This article introduces an enhanced radar filter designed for training, with features for detecting range deception, incorporating moving target detection and target association for improved accuracy.

MTD Enhanced Disturbance Filter

The MTD Enhanced Disturbance Filter is a new feature developed from SkyRadar which is part of a radar system, designed to detect and handle range deception data using the MTD and RGPO features. This block includes several subfunctions, each serving a specific purpose in the processing of incoming radar reflections to identify and manage potential range-deceived targets.


Video Comparison on MTI-, Clutter Map- and MTD-based filter

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. 

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).


1. MTD

Description: Moving Target Detection (MTD) algorithm is a computational method utilized in radar systems to identify and track objects that are in motion relative to the radar's reference frame. It operates on the principle of Doppler shift, analysing changes in the frequency of radar echoes to discern moving targets from stationary clutter and background noise. MTD algorithms play a critical role in enhancing radar performance, particularly in scenarios where accurate target detection and tracking are paramount.

Function: The primary function of an MTD algorithm is to differentiate moving targets from stationary clutter within radar returns. This involves several key steps:

  • Signal Processing: Incoming radar signals undergo signal processing, including Doppler analysis, to extract information about the velocity and motion of targets.
  • Thresholding: Doppler-shifted signals exceeding a predetermined threshold are classified as potential moving targets.
  • Clutter Rejection: Signals below the threshold, indicative of stationary clutter, are filtered out to reduce false alarms and improve detection accuracy.
  • Target Tracking: Detected moving targets are tracked over time to estimate their trajectories, velocities, and other relevant parameters.
    Use: To classify radar reflections as potential moving targets based on their intensity compared to the stationary environment, which represents clutter in this case.

2. Associate Radar Reflections into Objects

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.

3. Velocity Gating

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.

4. Store Information for the First Three Rotations

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.

5. Detect If Deception Is Active

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.

6. Estimate and Track the Real Position

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.

7. Target Association

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.


The MTD Enhanced Disturbance Filter feature stands as a pivotal advancement in radar data processing, surpassing both the MTI Enhanced Disturbance Filter and the Clutter Map Enhanced Disturbance Filter in key aspects. It plays a central role in identifying and countering attempts to deceive radar systems through range manipulation, while offering notable enhancements over its predecessors.

Compared to the MTI Enhanced Disturbance Filter, the MTD Enhanced Disturbance Filter feature introduces the crucial addition of the velocity gating capability. This enhancement allows the filter to effectively process a broader range of target velocities, mitigating the limitations imposed by the previous velocity threshold. By incorporating velocity gating, the MTD filter significantly improves the detection of moving targets, thereby enhancing the overall performance of radar systems.

Furthermore, the MTD Enhanced Disturbance Filter surpasses the Clutter Map Enhanced Disturbance Filter by incorporating the essential feature of target association. This capability enables the filter to associate current radar measurements with existing target information, facilitating more accurate 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.

However, despite these advancements, the MTD Enhanced Disturbance Filter still lacks the predictive analysis feature, which is a key component of the Enhanced Kalman Filter. Predictive analysis allows radar systems to forecast the future positions and trajectories of targets, thereby improving situational awareness and threat detection capabilities. While the MTD filter excels in velocity gating and target association, the predictive analysis feature remains exclusive to the Enhanced Kalman Filter.

In conclusion, the MTD Enhanced Disturbance Filter represents a significant advancement in radar data processing, offering superior performance compared to its predecessors. By incorporating velocity gating and target association capabilities, the MTD filter enhances target detection and tracking while providing a foundation for further improvements in predictive analysis with the Enhanced Kalman Filter.

Many Applications for Electronic Warfare

Follow our blogs and videos on Electronic Warfare with SkyRadar's Disturbance Filtering & Analysis solutions, the jammers and the Pulse Radar! SkyRadar is the only provider world-wide, providing manufacturer-agnostic ECM and ECCM training with simulators and real radars and jammers. Learn more about the simulator, range deception, angle deception, speed deception, radar lock on and major state of the art defense algorithms against malicious attacks.

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.

Please note that all delivery is subject to the EU export regulations. Also this blog publication and video do not share classified information.

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