PAF pilot highlights how chinese J10C fighters has edged US F16's

[Michael]

Usually through trials, sending pilots over, sims and real time flying, and then send an EOI. In China's case, they customise the aircraft according to on needs and get a NOC for that.

In USA's case, thats for F-16 and PAF knows F-16s inside out including cost of parts which makes negotiations tough. Pakistan looks for concessions through FMS packages which reduces the price through an aid package. The lack of FMS package later on created problem for optional 18 F-16s Block 52+, then reduced to 8 F-16C Block 52+. Its the same case with AH-1Zs where Pakistan doesnt want to pay full price as the deal was under through FMS package.

Keep asking nicely, i will increase your information and knowledge.

Clearly, you are trying to avoid your problem by forcibly changing the subject.

Doing this has no practical significance.

 

[MH.Yang]

Uzbekistan just received the first batch few weeks ago.

next r Indonesia and Bangladesh

 

[Be dazzled]

PAF should get a share for practically demonstrating the beast to the world.

 

[Signalian]

Clearly, you are trying to avoid your problem by forcibly changing the subject.

Doing this has no practical significance.

Subject is aircrafts. Im having fun, why change it, lets continue.

Talking of "practical significance", lets slowly dive into the amalgamation of AI, ML (DL) and warfare. Lets set the foundation first.

LOHAN. Have you heard of it ?
Its an automated strike-planning and tactical coordination engine designed specifically for the Israeli Air Force (IAF). Deployed extensively by the IDF’s Matzpen software development division (part of the C4I and Cyber Defense Directorate), it acts as an operational bridge that accelerates the air combat decision loop from days or hours down to a matter of minutes.

Rather than generating individual targets from scratch (like Lavender), Lohan orchestrates the execution of complex air operations across multiple fronts simultaneously.

1. Strike Synchronization:
Lohan assists commanders in organizing multi-tiered aerial maneuvers. It calculates how to safely and efficiently coordinate different assets such as fighter jets, attack helicopters, and unmanned aerial vehicle (UAV) drone fleets like the IAF's Storm Clouds network so they do not conflict in the same airspace.
2. Dynamic Trajectory Adjustment:
When an aircraft is in flight, the AI processes incoming threats or shifting weather patterns in real-time. It automatically recalibrates the trajectories of fighter jets to bypass enemy anti-aircraft batteries or avoid sudden hazards on the fly.
3. Integration with C4I Systems: Lohan feeds directly into MapIt, a 3D satellite and visual data platform that builds a unified real-time map of the battlefield. This allows the Air Force to synchronize its aerial plans perfectly with ground troops and naval deployments to minimize friendly-fire incidents.
4. Speed of Execution: According to statements from IDF Matzpen commanders, the integration of algorithmic planning models like Lohan has cut down the logistical time required to authorize and map out complex strikes from multiple days to a few seconds.

So what does India has then?

1. Project Sanjay / Electronic Intelligence Collation System:
This acts as India's foundational battlefield command system. It ingests immense multi-source data streams from satellites, unmanned aerial vehicles (UAVs), and battlefield radars. Using data-fusion machine learning models, it strips out noise to create a single, real-time shared operational picture for commanders.

2. Trinitra: A direct companion to Project Sanjay.
It uses deep learning predictive analytics to evaluate combat environments, automatically identifying, ranking, and prioritizing the most critical hostile threats to establish strategic dominance in active conflict zones.

3. Kautilya:
An intelligence-fusion application deployed to map out and prioritize targets. The system runs data analytics over intercepted adversarial data including 26 years of archived frequency signatures and radio emission records to predict adversarial movements and pinpoint hidden command structures.

4. Indrajaal:
This wide-area anti-drone shield relies on an AI-driven command-and-control core. It manages a distributed web of sensory subsystems to detect, track, and counter low radar cross-section threats including loitering munitions and drone swarms across a defensive perimeter.

5. SAKSHAM:
This system bypasses reliance on traditional long-range surface-to-air missile radars. Instead, it uses AI-driven sensor fusion to stitch together a continuous unified threat picture from completely distinct sensor types:

5a. Passive RF (Radio Frequency) Classifiers:
SAKSHAM continuously scans the electromagnetic spectrum. The moment the BVR missile opens its mid-course data link to receive directional corrections from the datalink, SAKSHAM's passive RF grid intercepts the emission, pinpointing the missile's approximate position without emitting a signal of its own.

5b. Electro-Optical & Infrared (EO/IR) Tracking:
Missiles accelerating to Mach 4+ generate immense frictional heat and exhaust signatures. SAKSHAM integrates forward-deployed high-resolution thermal imaging systems that visually isolate the extreme infrared signature of a BVR missile plume against the sky baseline.

5c. Acoustic Sensor Arrays:
SAKSHAM leverages a distributed web of high-sensitivity acoustic arrays that flag the distinct sonic profiles of low-flying objects or sudden supersonic cracks.

SAKSHAM does not function as an isolated defensive outpost. It is natively integrated into Akashteer, the Indian Army’s automated Air Defence Control & Reporting System.

5d. RUASP Generation:
The grid automatically constructs a Recognized UAS Picture (RUASP) across entire theater commands in real time.

5e. Automated Target Differentiation:
SAKSHAM's core machine learning algorithms instantly classify every airspace user into a friendly, neutral, or hostile matrix. When a missile signature is detected, the AI calculates its trajectory backward to instantly confirm it was fired from an unverified, hostile sector, triggering the hard-kill mechanism immediately.

Once SAKSHAM's AI threat evaluation layer confirms an incoming BVR missile or a coordinate-tracking drone swarm, it instantly orchestrates automated responses via the encrypted, electronic-warfare-resistant Army Data Network (ADN):

Soft-Kill (Electronic Spoofing):
SAKSHAM can instantly cue high-powered directional jammers and GPS spoofers to break the missile's mid-course link with its source datalink, blinding its guidance system before it reaches its target.

Hard-Kill (Directed Energy and Lasers):
For highly threatening targets, the grid automates hand-offs to kinetic interceptors and DRDO-developed Directed Energy Weapons (DEWs). High-power laser tracking loops are fed precise positional coordinates by SAKSHAM's AI, instantly locking onto and thermal-vaporizing or destabilizing the incoming threat mid-flight.

Where does Pakistan stand ?

The integration of the PAF Multi-Domain Operations (MDO) network with Chinese satellite infrastructure represents a deep, structurally fused system of intelligence framework. Managed primarily by the PAF’s Centre of Artificial Intelligence and Computing (CENTAIC), this link translates space-based raw signals into real-time targeting telemetry for frontline assets like aircrafts.

The technical architecture and operational mechanisms of this integration rely on these factors:

1. The Multi-Modal Satellite Input & PIESAT Deal
The data layer of the MDO network is powered by continuous streams from Chinese sovereign military satellites and Pakistan's rapidly expanding, Chinese-backed low-earth orbit (LEO) observation grid.

1a. The PIESAT Constellation:
A deal with China’s PIESAT Information Technology integrates an advanced network of over 20 Interferometric Synthetic Aperture Radar (InSAR) satellites. These provide all-weather, day-and-night 3D topographical imaging for mapping.

1b. Dedicated Regional Orbits:
Satellites like PRSC-EO3 are placed in unique, non-sun-synchronous orbits specifically tailored to maximize "revisit rates" over South Asia. This allows the MDO network to continuously scan regional target matrices multiple times per day.

2. High-Speed AI Processing via CENTAIC
Raw data captured by optical, hyperspectral, and radar payloads is downlinked directly to ground stations and funneled immediately into the CENTAIC AI core.

2a. Automated Target Recognition (ATR):
Deep learning computer vision models scan incoming images to bypass manual analysis. The algorithms automatically identify multi-spectral camouflage, track moving troop movements, detect thermal exhaust signatures, and isolate anomalies.

2b. Pattern of Life Analysis:
Predictive analytics map out structural baselines, instantly alerting the MDO network when asset positions change.

3. Encrypted Data Links & Direct Kill Architecture
The operational fusion between PAF combat assets and Chinese satellites is glued together by secure, shared communication protocols:

3a. The BeiDou Assistance (controversial though):
This enables merging of precision geolocation with a robust satellite messaging layer (real time information transfer).
3b. Link-17:
All military assets ingest live satellite targeting feeds for information sharing.
3c. The Kill Chain:
This network integration facilitates BVR combat maneuvers. In an active engagement, a Rafale can be detected and detected by ground radar, the targeting data is streamed securely via satellite link to a AWACs, which calculates exactly where the enemy aircraft will be in the next 60 to 90 seconds then guides a BVR missile fired from a J10 operating completely "silent" with its onboard radar switched off. BVR missile does not turn on its own radar seeker (which would alert the target). Instead, it flies inertially toward a designated target location calculated by AWACs while it sends encrypted, real-time mid-course directional corrections directly to the missile's data link receiver. Only in the final few seconds of the flight, when the BVR missile is roughly 15–20 km away from the target, does the missile turn on its own internal, highly advanced active AESA radar seeker. At this point, the target (rafale) receives a sudden, maximum-intensity radar warning, but it is far too late to dodge a missile traveling at Mach 4+.

This works against stealth aircraft also. The space-based SAR satellites or ground-based VHF radars can often detect the stealth aircraft from alternative geometric angles or thermal signatures, feeding that tracking data straight to the BVR missile.

Now lets go the cyber side of the things for pakistan.

1. Network Obfuscation & Signal Scrambling

1a. Automated Signal Obfuscation:
Pakistan used machine learning to detect active packet inspection and tracking signatures from Indian scanners. Once detected, the model automatically altered packet sizes, headers, and transmission timing, effectively scrambling data feeds to blind Indian drone and satellite collation telemetry.

1b. IP Deception & Automated Proxies:
To counter precise geospatial tracking, Pakistan's networks utilized machine learning to automatically mask server signatures. The system dynamically rerouted critical command and control (C2) traffic through a rotating web of third-country IPs and false-flag proxies, preventing Indian analytics from mapping a baseline "mapping" for Pakistani operational hubs.

2. Algorithmic Decoy Server Deployment
2a. Dynamic Decoy Servers:
Rather than blocking Indian intrusions, Pakistan’s defense infrastructure deployed AI-orchestrated "honeypots". When Indian tracking algorithms or Advanced Persistent Threat (APT) groups attempted to map military networks, they were automatically isolated and funneled into virtualized, fake server environments.

2b. Telemetry Poisoning:
These decoy servers fed back false tracking data, spoofed logistical coordinates, and synthetic communications logs. This directly poisoned India's Project Sanjay data-fusion engine, leading Indian analytical models to calculate incorrect troop layouts and supply-chain logistics.


3. ICT Security & Enterprise Clouds

3a. Secure Cloud Partitioning:
The Pakistani military leveraged integrated cloud infrastructures hosted via tech providers to shield its cyber assets. Because these networks utilized closed enterprise backdoors, they remained heavily shielded from Western and Indian penetration tools.

3b. Zero-Trust Anomalous Baseline Tracking:
Deployed inside Pakistan's localized defensive firewalls, deep learning models monitored domestic military infrastructure for zero-day exploits. By continuously studying the standard working of internal networks, the AI could instantly flag the minute an Indian malware strains, such as custom remote access trojans (RATs) which attempted to ping external servers to exfiltrate data, dropping the connection in sub-milliseconds.

I would like to study more of israeli AI/ML/DL systems actually as India could potentially use those strategies in the next conflict which will have high use of AI and ML for all ops.

 

[huanghong]

Interestingly, Yankee, a Chinese military blogger known for having inside information, said on his program that during the “Exercise Shaheen ” joint exercise between Chinese and Pakistani pilots, the Chinese pilots found that the Pakistani pilots seemed not very adept at using medium-range attacks. They often pressed in to 40–50 kilometers before launching medium-range missiles. There also seemed to be few tactics for firing medium-range missiles. He suspected that this was because the effective range of the AIM-120C5s Pakistan possesses and the radar on its aging F-16s forced them to carry out medium-range attacks at 50–60 kilometers; beyond that distance, it became an ineffective shot.

 

[Michael]

Subject is aircrafts. Im having fun, why change it, lets continue.

Talking of "practical significance", lets slowly dive into the amalgamation of AI, ML (DL) and warfare. Lets set the foundation first.

LOHAN. Have you heard of it ?
Its an automated strike-planning and tactical coordination engine designed specifically for the Israeli Air Force (IAF). Deployed extensively by the IDF’s Matzpen software development division (part of the C4I and Cyber Defense Directorate), it acts as an operational bridge that accelerates the air combat decision loop from days or hours down to a matter of minutes.

Rather than generating individual targets from scratch (like Lavender), Lohan orchestrates the execution of complex air operations across multiple fronts simultaneously.

1. Strike Synchronization:
Lohan assists commanders in organizing multi-tiered aerial maneuvers. It calculates how to safely and efficiently coordinate different assets such as fighter jets, attack helicopters, and unmanned aerial vehicle (UAV) drone fleets like the IAF's Storm Clouds network so they do not conflict in the same airspace.
2. Dynamic Trajectory Adjustment:
When an aircraft is in flight, the AI processes incoming threats or shifting weather patterns in real-time. It automatically recalibrates the trajectories of fighter jets to bypass enemy anti-aircraft batteries or avoid sudden hazards on the fly.
3. Integration with C4I Systems: Lohan feeds directly into MapIt, a 3D satellite and visual data platform that builds a unified real-time map of the battlefield. This allows the Air Force to synchronize its aerial plans perfectly with ground troops and naval deployments to minimize friendly-fire incidents.
4. Speed of Execution: According to statements from IDF Matzpen commanders, the integration of algorithmic planning models like Lohan has cut down the logistical time required to authorize and map out complex strikes from multiple days to a few seconds.

So what does India has then?

1. Project Sanjay / Electronic Intelligence Collation System:
This acts as India's foundational battlefield command system. It ingests immense multi-source data streams from satellites, unmanned aerial vehicles (UAVs), and battlefield radars. Using data-fusion machine learning models, it strips out noise to create a single, real-time shared operational picture for commanders.

2. Trinitra: A direct companion to Project Sanjay.
It uses deep learning predictive analytics to evaluate combat environments, automatically identifying, ranking, and prioritizing the most critical hostile threats to establish strategic dominance in active conflict zones.

3. Kautilya:
An intelligence-fusion application deployed to map out and prioritize targets. The system runs data analytics over intercepted adversarial data including 26 years of archived frequency signatures and radio emission records to predict adversarial movements and pinpoint hidden command structures.

4. Indrajaal:
This wide-area anti-drone shield relies on an AI-driven command-and-control core. It manages a distributed web of sensory subsystems to detect, track, and counter low radar cross-section threats including loitering munitions and drone swarms across a defensive perimeter.

5. SAKSHAM:
This system bypasses reliance on traditional long-range surface-to-air missile radars. Instead, it uses AI-driven sensor fusion to stitch together a continuous unified threat picture from completely distinct sensor types:

5a. Passive RF (Radio Frequency) Classifiers:
SAKSHAM continuously scans the electromagnetic spectrum. The moment the BVR missile opens its mid-course data link to receive directional corrections from the datalink, SAKSHAM's passive RF grid intercepts the emission, pinpointing the missile's approximate position without emitting a signal of its own.

5b. Electro-Optical & Infrared (EO/IR) Tracking:
Missiles accelerating to Mach 4+ generate immense frictional heat and exhaust signatures. SAKSHAM integrates forward-deployed high-resolution thermal imaging systems that visually isolate the extreme infrared signature of a BVR missile plume against the sky baseline.

5c. Acoustic Sensor Arrays:
SAKSHAM leverages a distributed web of high-sensitivity acoustic arrays that flag the distinct sonic profiles of low-flying objects or sudden supersonic cracks.

SAKSHAM does not function as an isolated defensive outpost. It is natively integrated into Akashteer, the Indian Army’s automated Air Defence Control & Reporting System.

5d. RUASP Generation:
The grid automatically constructs a Recognized UAS Picture (RUASP) across entire theater commands in real time.

5e. Automated Target Differentiation:
SAKSHAM's core machine learning algorithms instantly classify every airspace user into a friendly, neutral, or hostile matrix. When a missile signature is detected, the AI calculates its trajectory backward to instantly confirm it was fired from an unverified, hostile sector, triggering the hard-kill mechanism immediately.

Once SAKSHAM's AI threat evaluation layer confirms an incoming BVR missile or a coordinate-tracking drone swarm, it instantly orchestrates automated responses via the encrypted, electronic-warfare-resistant Army Data Network (ADN):

Soft-Kill (Electronic Spoofing):
SAKSHAM can instantly cue high-powered directional jammers and GPS spoofers to break the missile's mid-course link with its source datalink, blinding its guidance system before it reaches its target.

Hard-Kill (Directed Energy and Lasers):
For highly threatening targets, the grid automates hand-offs to kinetic interceptors and DRDO-developed Directed Energy Weapons (DEWs). High-power laser tracking loops are fed precise positional coordinates by SAKSHAM's AI, instantly locking onto and thermal-vaporizing or destabilizing the incoming threat mid-flight.

Where does Pakistan stand ?

The integration of the PAF Multi-Domain Operations (MDO) network with Chinese satellite infrastructure represents a deep, structurally fused system of intelligence framework. Managed primarily by the PAF’s Centre of Artificial Intelligence and Computing (CENTAIC), this link translates space-based raw signals into real-time targeting telemetry for frontline assets like aircrafts.

The technical architecture and operational mechanisms of this integration rely on these factors:

1. The Multi-Modal Satellite Input & PIESAT Deal
The data layer of the MDO network is powered by continuous streams from Chinese sovereign military satellites and Pakistan's rapidly expanding, Chinese-backed low-earth orbit (LEO) observation grid.

1a. The PIESAT Constellation:
A deal with China’s PIESAT Information Technology integrates an advanced network of over 20 Interferometric Synthetic Aperture Radar (InSAR) satellites. These provide all-weather, day-and-night 3D topographical imaging for mapping.

1b. Dedicated Regional Orbits:
Satellites like PRSC-EO3 are placed in unique, non-sun-synchronous orbits specifically tailored to maximize "revisit rates" over South Asia. This allows the MDO network to continuously scan regional target matrices multiple times per day.

2. High-Speed AI Processing via CENTAIC
Raw data captured by optical, hyperspectral, and radar payloads is downlinked directly to ground stations and funneled immediately into the CENTAIC AI core.

2a. Automated Target Recognition (ATR):
Deep learning computer vision models scan incoming images to bypass manual analysis. The algorithms automatically identify multi-spectral camouflage, track moving troop movements, detect thermal exhaust signatures, and isolate anomalies.

2b. Pattern of Life Analysis:
Predictive analytics map out structural baselines, instantly alerting the MDO network when asset positions change.

3. Encrypted Data Links & Direct Kill Architecture
The operational fusion between PAF combat assets and Chinese satellites is glued together by secure, shared communication protocols:

3a. The BeiDou Assistance (controversial though):
This enables merging of precision geolocation with a robust satellite messaging layer (real time information transfer).
3b. Link-17:
All military assets ingest live satellite targeting feeds for information sharing.
3c. The Kill Chain:
This network integration facilitates BVR combat maneuvers. In an active engagement, a Rafale can be detected and detected by ground radar, the targeting data is streamed securely via satellite link to a AWACs, which calculates exactly where the enemy aircraft will be in the next 60 to 90 seconds then guides a BVR missile fired from a J10 operating completely "silent" with its onboard radar switched off. BVR missile does not turn on its own radar seeker (which would alert the target). Instead, it flies inertially toward a designated target location calculated by AWACs while it sends encrypted, real-time mid-course directional corrections directly to the missile's data link receiver. Only in the final few seconds of the flight, when the BVR missile is roughly 15–20 km away from the target, does the missile turn on its own internal, highly advanced active AESA radar seeker. At this point, the target (rafale) receives a sudden, maximum-intensity radar warning, but it is far too late to dodge a missile traveling at Mach 4+.

This works against stealth aircraft also. The space-based SAR satellites or ground-based VHF radars can often detect the stealth aircraft from alternative geometric angles or thermal signatures, feeding that tracking data straight to the BVR missile.

Now lets go the cyber side of the things for pakistan.

1. Network Obfuscation & Signal Scrambling

1a. Automated Signal Obfuscation:
Pakistan used machine learning to detect active packet inspection and tracking signatures from Indian scanners. Once detected, the model automatically altered packet sizes, headers, and transmission timing, effectively scrambling data feeds to blind Indian drone and satellite collation telemetry.

1b. IP Deception & Automated Proxies:
To counter precise geospatial tracking, Pakistan's networks utilized machine learning to automatically mask server signatures. The system dynamically rerouted critical command and control (C2) traffic through a rotating web of third-country IPs and false-flag proxies, preventing Indian analytics from mapping a baseline "mapping" for Pakistani operational hubs.

2. Algorithmic Decoy Server Deployment
2a. Dynamic Decoy Servers:
Rather than blocking Indian intrusions, Pakistan’s defense infrastructure deployed AI-orchestrated "honeypots". When Indian tracking algorithms or Advanced Persistent Threat (APT) groups attempted to map military networks, they were automatically isolated and funneled into virtualized, fake server environments.

2b. Telemetry Poisoning:
These decoy servers fed back false tracking data, spoofed logistical coordinates, and synthetic communications logs. This directly poisoned India's Project Sanjay data-fusion engine, leading Indian analytical models to calculate incorrect troop layouts and supply-chain logistics.


3. ICT Security & Enterprise Clouds

3a. Secure Cloud Partitioning:
The Pakistani military leveraged integrated cloud infrastructures hosted via tech providers to shield its cyber assets. Because these networks utilized closed enterprise backdoors, they remained heavily shielded from Western and Indian penetration tools.

3b. Zero-Trust Anomalous Baseline Tracking:
Deployed inside Pakistan's localized defensive firewalls, deep learning models monitored domestic military infrastructure for zero-day exploits. By continuously studying the standard working of internal networks, the AI could instantly flag the minute an Indian malware strains, such as custom remote access trojans (RATs) which attempted to ping external servers to exfiltrate data, dropping the connection in sub-milliseconds.

I would like to study more of israeli AI/ML/DL systems actually as India could potentially use those strategies in the next conflict which will have high use of AI and ML for all ops.

I will not engage in any in-depth discussion with members like you who dare not acknowledge their own identity.


BTW.
@Mr X @Musings @Waz
I recall that the forum rules explicitly state: "All users must display the correct flags."
So, why does the forum administration allow a member—who bears the label "THINK TANK: CONSULTANT" but lacks any "flag"—to go around the forum provoking others at will?

M

Thread 'Announcement: Please update your country flags'

Jan 9, 2024

@Registered

Please update your profile by visiting the following link

https://defencepk.com/forums/account/account-details

Select > Country of Origin
Select > Country of Residence
Save

All users must display the correct flags

• Mr X
• Replies: 130
• Forum: Announcements

• 
• 

 

[EgoKaempfer]

Interestingly, Yankee, a Chinese military blogger known for having inside information, said on his program that during the “Exercise Shaheen ” joint exercise between Chinese and Pakistani pilots, the Chinese pilots found that the Pakistani pilots seemed not very adept at using medium-range attacks. They often pressed in to 40–50 kilometers before launching medium-range missiles. There also seemed to be few tactics for firing medium-range missiles. He suspected that this was because the effective range of the AIM-120C5s Pakistan possesses and the radar on its aging F-16s forced them to carry out medium-range attacks at 50–60 kilometers; beyond that distance, it became an ineffective shot.

So the last "known" Shaheen X Exercise took place in 2023, during which the PAF fielded JF-17s and J-10CEs. Which of these platforms is equipped to carry AIM-120C5s?

 

[huanghong]

So the last "known" Shaheen X Exercise took place in 2023, during which the PAF fielded JF-17s and J-10CEs. Which of these platforms is equipped to carry AIM-120C5s?

What he’s saying is pilots are used to the range of the F-16 and AIM-120, which makes them, when using the JF-17 or J-10 in medium-range combat, still close in to 40 to 50 kilometers before firing.
And this is completely unnecessary.