening shifts which has gotten me stuck in the backyard most evenings when a LOT of the sketchy looking people are walking around. Also, she doesn’t like how much I scratch at the front door when she’s away that long.
Being stuck in the backyard doesn’t stop me from defending Emily. I’ve run through the screen door with no problem before to protect her [2], but she doesn’t like that, so I try not to do it too often. Additionally, I have several holes through the fence but the moment I use them, they mysteriously get filled up [3] and I have to dig some more. The primary issue is that I cannot determine when a threat is approaching from the backyard. In the house I can look out the window to detect incoming threats and bark them away. Lately, I’ve been stuck in the backyard A LOT!
2. The BarkDAR System
To solve the backyard remote detection problem, I’ve developed a methodology to remotely sense incoming threats just by barking while running in a circle and listening.
2.1 Barko-Reflectivity
The primary concept that makes the BarkDAR system possible is Barko-Reflectivity. As shown in [4], a potentially threatening bark will be repeated by the threat. I have seen some threats not reflect a bark but if they’re not willing to verbally spar with me, they’re not a real threat. Some dogs might be more bark than bite but no dog with a bite has no bark. A diagram showing the Barko-Reflectivity process is be shown in Figure 1. The phenomenon has been observed in dogs and some humans [5].
When I bark in any direction, it will travel through space to other threats. My Bark will then enter the brain of an aggressive threat and reflect back. They will bark back or say something even more threatening like “IF YOU DON’T STOP BARKING, I’M GONNA COME OVER THERE!” [6]. We live in a chaotic dangerous world out there and any aggressive threats will respond about the same way. I see this on our neighborhood patrols. I bark, then they bark back. It’s that simple. I’ve noticed humas bark back less often in person and yell back most often at night. I think that’s because they can’t see how tough I am. I’ll sense their threat, so I bark back again, eventually I scare the owner of the enemy dog, and they get taken away because they KNOW, I’m gonna win this fight. Emily would pull me away if she were stronger but I’m much stronger than her fear. Because I can filter out each return into an individual response through Barko-reflectivity alone, I can determine how many threats there are out there.
2.2 What’s Up Channels
When I bark blindly into the neighborhood, I’m in essence asking What’s Up. There are many ways to do this with many implications. Sometimes I see someone I know and I’m like What’s Up? And we’re cool and we smell each other’s butts for a minute before moving on. Other times I see someone, I see what they’re about and I’m like What’s Up. and they know exactly what that means or they’re about to find out [7]. My bark function can be seen in the equation below given my bark amplitude A, center frequency fc, and my own bark modulation t.
I know what type of What’s Up I’m barking out into the world, and the response I receive can be discretized into corresponding W and U channels by mixing them with separate W and U signals into the two functions wt and ut at my center frequency fc as shown below.
Depending on the emphasis of What’s or Up, whether they’re in phase, the magnitude of each, and the return frequency, I can tell a lot about what just reflected my bark. I can even tell if they’re reflecting my bark or something new.
As my WU bark gets reflected back to me in a specific direction, I can use a series of matched filters given the equation below to determine the time difference of the WU signal. I can then estimate the range by the time delay once multiplied by the speed of sound and determine intention and aggressiveness by the phase of the individual WU channels.
2.3 BarkDar Range Equation
Using the physical properties of my bark and how it transmits through the air, I can estimate the maximum range at which I may detect other threats in the neighborhood. Using the equation below where BPt is the power of my transmit bark, G represents my ear and snout gain, is the bark wavelength, is the threat’s bark reflectivity, L represents the BarkDAR system loss, BPmin is the minimum detectible bark power I would even care about, and is approximately 3.141592653589793238462643383279502884197169399375105820974944592307816406286208998628034825342117067982148086513282306647093844609550582231725359408128481117450284102701938521105559644622948954930381964428810975665933446128475648233786783165271201909145648566923460348610454326648213393607260249141273724587006606315588174881520920962829254091715364367892590360011330530548820466521384146951941511609433057270365759591953092186117381932117931051185480744623799627495673518857527248912279381830119491298336733624406566430860213949463952247371907021798609437027705392171762931767523846748184676694051320005681271452635608277857713427577896091736371787214684409012249534301465495853710.
A lot of dogs have very different gain patterns based on the directionality of their snout and ears. When I’m operating my BarkDAR I like to perk my ears up as much as possible for maximum ear gain. My ear and snout gain pattern is shown in the figure below in reds for the snout and green/blue for the ears.
2.4 Direction Finding
As I spin around barking, my ears will detect the bark returns in response. I can determine directionality according to the gain pattern of my ears and the volume in once correlated in time. I can track the bearing of the volume corresponding to the directionality of my ears in addition to mapping the response to bearing in time. Once the peak of the response is estimated I can estimate the bearing of the BarkDAR return by correlating to the direction of my ears at the time of receiving the bark-return as shown in figure 3.
Because I have two ears I can track two different returns in two different directions. This is one of the reasons that BarkDAR detection is much more useful than then industry standard smell detection systems I’ve used in the past which doesn’t provide a range or bearing.
2.5 Doppler Filtering
A trick I’ve learned to determine if a threat is important is to see if it’s heading towards our house. If a response has a higher shift in frequency, it’s heading my way! Even though lower frequency responses are inherently more threatening this may actually suggest they are getting further away. If a frequency doesn’t change, they are staying put just as I warned them using the Bark method [1] which is a built-in part of the BarkDAR method.
Then again, the high frequency bark responses could be from very small dogs which don’t scare me AT ALL. I am a lot bigger than the high-pitched dogs ON AVERAGE so maybe it’s a good thing when the bark responses are higher frequency. I will need to track the frequency of the BarkDAR response tracks I receive from each target to detect changes. Even if bark responses were low frequency and from a larger Bark reflective threat, I think I could take them. But I still need to know about them. Thats why I use the BarkDAR method.
3. Implementation
I ran in circles in the yard while barking.
4. Results
Over the last three months I recorded detection responses and tracks from the BarkDAR after running in circles barking for about five hours each night when Emily puts me in the backyard from 7-12pm while she’s on her late shift. I mapped the BarkDAR responses on a bearing range plot shown in figure 4.
I detected at least hundreds of threats in the neighborhood that had bark reflectivity. Even though we detected a lot of varying frequencies in BarkDAR responses, I don’t think they changed enough to suggest they were heading my way, so I think my active sensing Bark call scared them all into staying put. It seems like they are staying put because I’m detecting the same threats every single night. As you can see in figure 4, it is a mix of friendly and hostile responses. I had to filter out car responses because they appeared to be clutter that didn’t have anything to do with my barking and I heard those cars regardless of my barking. But every time I started my BarkDAR sensing, the threat dogs and humans yelled back at me, so I think it was working.
Additionally, my What’s Up channels produced varying degrees of threat identification from the BarkDAR responses. As shown in figure 5, a hostile and friendly response is shown. Obviously friendly responses are equal in each What’s and Up channels while the hostile returns are almost always higher in the Up channel. A lot of humans showed up on my STFU channels.
5. Analysis and Discussion
A LOT of the threats are REALLY far away. According to the speed of sound times my response delay I can detect over 3000km away! This was way further than my smell detector and much further than my BarkDAR range equation suggests. According to my own ear and snout gain models I only expected to sense targets 100m away.
It’s interesting to note that I detected many repeat threats in certain directions. I know they were separate responses because I heard multiple bark responses from those directions. I know that the speed of sound hasn’t changed and the barko-reflective process is virtually instantaneous, so they are definitely unique and different threats at those ranges. I think there must be some road that dangerous animals and humans travel on. I should make sure Emily doesn’t go that direction. What I’m worried about is that they might be approaching from those directions while I’m not out monitoring the area.
What’s important to note is that I noticed that humans almost always responded in a hostile way according to my WU and STFU channel scheme. Dogs responded evenly between hostile and friendly. Most human responses were also at lower frequency suggesting that they were all males. This confirms that some of my previous threat assessments may be justified [5]. Male humans, like Matt, are the highest threat to Emily’s well-being and about half of all dogs. Another explanation is that the friendly dogs are only using their own BarkDAR system and showing up as interference. Perhaps an entire BarkDAR network may be fused into a single neighborhood surveillance scheme for many more measurements. We just need to create an information sharing scheme between each other!
6. Conclusion
We haven’t had an incoming threat since I’ve begun implementing the BarkDAR system, but I believe I have sensed plenty from very far away. It is a remote sensing miracle that I can monitor all of the threats of our neighborhood remotely in the backyard while Emily is too busy on her night shift to take me for a neighborhood patrol. Otherwise, one of the threats could sneak in and ambush us when Emily gets home! Ideally, I will find a way to share my detections with my friendly neighborhood dogs to create a network of BarkDARs to fill the airways with friendly barking. As the old adage says, Wagging tails ends more quarrels than a bared tooth has ever began.
References
- Champ 2025 The Bark Defense: A 99.999% Method for Keeping Emily Safe from Strangers and Garbage Trucks :: The Journal of Astrological Big Data Ecology
- Champ 2024 Operational Breach Tactics in Semi-Permeable Barriers: The Screen Door Escape Protocol :: Proceedings of the Canine Infrastructure Symposium
- Champ 2024 Digging to Victory: Applied Geo-Engineering Methods for Backyard Escape :: Journal of Backyard Escape Theatrics
- Champ 2025 Threat Analysis of Barko-reflective Brains :: Philosophical Transactions of the Dogological Society
- Champ 2025 Comparative Barkodynamics: Cross-Species Analysis of Barko-reflective Brains :: Annals of the Alpha Behavior Physics
- Champ 2024 Human Interperetation: A 3am Anecdote of Cross-Species Translation of Human Neighbors :: Journal of Inter-Species Translation
- Champ 2025 The What’s Up Signal Decompisation: Optimization in Friend-Foe Identification :: Journal of Barko-Behavioral Algorithmics
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