Maximum Likelihood Estimation Algorithm for Radar Target Motion Parameters
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Solution Overview
Problem
Conventional radar systems face challenges in accurately and efficiently estimating target range, radial velocity, and acceleration due to the complexity of the 3D likelihood function, leading to prohibitively large computations, limited robustness, and accuracy issues, especially in low signal-to-noise ratio conditions.
Innovation Solution
A fast maximum likelihood estimate (MLE) algorithm that coherently optimizes the correlation between the real phase of received signals and a model phase through keystone processing, coarse search, and fine search methods, reducing computational load and improving detection sensitivity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MLE technique is used to find the maximum of the 3D likelihood function, then the estimation accuracy of target motion parameters is improved, but the number of computations becomes prohibitively large
Solution Approach 1:
The patent segments the 3D likelihood function maximization problem into two separate 2D problems: first maximizing with respect to range parameter τ, then with respect to velocity parameter ν. This segmentation reduces the computational complexity from searching a 3D space to two sequential 2D searches, making the MLE technique computationally feasible while maintaining estimation accuracy.
Solution Approach 2:
The patent performs preliminary maximization of the likelihood function with respect to the range parameter τ first, obtaining an intermediate result that is then used in the second maximization step for velocity ν. This preliminary action simplifies the subsequent optimization problem and reduces the overall computational burden.
2Measurement precision
If the mainlobe area is small and local optima are abundant, then the resolution of target parameters is improved, but the difficulty of finding the global maximum increases
Solution Approach 1:
By segmenting the optimization into two steps (first τ, then ν), the patent reduces the search space at each step, making it easier to find the global maximum while maintaining the small mainlobe resolution benefits.
Solution Approach 2:
The preliminary maximization over τ provides a simplified intermediate result that narrows down the search space for the subsequent velocity optimization, reducing the difficulty of finding the global maximum in the presence of abundant local optima.
3Productivity
If conventional methods are used for target motion parameter estimation, then the computational load is reduced, but the robustness and accuracy in low SNR conditions deteriorate
Solution Approach 1:
The segmented MLE approach maintains accuracy in low SNR conditions by coherently processing the data through two optimization steps, while the mathematical formulation ensures robustness against noise. The segmentation allows for more careful optimization at each step without excessive computational burden.
Solution Approach 2:
The patent transforms the original 3D likelihood function into a form that separates range and velocity parameters, allowing independent optimization. This parameter transformation maintains the statistical optimality of MLE for robust low SNR performance while reducing computational complexity through the separated optimization steps.
Data Source
AI summary
A system and method for implementing a maximum likelihood estimator for making a joint estimation of range, range rate, and acceleration of a target utilizing a pulse doppler radar. The MLE of target motion parameters are determined by keystone processing a baseband signal, and generating a first estimate of the motion parameters based on the processed signal. The first estimate is utilized to set up sampling intervals for the performance of a coarse search. Then a fine search is performed using Newton's method to determine the MLE.