Radar Target Estimation Using Sequential Range-Doppler Processing
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Solution Overview
Problem
Current radar systems face limitations in efficiently estimating range and Doppler of multiple targets due to high computational complexity, masking of weaker targets by stronger signals, and the need for short pulse durations, which restricts detection range and accuracy.
Innovation Solution
A sequential sample-by-sample process that reduces computational complexity, allowing arbitrarily long transmit pulses, and estimates range-Doppler state representation using a two-dimensional matrix, independent of return signal samples, with a measurement matrix and gain matrix calculations to update state estimates and generate an estimated range-Doppler map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pulse duration is decreased to resolve closely separated targets, then range resolution is improved, but detection range deteriorates because maximum detectable range depends on transmitted energy which is proportional to pulse duration
Solution Approach 1:
The patent segments the long pulse into multiple sub-pulses with different frequencies (chirp rates). Each sub-pulse corresponds to a different frequency sweep, allowing the system to process different range-Doppler regions separately. This segmentation enables resolution of closely separated targets while maintaining long pulse duration for extended detection range.
Solution Approach 2:
The patent employs dynamic frequency modulation where the chirp rate varies across different sub-pulses. By dynamically changing the frequency sweep characteristics, the system can adapt to different target scenarios and maintain optimal resolution and detection range performance that would be impossible with a static short pulse.
2Measurement precision
If matched filter processing is used to compress long pulses for resolution, then range resolution is improved, but computational complexity increases due to matrix inversion operations required for multiple target estimation
Solution Approach 1:
The patent divides the matched filter processing into separate operations for each frequency-subpulse combination. Instead of performing a single complex matrix inversion for all targets, the system segments the processing into multiple simpler stages, each handling a specific frequency component, thereby reducing overall computational complexity.
Solution Approach 2:
The patent implements a dynamic estimation process where range-Doppler states are updated sequentially as new sub-pulse data becomes available. This dynamic approach allows the system to maintain accurate multi-target estimates without requiring inversion of increasingly large matrices, as the computational burden is distributed across multiple smaller, sequential operations.
3Object-affected harmful factors
If PRN waveform with uniformly high side lobes is used to obscure radar signal from hostile observers, then security is improved, but weaker targets are masked by stronger targets' side lobes reducing detection accuracy
Solution Approach 1:
The patent segments the PRN waveform into multiple frequency-subpulse components, each with its own autocorrelation characteristics. By processing these segments separately and combining results, the system can identify and suppress the contribution of strong targets' side lobes in specific frequency regions, thereby revealing weaker targets that would otherwise be masked while maintaining the security benefits of PRN waveforms.
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
This approach effectively reduces masking of weaker targets, improves detection accuracy, and increases throughput by efficiently estimating range and Doppler without matrix inversion operations, enabling detection of multiple targets with longer pulse durations and arbitrary waveforms.
Implementation Method 1
the radar receives a return signal that is a superposition of reflections from each target
Implementation Method 2
targets and/or the radar may be in motion, so that target range may be changing at a non-zero rate. This results in a Doppler shift, i.e., a difference between the frequencies of the transmitted signal and the received target reflection
Data Source
AI summary
System and method for estimating locations and range rates of a multiplicity of targets by estimating range-Doppler state representation, including: computing a prior range-Doppler state estimate matrix and a measurement matrix containing phase shifted and re-ordered samples of a transmitted signal waveform; applying the measurement matrix to the range-Doppler state estimate matrix to generate a prediction of a current return sample and calculating a difference between the current return signal sample and the prediction of the current return signal sample; multiplying the difference by a gain matrix to obtain an adjustment to the prior range-Doppler state estimate matrix; repeating the process for a next return signal; computing an estimated range-Doppler map (RDM); applying a threshold to the estimated RDM to detect individual targets and obtain their range and Doppler parameters; and using the range and Doppler parameters to estimate the locations and range rates of the targets.