Each of these systems has strengths and weaknesses. Integration combines their advantages to overcome individual limitations:

● GPS (Global Positioning System):

• Strengths: Provides highly accurate absolute position and precise timing globally.
• Weaknesses: Susceptible to signal blockage (e.g., in urban canyons, indoors, under foliage), jamming, and spoofing. Can drift if satellite signals are lost for extended periods.

● INS (Inertial Navigation System):

• Strengths: Provides continuous, self-contained position, velocity, and attitude information without external signals. Immune to jamming or signal blockage. High short-term accuracy.
• Weaknesses: Accuracy degrades over time due to accumulation of errors (drift). Requires initial alignment. Cannot provide absolute position without external aiding.

● Radar (e.g., Navigation Radar, Ground Penetrating Radar, Altimeters):

• Strengths: Provides relative position (range, bearing) to detected objects, velocity (Doppler), and can operate in GPS-denied environments. Active sensor, so it doesn't rely on external emissions (like GPS). Can be used for mapping or terrain following.
• Weaknesses: Does not provide absolute position or timing directly. Can be affected by clutter, weather, and stealth.

Integration Benefits:

1. Robustness in GPS-Denied Environments: In areas where GPS signals are unavailable (e.g., indoors, underground, under heavy jamming), radar can provide crucial local navigation updates. For instance, a vehicle's navigation radar can map its surroundings, and this map can be used to localize the vehicle relative to features on the map (Simultaneous Localization and Mapping - SLAM).
2. Drift Correction for INS: Radar measurements of ground velocity (e.g., using Doppler navigation radar) or altitude (using radar altimeters) can be fed into an INS filter (like a Kalman filter) to correct for its accumulated errors, significantly improving the INS's long-term accuracy and preventing drift.
3. Enhanced Situational Awareness: Radar provides object detection and ranging capabilities that complement GPS's positioning. A combined system can not only know its own position but also the positions and velocities of other vehicles, obstacles, or terrain features, leading to superior situational awareness.
4. Improved Accuracy and Integrity: By fusing data from multiple sensors, the overall PNT solution becomes more accurate and reliable. Redundancy ensures that if one sensor fails or is compromised, the others can maintain navigation. Advanced filtering techniques (e.g., Extended Kalman Filters, Particle Filters) are used to optimally combine the diverse sensor data.
5. Autonomous Navigation and Landing: For autonomous vehicles, drones, and aircraft, integrated radar-GPS-INS systems are essential. GPS provides global context, INS handles short-term dynamics, and radar provides real-time obstacle detection, altimetry, and ground speed measurements, critical for precise maneuvering and landing (e.g., radar-assisted precision approach and landing systems).
6. "Radar-on-Map" Navigation: By correlating radar-generated maps of the environment with pre-existing geo-referenced maps, a vehicle can precisely determine its absolute position even in GPS-denied or spoofed conditions.

Numerical Example:
An aircraft relies on INS for navigation, which drifts at a rate of 1 nautical mile per hour (NM/hr).

• After 3 hours of flight without GPS aiding, the INS position error could be 3 NM.

Now, imagine this aircraft integrates a Doppler navigation radar that measures ground speed with an accuracy of 0.1 m/s and a radar altimeter with an accuracy of 0.5 m.

• By fusing the Doppler radar's ground speed measurements with the INS velocity estimates using a Kalman filter, the INS velocity errors can be continuously corrected. This significantly reduces position drift. For example, the drift rate might be reduced to 0.05 NM/hr.
• After 3 hours with radar aiding, the INS position error would be 3 hours×0.05 NM/hr=0.15 NM, which is a drastic improvement (20 times better) compared to a standalone INS.