In this paper, we proposed a method for radar modulation identification based on the measurement of inequality in the frequency domain. Gini's coefficient was used to exploit the inequality in the powers of spectral components. The maximum likelihood classifier was used to classify the detected radar signal into four types of modulations: unmodulated signal (UM), linear frequency modulation (LFM), non-linear frequency modulation (NLFM), and frequency shift keying (FSK). The simulation results demonstrated that the proposed method achieves an overall identification accuracy of 98.61% at a signal-to-noise ratio (SNR) of -6dB without a priori information such as carrier frequency, pulse arrival times or pulse width.

In this paper, we propose an automotive radar sensor compensation method improving direction of arrival (DOA) and preventing target split tracking. Amplitude and phase mismatching and mutual coupling between radar sensor arrays cause an inaccuracy problem in DOA estimation. By quantifying amplitude and phase distortion levels for each angle, we compensate the sensor distortion. Applying the proposed method to Bartlett, Capon and multiple signal classification (MUSIC) algorithms, we experimentally demonstrate the performance improvement using both experimental data from the chamber and real data obtained in actual road.

We present a new family of algorithms that solve the bias problem in the equation-error based adaptive infinite impulse response (IIR) filtering. A novel constraint, called the constant-norm constraint, unifies the quadratic constraint and the monic one. By imposing the monic constraint on the mean square error (MSE) optimization, the merits of both constraints are inherited and the shortcomings are overcome. A new cost function based on the constant-norm constraint and Lagrange multiplier is defined. Minimizing the cost function gives birth to a new family of bias-free adaptive IIR filtering algorithms. For example, two efficient algorithms belonging to the family are proposed. The analysis of the stationary points is presented to show that the proposed methods can indeed produce bias-free parameter estimates in the presence of white noise. The simulation results demonstrate that the proposed methods indeed produce unbiased parameter estimation, while being simple both in computation and implementation.

In this letter we propose a simple adaptive algorithm which solves the unit-norm constrained optimization problem. Instead of conventional parameter norm based normalization, the proposed algorithm incorporates single parameter normalization which is computationally much simpler. The simulation results illustrate that the proposed algorithm performs as good as conventional ones while being computationally simpler.