A dynamic adaptive method and system for direct current tracking correction

Abstract

This invention relates to the field of radio frequency signal processing technology, and discloses a dynamic adaptive method and system for DC tracking correction. The method includes: acquiring the I-channel and Q-channel baseband sampling sequences of a zero-IF radio frequency receiver; constructing channel DC observations; determining strong blocking states; generating DC update weights using a bounded weighted modulation mechanism; recursively estimating the DC bias of the I-channel and Q-channel; and performing DC correction on the baseband signal. Compared to existing methods that use simple high-pass filtering for DC suppression, especially in applications with strong blocking signals, this invention addresses the challenge of maintaining the integrity of the low-frequency components of the desired signal while suppressing DC bias. By introducing weighted modulation and a recursive DC tracking mechanism, this invention improves the DC correction stability of the zero-IF radio frequency receiver under complex interference environments.

Description

Technical Field

[0001] This invention relates to the field of radio frequency signal processing technology, and in particular to a dynamic adaptive method and system for DC tracking correction. Background Technology

[0002] Currently, zero-IF (zero intermediate frequency) radio frequency receivers are widely used in cellular communications, wireless local area networks, and IoT terminals due to their simple structure and ease of integration. However, in the actual operation of zero-IF receivers, factors such as local oscillator leakage, self-mixing effect, and analog front-end device mismatch often introduce significant DC bias components into the received baseband signal, which adversely affects subsequent demodulation processing.

[0003] To suppress the aforementioned DC bias, existing technologies typically employ fixed-parameter DC tracking algorithms or high-pass filtering-based DC suppression methods. For example, this involves estimating the mean of the baseband signal and recursively updating the DC compensation, or directly introducing a high-pass filter into the baseband processing link to filter out zero-frequency components. However, in the presence of strong blocking signals or large signals in adjacent channels, the DC observations of the baseband signal are easily affected by the blocking signals, leading to distorted DC estimation results. This can result in over-compensation, correction oscillations, or even the incorrect suppression of the desired signal. Furthermore, while high-pass filtering can suppress DC components, it inevitably weakens or even destroys the effective modulation information near zero frequency in the desired signal, making it difficult to meet the requirements of applications with high low-frequency signal integrity.

[0004] Therefore, there is an urgent need for a method that can still achieve stable DC bias tracking and effective correction under complex operating conditions such as strong jamming, so as to improve the anti-interference capability and overall reception performance of zero-IF radio frequency receivers. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the purpose of this invention is to propose a dynamic adaptive method for DC tracking correction. This method aims to solve the technical problem that existing methods for DC suppression using simple high-pass filtering are difficult to maintain the integrity of the low-frequency components of the desired signal while suppressing DC bias, especially in applications with strong blocking signals.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a dynamic adaptive method for DC tracking correction.

[0007] The dynamic adaptive method for DC tracking correction includes:

[0008] Step S10: Obtain the I-channel baseband sampling sequence and Q-channel baseband sampling sequence of the zero-IF RF receiver. Based on the I-channel and Q-channel baseband sampling sequences, perform the DC observation construction task using a periodic mean constraint mechanism, and output the I-channel DC observation. and Q-channel DC observations ;

[0009] Step S20: Based on I-channel DC observations and Q-channel DC observations A dual-evidence gating mechanism based on power threshold and DC consistency is used to perform the strong blocking state determination task, and the strong blocking determination flag B is output.

[0010] Step S30: Based on the strong blocking determination flag B, a bounded weight modulation mechanism based on blocking freeze, gain attenuation, and soft recovery limiting is used to perform the dynamic DC update weight generation task, and the DC update weight is output. ;

[0011] Step S40: Update weights based on DC I-channel DC observation and Q-channel DC observations The task of recursively calculating the DC bias of the execution channel is adopted, and the DC bias compensation of the output channel I is calculated. DC bias compensation for Q channel ;

[0012] Step S50: Based on the DC bias compensation amount of channel I DC bias compensation for Q channel A DC injection mechanism based on state consistency constraints is used to perform the baseband signal compensation task, and the I-channel compensated baseband signal is output. Q-channel compensation baseband signal .

[0013] Preferably, in step S10, the I-channel baseband sampling sequence and Q-channel baseband sampling sequence of the zero-IF radio frequency receiver are obtained. Based on the I-channel baseband sampling sequence and Q-channel baseband sampling sequence, a periodic mean constraint mechanism is used to perform the DC observation construction task, and the I-channel DC observation is output. and Q-channel DC observations The steps specifically include:

[0014] Step S101: Obtain the I-channel baseband sampling sequence of the zero-IF RF receiver and Q channel baseband sampling sequence ;in, This represents the number of sampling points in the k-th sampling period, used to limit the sample range used in constructing the DC observation; n represents the index of the discrete sampling points in the sampling period.

[0015] Step S102: Sampling sequence for channel I baseband and Q channel baseband sampling sequence Perform the mean calculation within the k-th sampling period to obtain the DC observations of channel I. and Q-channel DC observations , , Among them, the DC observation of channel I. Used to represent the equivalent DC component in the I-channel baseband signal introduced by local oscillator leakage and self-mixing effects within the current sampling period; Q-channel DC observation. Used to represent the equivalent DC component in the Q-channel baseband signal introduced by local oscillator leakage and self-mixing effect within the current sampling period.

[0016] Preferably, in step S20, the DC observation based on the I-channel is... and Q-channel DC observations The steps for executing the strong blocking state determination task and outputting the strong blocking determination flag B using a dual-evidence gating mechanism based on power threshold and DC consistency include:

[0017] Step S201: Based on I-channel DC observations and Q-channel DC observations Construct DC consistency evidence parameters to characterize the degree of synchronization between the DC components of the I-channel and Q-channel. , ,in, A preset positive number used to prevent the denominator from being zero;

[0018] Step S202: Based on I-channel DC observations and Q-channel DC observations Constructing DC composite intensity evidence parameters DC composite intensity evidence parameters Parameters for DC synthesis intensity evidence , ;

[0019] Step S203: Obtain the input power indication of the zero-IF RF receiver. Based on input power indication DC consistency evidence parameters and DC composite intensity evidence parameters Construct a strong blocking determination flag B.

[0020] Preferably, in step S203, the formula for the strong blocking determination flag B is expressed as follows:

[0021] ;

[0022] in, The pre-calibrated strong blocking power threshold is used to characterize the lower limit of the input power that may cause a significant increase in local oscillator leakage and self-mixing effect in a zero-IF RF receiver. The DC composite intensity threshold; This is the DC consistency threshold.

[0023] Preferably, in step S30, a bounded weight modulation mechanism based on blockage freezing, gain attenuation, and soft recovery limiting is used to perform the dynamic DC update weight generation task based on the strong blockage determination flag B, and the DC update weight is output. The steps specifically include:

[0024] Step S301: Construct a blocking freeze gating weight factor based on the strong blocking determination flag B using a binary complementary mapping method. Among them, the blocking freeze gate weight factor Used to freeze the DC update process in a strongly blocked state, and to allow the DC update process to continue in a non-blocking state;

[0025] Step S302: Obtain the automatic gain control status of the zero-IF RF receiver during the current sampling period. According to the automatic gain control state Combined with the preset gain attenuation modulation factor β and minimum gain reference value The gain attenuation weighting factor is constructed using a fractional mapping method based on a monotone bounded attenuation model. Among them, the gain attenuation weighting factor Used to reduce the DC update rate when the automatic gain control is in low gain mode, and to maintain a large DC update rate when the automatic gain control is in high gain mode.

[0026] Step S303: Based on blocking freeze gating weight factor and gain attenuation weighting factor Unlimited DC update weights are constructed using a product-weighted coupling method. , ,in, The baseline DC update coefficients are defined under non-blocking conditions; subsequently, update weights are applied to unlimited DC. A soft-recovery limiting method combining amplitude clipping and rate of change constraints is used to apply soft-recovery limiting constraints, resulting in the final DC update weights. .

[0027] Preferably, in step S40, the weights are updated based on DC. I-channel DC observation and Q-channel DC observations The task of recursively calculating the DC bias of the execution channel is adopted, and the DC bias compensation of the output channel I is calculated. DC bias compensation for Q channel The steps specifically include:

[0028] Step S401: For channel I, update the weights based on DC. and I-channel DC observations The DC bias estimation of channel I is performed using an exponentially weighted recursive estimation method, and the DC bias compensation of channel I is output. ;

[0029] Step S402: For the Q channel, update the weights based on DC. and Q-channel DC observations The Q-channel DC bias recursive estimation is performed using a combination of exponentially weighted recursive estimation and amplitude clipping, and the Q-channel DC bias compensation is output. .

[0030] Preferably, in step S50, the DC bias compensation amount of channel I is used. DC bias compensation for Q channel A DC injection mechanism based on state consistency constraints is used to perform the baseband signal compensation task, and the I-channel compensated baseband signal is output. Q-channel compensation baseband signal The steps specifically include:

[0031] Step S501: Adjust the DC bias compensation amount of channel I. DC bias compensation for Q channel The corresponding I-channel DC compensation node and Q-channel DC compensation node are injected into the digital baseband processing link of the zero intermediate frequency radio frequency receiver. The I-channel DC compensation node and Q-channel DC compensation node output the I-channel DC compensation status quantity and the Q-channel DC compensation status quantity, respectively.

[0032] Step S502: Acquire the I-channel baseband sampling signal and the Q-channel baseband sampling signal. Based on the I-channel DC compensation state quantity and the Q-channel DC compensation state quantity, perform DC cancellation operation on the I-channel baseband sampling signal and the Q-channel baseband sampling signal respectively, and output the preliminary I-channel compensated baseband signal and the preliminary Q-channel compensated baseband signal. The DC cancellation operation is implemented in the digital baseband domain in the form of subtraction to eliminate the DC bias component introduced by local oscillator leakage and self-mixing effect.

[0033] Step S503: Verify the state consistency of the preliminary I-channel compensated baseband signal and the preliminary Q-channel compensated baseband signal using a state consistency judgment method based on zero-mean hypothesis testing. After the state consistency verification is passed, output the final I-channel compensated baseband signal. Q-channel compensation baseband signal .

[0034] The present invention also provides a dynamic adaptive system for DC tracking correction, comprising:

[0035] The DC observation construction module is used to acquire the I-channel and Q-channel baseband sampling sequences of the zero-IF RF receiver. Based on the I-channel and Q-channel baseband sampling sequences, a periodic mean constraint mechanism is used to perform the DC observation construction task, and output the I-channel DC observations. and Q-channel DC observations ;

[0036] Strong blocking state determination module, used for I-channel DC observation. and Q-channel DC observations A dual-evidence gating mechanism based on power threshold and DC consistency is used to perform the strong blocking state determination task, and the strong blocking determination flag B is output.

[0037] The DC update weight modulation module is used to perform dynamic DC update weight generation based on a bounded weight modulation mechanism based on blockage freezing, gain attenuation, and soft recovery limiting, using a strong blocking determination flag B, and outputs the DC update weight. ;

[0038] The DC bias recursive estimation module is used to update weights based on DC bias. I-channel DC observation and Q-channel DC observations The task of recursively calculating the DC bias of the execution channel is adopted, and the DC bias compensation of the output channel I is calculated. DC bias compensation for Q channel ;

[0039] DC compensation injection module, used for DC bias compensation based on I-channel. DC bias compensation for Q channel A DC injection mechanism based on state consistency constraints is used to perform the baseband signal compensation task, and the I-channel compensated baseband signal is output. Q-channel compensation baseband signal .

[0040] The present invention also provides a dynamic adaptive device for DC tracking correction, comprising: a memory, a processor, and a dynamic adaptive program for DC tracking correction stored in the memory and executable on the processor, wherein the dynamic adaptive program for DC tracking correction implements a dynamic adaptive method for DC tracking correction when executed by the processor.

[0041] The present invention also provides a computer program product, including a dynamic adaptive program for DC tracking correction, wherein the dynamic adaptive program for DC tracking correction implements the dynamic adaptive method for DC tracking correction when executed by a processor.

[0042] The beneficial effects of this invention are as follows: By introducing a blocking-aware dynamic DC tracking correction mechanism, this invention adaptively modulates the DC update process in the presence of a strong blocking signal, enabling the DC bias estimation to converge smoothly under non-blocking conditions and remain stable under strong blocking conditions. This avoids DC misestimation and correction oscillation caused by blocking signal pull, thereby improving the stability of the DC correction process of the zero-IF radio frequency receiver.

[0043] This invention employs a correction method that combines recursive DC estimation with DC compensation injection to achieve DC suppression of the baseband signal without relying on high-pass filtering. This avoids the destruction of the effective modulation components near the zero frequency in the desired signal, thus balancing DC suppression and signal fidelity in complex interference environments and improving the overall receiving performance of the zero-IF RF receiver. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating the first embodiment of a dynamic adaptive method for DC tracking correction according to the present invention.

[0046] Figure 2 This is a schematic diagram of a device for a dynamic adaptive method for DC tracking correction according to the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Example 1: As Figure 1 The diagram shown is a flowchart of the first embodiment of the dynamic adaptive method for DC tracking correction of the present invention, which presents the first embodiment of the dynamic adaptive method for DC tracking correction of the present invention.

[0049] In the first embodiment, the dynamic adaptive method for DC tracking correction includes:

[0050] Step S10: Obtain the I-channel baseband sampling sequence and Q-channel baseband sampling sequence of the zero-IF RF receiver. Based on the I-channel and Q-channel baseband sampling sequences, perform the DC observation construction task using a periodic mean constraint mechanism, and output the I-channel DC observation. and Q-channel DC observations ;

[0051] It should be noted that the "period mean constraint mechanism" in this step refers to the following: during the digital baseband processing of the zero-IF RF receiver, a preset time length is used as the statistical period to perform mean statistical operations on the I-channel baseband sampling sequence and the Q-channel baseband sampling sequence respectively, thereby constructing a DC observation that reflects the current DC component state of the baseband signal. The period mean constraint is not a simple instantaneous average, but rather, by limiting the length of the statistical window, the DC observation results can effectively suppress the rapid fluctuations in the baseband signal caused by modulation information and random noise, while maintaining sensitivity to the slow changes in DC bias, including changes in DC components caused by local oscillator leakage, self-mixing effect, and analog front-end device mismatch.

[0052] Understandably, by introducing a periodic mean constraint mechanism into the baseband sampling sequence, the DC bias, a "low-frequency component that changes slowly over time," can be separated from the rapidly changing modulation signal. This allows the constructed DC observations to primarily reflect the current DC operating state, rather than being dominated by instantaneous modulation symbol changes or short-term noise disturbances. This provides a stable, repeatable, and physically meaningful observation basis for the subsequent DC tracking correction process, which is beneficial for improving the controllability and convergence of the entire DC correction link.

[0053] It should be understood that, compared to traditional techniques that directly perform high-pass filtering on the baseband signal or use fixed-parameter DC estimation methods, this step constructs DC observations through periodic mean constraints. This does not rely on the forced suppression of signal components near zero frequency, thus avoiding the destruction of low-frequency modulation information in the desired signal. At the same time, it will not introduce DC estimation deviations in different working scenarios due to improper setting of the fixed time constant, thereby ensuring both DC observability and signal integrity.

[0054] For example, in specific application scenarios, when a zero-IF RF receiver operates in an environment with low signal-to-noise ratio and a certain amplitude modulation component, directly using instantaneous sampled values ​​or short-window averaging as the basis for DC estimation can easily lead to significant fluctuations in the DC observations due to the modulation symbol fluctuations. However, by performing mean statistics on the I-channel and Q-channel baseband sampling sequences within a preset period, even if the modulation signal amplitude changes, the obtained DC observations still mainly reflect the actual DC bias level, making the subsequent DC tracking correction process less susceptible to interference from modulation characteristics, thereby maintaining the stability and reliability of DC observation results in complex signal environments.

[0055] Step S20: Based on I-channel DC observations and Q-channel DC observations A dual-evidence gating mechanism based on power threshold and DC consistency is used to perform the strong blocking state determination task, and the strong blocking determination flag B is output.

[0056] It should be noted that the "dual-evidence gating mechanism based on power threshold and DC consistency" in this step refers to: on the basis of constructing DC observations, simultaneously introducing the statistical consistency of baseband signal power characteristics and DC observations as the judgment criteria, and identifying whether there is a strong blocking signal in the current receiving state by jointly judging the two types of evidence; wherein, the power threshold is used to reflect whether the baseband signal energy deviates significantly from the normal operating range, and DC consistency is used to characterize the consistency of the DC observations of the I channel and Q channel in terms of amplitude and change trend. The dual-evidence gating mechanism outputs a strong blocking judgment flag by limiting the simultaneous satisfaction of the two types of criteria or the satisfaction of the criteria according to a preset logical combination.

[0057] Understandably, in zero-IF RF receivers, the presence of strong blocking signals often manifests not only as an abnormal increase in the overall power of the baseband signal, but also significantly influences DC observation results through local oscillator leakage and self-mixing effects. This causes the DC observations of the I and Q channels to exhibit abnormally consistent or abnormally divergent characteristics in terms of amplitude or trend. By simultaneously examining the power threshold and DC consistency, the impact of strong blocking can be characterized more comprehensively, thereby avoiding misjudgments caused by relying solely on a single power index or a single DC characteristic.

[0058] For example, when a large-amplitude expected signal or a short-term power surge occurs in the received signal but does not result in sustained blocking, although the baseband signal power may briefly exceed the preset threshold, the DC observations of the I and Q channels remain within the normal range of variation, and the DC consistency criterion will not be triggered. In this case, the dual-evidence gating mechanism will not output a strong blocking judgment flag. However, when a strong blocking signal caused by strong interference in adjacent channels or local oscillator leakage occurs, the baseband power remains high, and the DC observations of the I and Q channels show obvious abnormal and consistent change characteristics. The dual-evidence gating mechanism will combine the two types of evidence to output a strong blocking judgment flag B, thereby providing a reliable basis for the adaptive modulation of the subsequent DC update weights.

[0059] Step S30: Based on the strong blocking determination flag B, a bounded weight modulation mechanism based on blocking freeze, gain attenuation, and soft recovery limiting is used to perform the dynamic DC update weight generation task, and the DC update weight is output. ;

[0060] It should be noted that the "bounded weight modulation mechanism based on blocking freeze, gain attenuation and soft recovery limiting" in this step refers to: using the strong blocking judgment flag B output in step S20 as the control input, the range and rate of change of the DC update weight are constrained and modulated; when the strong blocking state is determined, the effective amplitude of the DC update weight is limited by blocking freeze or gain attenuation to suppress the rapid pulling of DC estimation under abnormal conditions; when the strong blocking state is lifted, the DC update weight is gradually restored to the normal update level within the preset boundary by soft recovery limiting, thereby ensuring that the DC update process is always in a controlled bounded state.

[0061] Understandably, in zero-IF RF receivers, the actual change in DC bias is usually slow, while DC observation anomalies caused by strong blocking signals are often sudden and large in amplitude. If a fixed update weight is still used for DC recursion under strong blocking conditions, the DC compensation amount is easily pulled off by the blocking signal, and even a long recovery oscillation occurs after the blocking disappears. By introducing a bounded weight modulation mechanism, the "step strength" of the DC update can be adaptively adjusted according to whether strong blocking exists, making the DC estimation process more consistent with the physical characteristics of the actual change in DC bias.

[0062] For example, when a strong blocking flag B is detected, indicating a current strong blocking state, the DC update weight is limited to a small range or even frozen, ensuring that subsequent DC recursive estimation relies primarily on historical stable estimation results and is not significantly affected by current abnormal DC observations. When the strong blocking state is resolved, the DC update weight does not immediately jump to its normal value, but gradually increases under the constraint of a soft recovery limiting mechanism, allowing the DC compensation to smoothly return to its accurate level. This avoids DC correction oscillations caused by a sudden increase in weight at the moment the blocking disappears. For example, zero-IF DC correction almost universally uses a first-order recursive mean estimation model: for I / Q baseband sampling sequences: ;

[0063] DC estimators are typically constructed as follows:

[0064]

[0065]

[0066] Based on the obtained DC estimate, the baseband signal is compensated using a first-order IIR low-pass filter with DC compensation as follows:

[0067]

[0068]

[0069] Where n is the sampling time index of the digital baseband sampling sequence; The baseband sample value of channel I obtained at the nth sampling time has the physical meaning of the instantaneous amplitude of the in-phase component after down-conversion, filtering and analog-to-digital conversion; The baseband sample value of the Q channel obtained at the nth sampling time is physically represented as the instantaneous amplitude of the quadrature component after down-conversion, filtering, and analog-to-digital conversion. This is the DC bias of channel I estimated at the nth sampling time. The DC update coefficient, with a value range of 0 < α < 1, is used to characterize the weight of the current observation sample in the DC recursive estimation. Its reciprocal is proportional to the DC estimation time constant. This is the DC bias of channel I estimated at the (n-1)th sampling time. This is the estimated DC bias of the Q channel at the nth sampling time. This is the DC bias of the Q channel estimated at the (n-1)th sampling time. This represents the I-channel baseband signal after DC compensation; This represents the Q-channel baseband signal after DC compensation;

[0070] Step S40: Update weights based on DC I-channel DC observation and Q-channel DC observations The task of recursively calculating the DC bias of the execution channel is adopted, and the DC bias compensation of the output channel I is calculated. DC bias compensation for Q channel ;

[0071] It should be noted that the "channel DC bias recursive task" in this step refers to: under the premise that the DC update weight has been obtained, for both the I channel and the Q channel, the DC observations in the current sampling period are weighted and fused with the DC bias estimation results of the previous period, and the DC bias compensation amount of the corresponding channel is updated in a recursive manner; the recursive process is constrained by the DC update weight, which is used to control the degree of influence of new observation information on the DC bias estimation results, so that the DC compensation amount evolves gradually over time.

[0072] It is understandable that by introducing a recursive mechanism in the DC bias estimation process, the DC compensation amount can have time continuity and historical memory characteristics, making it more consistent with the physical law of the slow change of DC bias in a zero-IF radio frequency receiver. At the same time, by combining the DC update weight to modulate the recursive process, the DC compensation amount can have good tracking ability when the signal environment is stable and necessary suppression ability when the signal environment changes abruptly, thus taking into account the DC tracking accuracy under different operating conditions.

[0073] For example, when a zero-IF RF receiver operates under conditions without strong congestion, the recursive DC bias estimation method described in this step allows the DC compensation amounts of the I and Q channels to gradually converge to a stable value over multiple consecutive sampling periods, with fluctuations remaining within a small range. When a strong congestion signal is suddenly introduced and persists for a period of time, if the traditional fixed-step recursive method is used, the DC compensation amount will be significantly biased in a short period of time, and it will take a long time to recover to a stable state after the congestion disappears. However, when using the method of this invention, since the DC update weight is dynamically reduced, the DC compensation amount basically maintains its original level during the congestion period, and only a few sampling periods are needed to recover to an accurate state after the congestion is lifted. Thus, in experimental comparisons, it shows a smaller DC estimation offset and a faster recovery speed.

[0074] Step S50: Based on the DC bias compensation amount of channel I DC bias compensation for Q channel A DC injection mechanism based on state consistency constraints is used to perform the baseband signal compensation task, and the I-channel compensated baseband signal is output. Q-channel compensation baseband signal .

[0075] It should be noted that the "DC injection mechanism based on state consistency constraints" in this step refers to: after obtaining the DC bias compensation amount of the I channel and the DC bias compensation amount of the Q channel, injecting the compensation amount as a DC compensation state into the digital baseband processing path of the zero intermediate frequency radio frequency receiver, and performing DC cancellation processing on the baseband signal of the corresponding channel; wherein, the state consistency constraint is used to ensure the consistency of the DC compensation injection process with the preceding DC observation construction, strong blocking determination and DC bias recursive estimation process in terms of time evolution and state update, including the injection timing, scope of action and update continuity of the compensation amount, so that the DC compensation behavior always matches the current state.

[0076] Understandably, by introducing state consistency constraints during the baseband signal compensation stage, it is possible to prevent the DC compensation amount from becoming disconnected from the previous estimated state during the injection process, thereby preventing baseband signal distortion caused by sudden changes in the compensation amount or injection timing mismatch. Under this mechanism, the DC compensation result of the baseband signal can truly reflect the latest state of the DC bias recursive estimation, ensuring that the compensated I-channel and Q-channel baseband signals remain continuous and smooth in time, which is beneficial for the stable operation of subsequent demodulation and signal processing modules.

[0077] It should be understood that, compared to the method of directly performing a one-time DC subtraction compensation on the baseband signal, this step incorporates the DC compensation process into a state constraint framework consistent with DC observation, weighted modulation, and recursive estimation. This makes DC compensation no longer an isolated end operation, but an organic component of the entire DC tracking correction closed loop. This effectively avoids baseband signal jumps, low-frequency distortion, or compensation oscillations caused by discontinuous compensation injection when strong blocking occurs or disappears, further improving the signal stability of the zero-IF RF receiver in complex interference environments.

[0078] For example, when operating under normal reception conditions and gradually introducing strong blocking signals, if a traditional DC compensation method is used, the baseband signal is prone to significant DC jumps or low-frequency disturbances at the moment of compensation injection, affecting subsequent demodulation performance. However, when using the DC injection mechanism based on state consistency constraints in this step, the compensated baseband signals of the I and Q channels maintain continuous change characteristics throughout the entire process of the occurrence, duration, and disappearance of the blockage. The DC component changes smoothly, and no obvious abrupt changes are observed. This indicates that the mechanism can significantly reduce the transient interference introduced by compensation while ensuring the DC suppression effect, thereby improving the overall reception performance under dynamic interference conditions.

[0079] Example 2: Furthermore, the present invention provides a dynamic adaptive system for DC tracking correction, employing a dynamic adaptive method for DC tracking correction as described in the above embodiments, which can solve the technical problem of dynamic adaptation in DC tracking correction. The beneficial effects of the dynamic adaptive system for DC tracking correction provided by the present invention are the same as those of the dynamic adaptive method for DC tracking correction provided in the above embodiments, and other technical features of the dynamic adaptive system for DC tracking correction are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0080] Example 3: This invention provides a dynamic adaptive device for DC tracking correction. Please refer to... Figure 2A dynamic adaptive device for DC tracking correction includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform a dynamic adaptive method for DC tracking correction as described in Embodiment 1 above. The dynamic adaptive device for DC tracking correction in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. This dynamic adaptive device for DC tracking correction is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this invention. A dynamic adaptive device for DC tracking correction may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. Random access memory 1004 also stores various programs and data required for the operation of a DC tracking correction dynamic adaptive device. Processing device 1001, read-only memory 1002, and random access memory 1004 are interconnected via bus 1005. I / O interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows a DC tracking correction dynamic adaptive device to communicate wirelessly or wiredly with other devices to exchange data. Although a DC tracking correction dynamic adaptive device with various systems is shown in the figures, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0081] Example 4: The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the dynamic adaptive method for DC tracking correction as described above. The computer program product provided by the present invention can solve the technical problem of dynamic adaptive DC tracking correction. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of the dynamic adaptive method for DC tracking correction provided in the above embodiments, and will not be repeated here.

[0082] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0083] It should be understood that the various parts disclosed in this invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0084] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A dynamic adaptive method for DC tracking correction, characterized in that, The methods include: Step S10: Obtain the I-channel baseband sampling sequence and Q-channel baseband sampling sequence of the zero-IF RF receiver. Based on the I-channel and Q-channel baseband sampling sequences, perform the DC observation construction task using a periodic mean constraint mechanism, and output the I-channel DC observation. and Q-channel DC observations ; Step S20: Based on I-channel DC observations and Q-channel DC observations A dual-evidence gating mechanism based on power threshold and DC consistency is used to perform the strong blocking state determination task, and the strong blocking determination flag B is output. Step S30: Based on the strong blocking determination flag B, a bounded weight modulation mechanism based on blocking freeze, gain attenuation, and soft recovery limiting is used to perform the dynamic DC update weight generation task, and the DC update weight is output. ; Step S40: Update weights based on DC I-channel DC observation and Q-channel DC observations The task of recursively calculating the DC bias of the execution channel is adopted, and the DC bias compensation of the output channel I is calculated. DC bias compensation for Q channel ; Step S50: Based on the DC bias compensation amount of channel I DC bias compensation for Q channel A DC injection mechanism based on state consistency constraints is used to perform the baseband signal compensation task, and the I-channel compensated baseband signal is output. Q-channel compensation baseband signal .

2. The dynamic adaptive method for DC tracking correction as described in claim 1, characterized in that, In step S10, the I-channel baseband sampling sequence and Q-channel baseband sampling sequence of the zero-IF radio frequency receiver are obtained. Based on the I-channel and Q-channel baseband sampling sequences, a periodic mean constraint mechanism is used to perform the DC observation construction task, and the I-channel DC observation is output. and Q-channel DC observations The steps specifically include: Step S101: Obtain the I-channel baseband sampling sequence of the zero-IF RF receiver and Q channel baseband sampling sequence ;in, This represents the number of sampling points in the k-th sampling period, used to limit the sample range used in constructing the DC observation; n represents the index of the discrete sampling points in the sampling period. Step S102: Sampling sequence for channel I baseband and Q channel baseband sampling sequence Perform the mean calculation within the k-th sampling period to obtain the DC observations of channel I. and Q-channel DC observations , , Among them, the DC observation of channel I. Used to represent the equivalent DC component in the I-channel baseband signal introduced by local oscillator leakage and self-mixing effects within the current sampling period; Q-channel DC observation. Used to represent the equivalent DC component in the Q-channel baseband signal introduced by local oscillator leakage and self-mixing effect within the current sampling period.

3. The dynamic adaptive method for DC tracking correction as described in claim 1, characterized in that, In step S20, based on the I-channel DC observation... and Q-channel DC observations The steps for executing the strong blocking state determination task and outputting the strong blocking determination flag B using a dual-evidence gating mechanism based on power threshold and DC consistency include: Step S201: Based on I-channel DC observations and Q-channel DC observations Construct DC consistency evidence parameters to characterize the degree of synchronization between the DC components of the I-channel and Q-channel. , ,in, A preset positive number used to prevent the denominator from being zero; Step S202: Based on I-channel DC observations and Q-channel DC observations Constructing DC composite intensity evidence parameters DC composite intensity evidence parameters Parameters for DC synthesis intensity evidence , ; Step S203: Obtain the input power indication of the zero-IF RF receiver. Based on input power indication DC consistency evidence parameters and DC composite intensity evidence parameters Construct a strong blocking determination flag B.

4. The dynamic adaptive method for DC tracking correction as described in claim 3, characterized in that, In step S203, the formula for the strong blocking determination flag B is expressed as follows: ； in, The pre-calibrated strong blocking power threshold is used to characterize the lower limit of the input power that may cause a significant increase in local oscillator leakage and self-mixing effect in a zero-IF RF receiver. The DC composite intensity threshold; This is the DC consistency threshold.

5. The dynamic adaptive method for DC tracking correction as described in claim 1, characterized in that, In step S30, based on the strong blocking determination flag B, a bounded weight modulation mechanism based on blocking freeze, gain attenuation, and soft recovery limiting is used to perform the dynamic DC update weight generation task, and the DC update weight is output. The steps specifically include: Step S301: Construct a blocking freeze gating weight factor based on the strong blocking determination flag B using a binary complementary mapping method. Among them, the blocking freeze gate weight factor Used to freeze the DC update process in a strongly blocked state, and to allow the DC update process to continue in a non-blocking state; Step S302: Obtain the automatic gain control status of the zero-IF RF receiver during the current sampling period. According to the automatic gain control state Combined with the preset gain attenuation modulation factor β and minimum gain reference value The gain attenuation weighting factor is constructed using a fractional mapping method based on a monotone bounded attenuation model. Among them, the gain attenuation weighting factor Used to reduce the DC update rate when the automatic gain control is in low gain mode, and to maintain a large DC update rate when the automatic gain control is in high gain mode. Step S303: Based on blocking freeze gating weight factor and gain attenuation weighting factor Unlimited DC update weights are constructed using a product-weighted coupling method. , ,in, The baseline DC update coefficients are defined under non-blocking conditions; subsequently, update weights are applied to unlimited DC. A soft-recovery limiting method combining amplitude clipping and rate of change constraints is used to apply soft-recovery limiting constraints, resulting in the final DC update weights. .

6. The dynamic adaptive method for DC tracking correction as described in claim 1, characterized in that, In step S40, the weights are updated based on DC. I-channel DC observation and Q-channel DC observations The task of recursively calculating the DC bias of the execution channel is adopted, and the DC bias compensation of the output channel I is calculated. DC bias compensation for Q channel The steps specifically include: Step S401: For channel I, update the weights based on DC. and I-channel DC observations The DC bias estimation of channel I is performed using an exponentially weighted recursive estimation method, and the DC bias compensation of channel I is output. ; Step S402: For the Q channel, update the weights based on DC. and Q-channel DC observations The Q-channel DC bias recursive estimation is performed using a combination of exponentially weighted recursive estimation and amplitude clipping, and the Q-channel DC bias compensation is output. .

7. The dynamic adaptive method for DC tracking correction as described in claim 1, characterized in that, In step S50, based on the DC bias compensation amount of channel I... DC bias compensation for Q channel A DC injection mechanism based on state consistency constraints is used to perform the baseband signal compensation task, and the I-channel compensated baseband signal is output. Q-channel compensation baseband signal The steps specifically include: Step S501: Adjust the DC bias compensation amount of channel I. DC bias compensation for Q channel The corresponding I-channel DC compensation node and Q-channel DC compensation node are injected into the digital baseband processing link of the zero intermediate frequency radio frequency receiver. The I-channel DC compensation node and Q-channel DC compensation node output the I-channel DC compensation status quantity and the Q-channel DC compensation status quantity, respectively. Step S502: Acquire the I-channel baseband sampling signal and the Q-channel baseband sampling signal. Based on the I-channel DC compensation state quantity and the Q-channel DC compensation state quantity, perform DC cancellation operation on the I-channel baseband sampling signal and the Q-channel baseband sampling signal respectively, and output the preliminary I-channel compensated baseband signal and the preliminary Q-channel compensated baseband signal. The DC cancellation operation is implemented in the digital baseband domain in the form of subtraction to eliminate the DC bias component introduced by local oscillator leakage and self-mixing effect. Step S503: Verify the state consistency of the preliminary I-channel compensated baseband signal and the preliminary Q-channel compensated baseband signal using a state consistency judgment method based on zero-mean hypothesis testing. After the state consistency verification is passed, output the final I-channel compensated baseband signal. Q-channel compensation baseband signal .

8. A dynamic adaptive system for DC tracking correction, applied to the dynamic adaptive method for DC tracking correction according to any one of claims 1 to 7, characterized in that, The dynamic adaptive system for DC tracking correction includes: The DC observation construction module is used to acquire the I-channel and Q-channel baseband sampling sequences of the zero-IF RF receiver. Based on the I-channel and Q-channel baseband sampling sequences, a periodic mean constraint mechanism is used to perform the DC observation construction task, and output the I-channel DC observations. and Q-channel DC observations ; Strong blocking state determination module, used for I-channel DC observation. and Q-channel DC observations A dual-evidence gating mechanism based on power threshold and DC consistency is used to perform the strong blocking state determination task, and the strong blocking determination flag B is output. The DC update weight modulation module is used to perform dynamic DC update weight generation based on a bounded weight modulation mechanism based on blockage freezing, gain attenuation, and soft recovery limiting, using a strong blocking determination flag B, and outputs the DC update weight. ; The DC bias recursive estimation module is used to update weights based on DC bias. I-channel DC observation and Q-channel DC observations The task of recursively calculating the DC bias of the execution channel is adopted, and the DC bias compensation of the output channel I is calculated. DC bias compensation for Q channel ; DC compensation injection module, used for DC bias compensation based on I-channel. DC bias compensation for Q channel A DC injection mechanism based on state consistency constraints is used to perform the baseband signal compensation task, and the I-channel compensated baseband signal is output. Q-channel compensation baseband signal .

9. A dynamic adaptive device for DC tracking correction, characterized in that, The dynamic adaptive device for DC tracking correction includes: a memory, a processor, and a dynamic adaptive program for DC tracking correction stored in the memory and executable on the processor. When the dynamic adaptive program for DC tracking correction is executed by the processor, it implements a dynamic adaptive method for DC tracking correction according to any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a dynamic adaptive program for DC tracking correction, which, when executed by a processor, implements a dynamic adaptive method for DC tracking correction according to any one of claims 1 to 7.

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