Ground moving target classification by using DCT coefficients extracted from micro-Doppler radar signatures and artificial neuron network

Operational assessment and adaptive selection of micro‐Doppler features

“…Over the past few decades a plethora of features have been proposed for use in micro‐Doppler classification. These features may be divided into four basic types: (i) physical features [7–9], which aim at deriving quantities relating to the physical characteristics of targets and their motion; (ii) transform‐based features [10–12], which utilise the coefficients of transforms, such as the discrete cosine transform (DCT), as features; (iv) component analysis features [13–15], where the basis computed from algorithms such as principle component analysis (PCA) are defined as features; and (iv) speech features [16–18], which have typically been designed for and used to process speech signals, but have yielded good results in micro‐Doppler classification as well. Combined, hundreds of features may potentially be extracted from micro‐Doppler signatures.…”

Robust PCA micro-doppler classification using SVM on embedded systems

“…For the analysis, 50 randomly selected sequences of training and testing data from the different classes have been considered, being evaluated for each of the 4 duration lengths (4, 2, 1, 0.5 s) and 5 choices of components (10,20,30,40,50).…”

Micro‐Doppler‐based in‐home aided and unaided walking recognition with multiple radar and sonar systems

“…Moreover, scale invariance enables mitigation of inter‐class variability: namely, physical differences between targets of the same class (e.g. two people walking, a tall and a short one that would introduce different micro‐Doppler shift that might otherwise have led to an incorrect classification). Speech‐inspired features [19–22], which are features initially developed for speech processing, but which have been found to be useful in classifying micro‐Doppler such as linear predictive coefficients (LPCs), cepstrum coefficients, and, especially, mel‐frequency cepstrum coefficients. Transform‐based features [23–25], which are typically chosen as the coefficients of a transform such as the Fourier transform, wavelet transform, and discrete cosine transform (DCT). The efficacy of a given feature set is dependent on many parameters including transmitter frequency, range, and Doppler resolution, the aspect angle of target motion relative to the radar line‐of‐sight, and signal‐to‐noise ratio (SNR). A study of the effect of features on performance under varying operational conditions is given in [26] based on simulated micro‐Doppler signatures.…”