To improve traffic safety, millimeter wave radars have been widely used for sensing traffic environment. As radars also operate on a narrow small road and in the same frequency band, mutual interference between different automotive radars that arises cannot be easily reduced by frequency or polarization diversity. This letter presents novel orthogonal noise waveforms to reduce such neighboring interferences. First, the spectral density distribution function of the proposed waveforms is defined by using an optimized Kaiser function. Subsequently, the phases of the noise waveforms are formulated as a problem of phase retrieval and are explored. Thanks to nonuniqueness solutions, the proposed method generates the orthogonal signals with a good random phase diversity. The proposed method was tested on a representative scenario for interference reduction. The experimental results show that the proposed method can produce visually convincing radar images, and the signal-to-interference and noise ratio is better than the existing methods.

With the widespread use of millimeter-wave radar in advanced driver assistance systems (ADASs) and higher level autonomous driving, the probability of mutual interference increases. Radar echoes are distorted by the presence of mutual interferences, leading to the increase of the noise level and even producing ghost targets. Therefore, mitigating interference is one of the most important and pressing challenges for automotive radars today. This article presents an interference mitigation technique in the tunable $Q$ -factor wavelet transform (TQWT) domain. It is discovered that the target’s signal exhibits sparsity in the high $Q$ -factor wavelet transform. Conversely, the interference signal has better representation in the low $Q$ -factor wavelet transform. Then, using morphological components analysis and $l_{1}$ -norm penalized least squares, one sparsity-based nonlinear signal separation model is further derived to reduce the interferences. Finally, the split augmented Lagrangian shrinkage algorithm is applied to optimize the proposed model. Experiments demonstrate that the performance of the proposed algorithm is comparable to three state-of-the-art methods.

The automobile industry has a strategic intent to develop vehicles capable of higher and higher levels of autonomy. Radar is a key sensing component, being the only viable option for all weather, 24-hour-a-day use. Autonomy will require the radar to provide much greater levels of detailed information than is available from today's cruise control and collision avoidance systems. In particular, much wider bandwidths are needed for high range resolution. Furthermore, high Doppler resolution, requiring long integration times, is needed for moving object classification. These two opposing requirements create significant design and signal processing challenges. This paper examines the way in which choices in the key design parameters effect performance, showing that as both range and Doppler resolutions are enhanced, the potential for received energy to migrate across resolution cells increases and must be corrected to enable measurement of both focused high-resolution imagery and micro-Doppler signatures, as well as restoring system sensitivity. An advanced range and Doppler cell migration correction (RDCMC) algorithm is introduced and performance evaluated using both theory and experimentation.

With the increasing number of radar-equipped vehicles in dense traffic situations, millimeter wave radars are suffering from serious interference problems. Therefore, this article presents a novel bi-level $l_1$ optimization based approach for reducing interferences between automotive radars for range, velocity and angle measurement. Firstly, sparse difference analysis between the interfering signal and the target signal is investigated. According to the analysis results, one bi-level based signal optimization model is further derived by using $l_1$ -norm penalized least squares. This bi-level optimization enables a trade-off between suppressing interference and preserving radar targets. Specifically, in the first $l_1$ level, the interfering signal is first optimized as the “desired signal”, while the target is considered as “noise”. Meanwhile, sparse optimization is applied on the target signals in the frequency domain at the second $l_1$ level. Finally, the iterated soft-thresholding algorithm is used to optimize the proposed model. In real road interference suppression experiments, the proposed method improves the signal to interference plus noise ratio (SINR) for the target from 5.06 dB to 19.26 dB in the range-Doppler domain and from 6.86 dB to 22.10 dB in the azimuth spatial domain.

Current automotive radar technology is almost exclusively implemented using frequency modulated continuous wave (FMCW) radar in the millimeter wave bands. Unfortunately, incoherent interference is becoming a serious problem due to the increasing number of automotive radars in dense traffic situations. To address this issue, this article presents a sparsity-based technique for mitigating the incoherent interference between FMCW radars. First, a low-pass filter-based technique is developed to detect the envelope of the interference. Next, the labeled regions where interference is present are considered as missing data. In this way, the problem of mitigating interference is further formulated as the restoration of the echo using L1 norm-regularized least squares. Finally, the alternating direction method of the multipliers-based technique is applied to restore the radar echoes. Extensive experimental results demonstrate the effective performance of the proposed approach. Compared to state-of-the-art interference mitigation methods, the proposed method remarkably improves the quality of radar targets.