DCS: F-16C Viper by Eagle Dynamics

Bye
Phant

Air-to-Air Radar Improvement

In our previous White Paper regarding Phase 1 of improving the F-16C and F/A-18C radars, we discussed advances in how we calculate detection range based on Pulse Repetition Frequency (PRF), average transmitted power, receiver noise figure, antenna area, and Signal to Noise Ratio (SNR). You can find this White Paper here:

Eagle_Dynamics_Radar_White_Paper_v1 (digitalcombatsimulator.com)

For Phase 2 of radar model update, we will be accounting for the following:

Fluctuation of Target RCS. Real targets have complex shapes, and their linear sizes are often larger than radar wavelength. This means that radar returns from different parts of the airframe may add or cancel each other depending on their relative phase causing the RCS to fluctuate. In our approach, RCS is approximately constant during dwell, but randomly changes from dwell to dwell according to exponential distribution (this approach is known as the Swerling Case I model). This results in non-constant detection range and target detection probability.

Noise Variability. Detection probability will also depend on the noise level, its variability, and the number of Coherent Processing Intervals (CPIs) per dwell. Because the noise level continuously changes, the target may or may not be detected in a particular CPI. For example: There are three CPIs per dwell in HPRF RWS mode, and for successful ranging, the target should be detected in all three CPIs. Obviously, the probability of detection in all three CPIs is lower than the probability of detection in one of three CPIs or in three of eight CPIs (like in MPRF mode). In HPRF Velocity Search mode, Post-Detection Integration (PDI) replaces Frequency Modulation Ranging (FMR). In that mode, signals from three CPIs are summed to make noise fluctuations smaller and thus minimise the probability of false alarms. This allows lower threshold sensitivity and increased detection range without increasing false alarm probability.

Mode-Specific Range and Doppler Resolution. Closely spaced targets may not be resolved individually, and they may be displayed as a single target. Return energy off such targets may fall into a single doppler range bin and result in detection at longer ranges. Velocity resolution depends on CPI duration. So, in HPRF with three CPIs per dwell resolution is better than that in MPRF mode with eight CPIs per dwell (dwell duration is constant, so CPIs are shorter). In RAID mode, up to four CPIs may be merged into one, thus increasing velocity resolution four times. RWS HPRF mode uses linear frequency modulation for ranging, and it has poor range resolution (in order of 2 nm, which improves four times in RAID mode). In MPRF mode, range resolution is defined by range bin size and it is always equal to 150 meters.

Atmospheric Propagation Loss. The atmosphere absorbs radio waves proportional to its density. So, at higher altitudes, detection range is greater than at low altitude.

In summary, the Phase 2 changes provide a more realistic simulation of radar detection probabilities that will have more variable detection ranges, low-quality/spurious detections, more accurate RCS effects, and modelling of radar modes.

In Phase 3 we will focus on false targets, look-down performance, and improved modelling of Single Target Track (STT) mode.
 

Bye
Phant

Bye
Phant

F-16C Viper Development Report

F-16C INS+GPS System Overview

The navigation system on the DCS: F-16C Viper is a complicated mixture of technical solutions that are intended to supply the avionics with coordinates, velocity, and angles, that are characterised by precision, availability, integrity and autonomy. This is achieved by the cooperative work of the Inertial Navigation System (INS) and Global Positioning System (GPS) whose navigation inputs are processed through a Kalman filter in the Modular Mission Computer (MMC). Let’s discuss each of the components in detail.

INS

The Inertial Navigation System is an autonomous device that performs dead reckoning of aircraft coordinates by measuring the accelerations and then integrating them twice whilst taking into account the aircraft’s orientation in space. The latter is obtained from the F-16 ring-laser gyros. This type of INS is termed “strapdown” as there are no rotating parts. Basically, INS consists of three accelerometers, each for one orthogonal axis, and three gyros.

The main features of INS improvements are:

• Autonomy, as it doesn’t require any external signals to do dead reckoning.
• Stability in a short period of time (5-10 minutes).
• Noticeable error accumulation over longer periods of time based on the physics of dead reckoning. Together with the integration of accelerations (to update speed) and integration of position (to update coordinates), the small errors at the level of accelerations that are introduced by accelerometer noises and imperfect alignment are integrated twice as well.

Furthermore, the larger those errors are, the faster they accumulate due to the so-called integral correction of INS, which updates the local Earth gravitational force vector with the coordinates and adds them into the relative angles of the G vector.

Another distinctive feature of INS is the Schuler Oscillation with a period of 84.4 minutes. Due to the integral correction algorithm mentioned above, the INS behaves like a pendulum. In ideal circumstances, it stays in equilibrium while the aircraft moves along the Earth. When coordinate errors appear, it displaces the pendulum from the resting point and it starts oscillating. The larger the errors are, the larger the amplitude of the introduced oscillations. That’s why one may notice that INS errors get smaller at a rate of 84.4 minutes once airborne.

GPS

Global positioning system measures the aircraft position by measuring the signal propagation delay from GPS satellites to the receiver. Satellite orbits are precisely known, the exact positions of the satellites are computed according to an almanack that is transmitted in the same GPS radio signals. That’s why GPS needs a couple of minutes after the cold to start obtaining the almanack. The moments of the signal transmission are also known and are defined by a very precise atomic clock on board the satellite. Thus, in an ideal case, if the GPS signals are propagated through space with the constant speed of light, as they do in a vacuum, the receiver could precisely determine its position by intersecting the surfaces of equidistant radio signal delays from the satellites. You may think of it as spheres with centres located at the satellite’s positions, although it’s a bit more complicated in real life. However, there are two significant factors that prevent us from obtaining the ideal point of the surface intersections; the ionospheric delay and multipath. Both add unknown time to the actual signal propagation time. Multipath happens when the receiver is placed relatively near the ground and the signal may be reflected from ground objects that results in the signal's edges degrading; this is similar to an echo in the mountains where it’s too hard to tell one word from another. When such delays are unexpectedly added by the receiver, the precise navigation solution gets lost and the output

coordinate gets noisy. That’s where military GPS signals help to get a better signal resolution by the use of so-called P-codes, and the usage of dual frequency helps to eliminate the unknown ionospheric delay.

Integrated solution. Kalman filtering

To summarise the above: we have two navigation systems, both of which have flaws: INS accumulates errors over time, GPS is noisy and prone to interference due to natural factors like multipath and ionospheric delay and to enemy jamming and spoofing. Here is the good news! There is a way to avoid these flaws with the Kalman filter. It takes GPS and INS coordinates together with speeds as its input. The Kalman filter is a great algorithm that is able to get the maximum precision even out of measurements far from ideal, and it takes the best aspects from both systems: the stability and autonomy of INS and the precision of GPS to obtain an integrated navigation solution that is both stable and precise.

Furthermore, the Kalman filter knows, in terms of mathematical equations, the dynamic properties of the aircraft that is moving through space. If the aircraft is moving, it predicts where the aircraft will be on the next filter step. That’s why it is called recursive and the filter won’t let erroneous GPS signals decrease the precision of the output navigation solution. Moreover, it is able to dynamically change its measurements vs. prediction weights to adjust to a degraded navigation precision of any input.

Bye
Phant