Blinded by Deception: Navigating the Fog of GPS Warfare

 

From OpenArt

Imagine piloting a high-tech drone, only to have its navigation system hijacked by an invisible enemy. This isn't science fiction; it's the chilling reality of GPS spoofing and denial-of-service (DoS) attacks, posing a critical threat to modern militaries reliant on unmanned aerial vehicles (UAVs).

Not yet do we see drone-on-drone aerial dogfights. Today's battlefields are dominated by silent UAVs, eyes in the sky gathering intel and delivering precision strikes. But their effectiveness hinges on accurate navigation, often solely provided by GPS. Just like your phone, these drones rely on satellite signals for positioning, making them vulnerable to manipulation.

Enter the nefarious world of spoofing. Malicious actors can mimic GPS signals, broadcasting false data that throws the UAV's navigation system off course. Imagine your drone being tricked into thinking it's miles away, potentially flying into enemy territory or missing its target by a devastating margin.

DoS attacks take a different approach, overwhelming GPS satellites with jamming signals, essentially blinding the drones to their true location. This can render an entire fleet of UAVs useless, crippling reconnaissance and attack capabilities.

So, how are militaries fighting back in this fog of GPS warfare? Here's a glimpse into their arsenal:

1. Diversifying the Drone's Senses: Moving beyond GPS, militaries are equipping UAVs with a multi-sensory navigation suite. Inertial Navigation Systems (INS) use internal sensors to track movement, while terrain-matching algorithms compare real-time imagery to onboard maps. Celestial navigation, an age-old technique, utilizes the stars as celestial beacons. Each method has its limitations, but their combined data paints a more accurate picture, making it harder to fool the drone.

2. Redundancy and Fault Tolerance: Just like having a spare tire, militaries are equipping drones with multiple GPS receivers and INUs. If one is spoofed or jammed, the others can still provide some level of navigation, preventing complete blackout. Ground-based radio systems can provide location abilities at strong signal strengths, like the British Gee navigation system for bombers in WW2.

3. Advanced Spoofing Detection: Gone are the days of simply trusting the GPS signal. Militaries are developing sophisticated algorithms that can sniff out fake signals by analyzing signal strength, satellite geometry, and even subtle inconsistencies in the data.

4. Cooperative Navigation: Imagine drones sharing navigational data like a flock of birds. This is the essence of cooperative navigation, where UAVs use information from each other to cross-check their positions and identify anomalies. This not only bolsters individual drones but also creates a resilient network that's harder to disrupt.

The battle for GPS supremacy is far from over. As technology advances, so too will the sophistication of spoofing and jamming techniques. But by diversifying their navigation toolbox, embracing redundancy, and developing advanced detection and cooperation strategies, militaries are taking crucial steps to ensure their UAVs remain sharp-sighted even in the fog of GPS warfare.

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This was written by Bard from lengthy prompts, following reports of the failure of American precision munitions on the Ukraine-Russian front due to Russian GPS jamming/spoofing. I also asked Bard to write this in the style of "Foreign Affairs" or "The Economist" - for a slightly less breathless piece - but the result was too boring to share with you.

GPS vs ATP

There is a view to which I'm quite attracted: that an automated theorem prover is a general-purpose AI engine, which can be used to power arbitrary intelligent behaviour.

This kind of thinking goes back to the earliest days of AI. In "Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp" by Peter Norvig, we read this (Chapter 4):

"The General Problem Solver, developed in 1957 by Alan Newell and Herbert Simon, embodied a grandiose vision: a single computer program that could solve any problem, given a suitable description of the problem.

GPS caused quite a stir when it was introduced, and some people in A1 felt it would sweep in a grand new era of intelligent machines. Simon went so far as to make this statement about his creation:

'It is not my aim to surprise or shock you. . . . But the simplest way I can summarize is to say that there are now in the world machines that think, that learn and create. Moreover, their ability to do these things is going to increase rapidly until - in a visible future  -the range of problems they can handle will be coextensive with the range to which the human mind has been applied.' "

GPS was not a theorem prover, it was - as the name implies - a planner. It used a logic-like syntax to express goals, initial conditions, and the preconditions and postconditions of actions. Carrying out an action, where it was applicable, would delete the preconditions from the current database and assert the postconditions.

GPS started with the goal and contemplated applying the actions in reverse, to create a tree of subgoals looking for a way back to the initial conditions.

Here is how GPS stacks blocks.

I'll come back to why GPS did not solve the problem of artificial general intelligence in a moment. Let's think about GPS and theorem proving.

Logic concerns itself with abstract relationships. It deals in a deeply uninterpreted universe of objects (constants, variables and functions), sets of those objects, and the connections between them.

It's timeless and spaceless, and if you want those things you have to explicitly introduce them, either with extra axioms or by beefing up the inference system so that you're less like a vanilla theorem prover and more like a real-world planner. This is not a small task.

Prolog exemplifies the dilemma. Prolog is a resolution theorem prover for a highly-restricted syntax of first-order logic, namely Horn clauses. But Prolog also has many built-in functions and relationships, for example arithmetic which allows time to be encoded.

It can also extra-logically alter its own axioms mid-computation.

As David H. D. Warren did in the famous WARPLAN back in 1974, you can get the functionality of an efficient planner in an enhanced theorem prover - Prolog - but it's completely non trivial.

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So why didn't GPS take over the world?

It has a major Achilles heel. It's only as good as its operators and the expressive power of its description language. These are magically introduced by the programmer reflecting a pre-existing paradigm of problem-conceptualisation.

GPS has nothing to say about how such a paradigm might be synthesised in the first place.

This is feature (or bug) which GPS-like systems share with all rule-based systems. It's why Expert Systems proved so fragile, and hard to build and maintain.

In real life, our engagement with 'reality' is always changing, the rules never exactly apply and obsolesce even as they cohere. Learning, intervention and further understanding has to occur at a more granular level than GPS - indeed, the latter is best understood as emergent.

Although artificial neural nets, with their fluid, distributed weighting-representations are the new central dogma of AI, the GPS approach was both easier and more obvious. It even works in some stable, well-defined domains.

It had to be tried; it was hardly wasted effort.

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Another joy from Norvig's book (available for download here) is in section 1.7: the origins of lambda notation.

"It is also possible to introduce a function without giving it a name, using the special syntax lambda. The name lambda comes from the mathematician Alonzo Church's notation for functions (Church 1941). Lisp usually prefers expressive names over terse Greek letters, but lambda is an exception. A better name would be make-function.

Lambda derives from the notation in Russell and Whitehead's Principia Mathematica, which used a caret over bound variables. Church wanted a one-dimensional string, so he moved the caret in front: ^x(x + x).

The caret looked funny with nothing below it, so Church switched to the closest thing, an uppercase lambda, Λx(x + x).

The Λ was easily confused with other symbols, so eventually the lowercase lambda was substituted: λx(x + x).

John McCarthy was a student of Church's at Princeton, so when McCarthy invented Lisp in 1958, he adopted the lambda notation. There were no Greek letters on the key punches of that era, so McCarthy used (lambda (x) (+ x x)) .. and it has survived to this day."