The Enhanced Kalman Disturbance Filter is a feature developed by SkyRadar. It is part of its training radar system, designed to detect and handle range deception data using the Kalman Filter and the most recent RGPO detection features. This block includes several sub-functions, each serving a specific purpose in the processing of incoming radar reflections to identify and manage potential range deceived targets.
In the following video we compare the previously introduced RGPO detection algorithms (MTI, Clutter-Map) with the MTD-based filter. Please note that the it is not a one to one comparison, as the algorithms are progressively equipped with more intelligence.
Students may in a later step configure their own composite filter with the "atomic" blocks and create a variety of other combinations. Before watching the video, have a short look at the feature list.
| RGPO-EDF | MTI-EDF | Clutter-Map-EDF | MTD-EDF | Kalman-EDF | |
| Automatic Threshold based on MaxValue | x | x | x | x | x |
| Associate Radar Reflections into one object | x | x | x | x | x |
| Store Object Information | x | x | x | x | x |
| Detect RGPO | x | x | x | x | x |
| Velocity Gating | x | x | x | ||
| Target Association | x | x | |||
| Prediction | x |
Now let us look at the video:
Read more about Range Gate Pull Off (RGPO).
Description: The Kalman Filter is a recursive mathematical algorithm utilized for estimating the state of a dynamic system from a series of noisy measurements. It was developed by Rudolf E. Kálmán to address problems related to control system design and estimation in the presence of uncertainty. The Kalman Filter operates by continuously updating and refining its estimate of the system state based on new measurements, incorporating both the system dynamics model and the measurement noise characteristics.
Function: The primary functionality of the Kalman Filter can be summarized as follows:
- State Estimation: The Kalman Filter estimates the current state of a dynamic system by combining information from previous state estimates and new measurements. It utilizes a recursive algorithm that predicts the system's state based on the system model and corrects this prediction using the latest measurements.
- Prediction: The Kalman Filter predicts the next state of the system using a dynamic model of the systems behaviour. This prediction is based on the system's previous state and known inputs.
- Correction: After predicting the next state, the Kalman Filter compares this prediction with the actual measurement. It then calculates the discrepancy between the prediction and the measurement, known as the measurement residual. The filter adjusts its state estimate based on this residual, updating its belief about the true state of the system.
- Optimization: The Kalman Filter optimally combines information from the system model and measurements to minimize the error in the state estimation. It achieves this by dynamically adjusting the weights given to the prediction and measurement terms based on their respective uncertainties.
Use: To estimate the position, velocity, and orientation of moving objects, and filter out static targets.
Description: The Associate Radar Reflections into Objects subfunction groups radar reflections from a single target to create an object with specific distance and angle information.
Function: It identifies and associates reflections originating from a single target by analysing their characteristics.
Use: To establish individual objects representing targets, facilitating further tracking and analysis.
Description: The Velocity Gating builds upon the core functionality by introducing the capability to create new objects or associate incoming radar echoes with existing ones based on their velocities.
Function: By comparing the velocities of radar echoes with a user-defined threshold velocity, this enhancement enables radar systems to dynamically adapt to various scenarios and target velocities. It facilitates the creation of new tracks or the association of echoes with existing tracks, enhancing target tracking and management capabilities.
Use: Incoming radar echoes undergo velocity comparison against the predefined threshold velocity. Echoes meeting the criteria are either assigned to existing tracks or used to initiate new tracks, based on user-defined rules and algorithms.
Description: This subfunction stores target information for the first three rotations of radar data.
Function: It keeps track of the information for the real target, followed by two additional sets of target information from range-deceived targets with shifted distances.
Use: To enable the comparison of target information across rotations and detect potential deception based on differences in data.
Description: The Detect If Deception Is Active subfunction monitors the gradient between target information from the first and second rotations, as well as the second and third rotations.
Function: It evaluates the differences in distance, velocity, and angle information between these target sets and checks if the gradient falls outside an expected range, indicating the presence of deception.
Use: To identify whether a deception attempt is in progress based on anomalous changes in target characteristics.
Description: This subfunction estimates and tracks the real position of a target by leveraging the gradient, distance, velocity, and angle information.
Function: It calculates the position update for the real target, differentiating it from the range-deceived targets. Use: To determine and continuously track the actual position of a target amidst deception attempts, enabling reliable tracking and threat assessment.
Description: The Target Association Algorithm is a crucial sub-feature employed in radar systems for tracking moving targets by associating current radar measurements with previous target information. This algorithm utilizes a combination of parameters such as distance, angle, velocity, elapsed time, and radar iteration to establish the most probable association between successive radar updates and existing target data. By comparing these parameters and calculating a score, the algorithm determines the optimal association, ensuring accurate and consistent target tracking.
Function: The primary function of the Target Association Algorithm is to match current radar measurements with stored target information based on various parameters:
- Distance: Measures the spatial separation between the radar measurement and the existing target position.
- Angle: Determines the angular deviation between the radar measurement and the target's direction of motion.
- Velocity: Compares the velocity of the radar measurement with the expected velocity based on previous target information.
- Elapsed Time: Calculates the time elapsed between successive radar updates, providing temporal context for target tracking.
- Elapsed Radar Iteration: Tracks the number of radar iterations since the last update, indicating the freshness of the target information.
Use: Using these parameters, the algorithm computes a score for each potential association, with a lower score indicating a stronger likelihood of a valid match. By iteratively evaluating and updating associations, the algorithm ensures robust and accurate target tracking across changing radar conditions and target dynamics.
Description: The Prediction sub feature is an integral component of radar signal processing algorithms, designed to address challenges associated with close-moving targets. In scenarios where reflections from multiple targets may be incorrectly associated or merged into a single target, the Prediction sub feature aids in accurately predicting the presence of multiple targets. By leveraging predictive modelling techniques, this sub feature enhances the radar's ability to differentiate and track distinct targets, improving overall target detection and tracking performance.
Function: The primary functionality of the Prediction sub feature can be outlined as follows:
- Predictive Modelling: The Prediction sub feature utilizes predictive modelling techniques to forecast the future positions and trajectories of targets based on their current state and known dynamics. This predictive model accounts for factors such as target velocity, acceleration, and manoeuvring behaviour.
- Gate Creation: Based on the predicted target trajectories, the Prediction sub feature establishes prediction gates around each target's expected position. These gates define regions within which future radar measurements are expected to fall if the predicted trajectories hold true.
- Target Update Creation: When a new radar measurement update is received, the Prediction sub feature evaluates whether it falls within the prediction gates of any existing targets. If a measurement update falls within the prediction gate of a target, it is used to partially confirm the presence of that target. If a measurement update falls within the prediction gates of multiple targets, indicating the potential presence of multiple targets in close proximity, the Prediction sub feature generates separate target updates for each predicted target. These updates are then further refined and confirmed through subsequent measurements.
Use: It assists in accurately tracking target movements, particularly in congested spaces where close proximity targets may pose challenges for target association and tracking.
The Enhanced Kalman Disturbance Filter represents a significant leap forward in radar data processing, surpassing its predecessors—the MTI Enhanced Disturbance Filter, the Clutter Map Enhanced Disturbance Filter, and the MTD Enhanced Disturbance Filter—in critical aspects. It plays a central role in identifying and mitigating attempts to deceive radar systems through range manipulation, while offering substantial enhancements over previous iterations.
Compared to the MTI Enhanced Disturbance Filter, the Enhanced Kalman Disturbance Filter integrates the crucial velocity gating feature. This addition allows the filter to effectively process a broader range of target velocities, mitigating the limitations imposed by previous velocity thresholds. By incorporating velocity gating, it significantly improves the detection of moving targets, thereby enhancing the overall performance of radar systems.
Furthermore, the Enhanced Kalman Disturbance Filter surpasses the Clutter Map Enhanced Disturbance Filter by incorporating the essential feature of target association. This integration empowers the filter to correlate current radar measurements with existing target information, facilitating more precise and reliable target tracking. The inclusion of target association further strengthens the filter's ability to discern genuine targets from clutter and background noise, enhancing its effectiveness in complex radar environments.
Moreover, the Enhanced Kalman Disturbance Filter outperforms the MTD Enhanced Disturbance Filter by incorporating the predictive analysis feature. This capability enables the filter to anticipate the future positions and trajectories of targets, enhancing situational awareness and threat detection capabilities. While excelling in velocity gating, target association, and predictive analysis, the Enhanced Kalman Disturbance Filter sets a new standard for jammed radar data processing.
In conclusion, the Enhanced Kalman Disturbance Filter represents a substantial advancement in radar data processing, offering superior performance compared to its predecessors. By incorporating velocity gating, target association, and predictive analysis capabilities, the Enhanced Kalman Disturbance Filter elevates target detection and tracking to new heights, setting the stage for further advancements of jamming detection in radar technology.
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Such defense is not only useful in a military context but also in a civil aviation setting. Increasingly speed radar jammers by trucks and cars disturb airport infrastructure. Also hybrid warfare is used to perturb critical infrastructure like airports and civil air surveillance and navigation services.
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