Adaptive Sidelobe Clutter Cancellation in Ground Radar
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Solution Overview
Problem
Conventional clutter cancellation techniques in ground-based radars, such as Doppler processing and non-adaptive spatial nulling, introduce signal loss and velocity blind spots, and fail to accurately match null shapes with clutter distributions due to unknown amplitude and phase errors.
Innovation Solution
A digital beamforming system that adaptively cancels sidelobe clutter by coherently integrating subarray signal samples, applying zero-hertz-centered Doppler filtering, and modifying beam steering weights to form nulls in the direction of clutter, reducing sidelobe gain and improving clutter-to-noise ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If Doppler processing is applied to cancel clutter, then clutter suppression is improved, but signal loss increases and velocity blind spots are created
Solution Approach 1:
The patent extracts and removes clutter components from the received signal using adaptive spatial nulling techniques. By identifying clutter sources in the spatial domain and applying针对性的 nulling weights, the system separates clutter from legitimate targets, avoiding the signal loss inherent in Doppler processing which removes entire velocity bands.
Solution Approach 2:
The patent applies local quality by creating spatially selective nulls only in directions where clutter is present, rather than applying uniform Doppler filtering across all velocity ranges. The adaptive weighting scheme adjusts null depth and position locally based on clutter characteristics, preserving target signals in unaffected velocity ranges.
2Object-affected harmful factors
If non-adaptive spatial nulling is used, then clutter cancellation is achieved, but null depth is limited due to amplitude and phase errors
Solution Approach 1:
The patent implements feedback by using actual received signal measurements to adaptively adjust nulling weights. The system continuously monitors clutter characteristics and modifies weighting coefficients to compensate for amplitude and phase errors in the antenna channels, achieving deeper and more accurate nulls compared to fixed non-adaptive schemes.
Solution Approach 2:
The patent transitions from static non-adaptive nulling to dynamic adaptive nulling. The nulling weights are continuously updated based on current clutter conditions and channel characteristics, allowing the system to maintain optimal null depth despite variations in amplitude and phase errors across different operating conditions.
3Object-affected harmful factors
If non-adaptive nulling is applied, then clutter suppression is achieved, but mismatch occurs between null shape and clutter angle distribution
Solution Approach 1:
The patent uses feedback from measured clutter angle distributions to adaptively shape nulls. By monitoring the actual angular distribution of clutter returns and adjusting nulling weights accordingly, the system achieves precise matching between null geometry and clutter spatial characteristics, eliminating the mismatch problems of non-adaptive approaches.
Solution Approach 2:
The patent employs dynamic adaptation of null shape and orientation based on real-time clutter observations. The nulling pattern automatically adjusts its geometry to match the clutter's angular distribution, providing versatile clutter suppression across varying clutter scenarios rather than relying on fixed predetermined patterns.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The adaptive approach significantly reduces sidelobe clutter-to-noise ratio below thermal noise levels, deepens null depths, and aligns nulls with actual clutter distributions, enhancing target detection and reducing the burden on radar subsystems.
Implementation Method 1
a plurality of antenna subarrays receive electromagnetic energy phase fronts
Implementation Method 2
A coherent integration processor coherently integrates the complex subarray I/Q samples to enhance a signal-to-noise ratio
Implementation Method 3
A zero-hertz-centered Doppler filter sums the pulse compressor samples for each range pulse-to-pulse
Data Source
AI summary
A system and method of providing to a beamformer a modified complex beam steering vector includes collecting subarray I/Q samples from a plurality of subarrays receiving clutter, performing coherent integration of the subarray I/Q samples to increase the CNR, adaptively modifying a complex beam steering vector to form a null in the direction of the received clutter, and outputting to a beamformer the modified complex beam steering vector. The beamformer receives complex I/Q data samples representing a radar signal containing near-horizon clutter and applies the modified beam steering vector to generate a beamformed signal having an elevated mainlobe and a spatial sidelobe null in the direction of the received clutter.